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Review article, machine learning in structural design: an opinionated review.

• Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom

The prominence gained by Artificial Intelligence (AI) over all aspects of human activity today cannot be overstated. This technology is no newcomer to structural engineering, with logic-based AI systems used to carry out design explorations as early as the 1980s. Nevertheless, the advent of low-cost data collection and processing capabilities have granted new impetus and a degree of ubiquity to AI-based engineering solutions. This review paper ends by posing the question of how long will the human engineer be needed in structural design. However, the paper does not aim to answer this question, not least because all such predictions have a history of going wrong. Instead, the paper assumes throughout as valid the claim that the need for human engineers in conventional design practice has its days numbered. In order to build the case towards the final question, the paper starts with a general description of the currently available AI frameworks and their Machine Learning (ML) sub-classes. The paper then proceeds to review a selected number of studies on the application of AI in structural engineering design. A discussion of specific challenges and future needs is presented with emphasis on the much exalted roles of “engineering intuition” and “creativity”. Finally, the conclusion section of the paper compiles the findings and outlines the challenges and future research directions.

1 Introduction

We call structural design the process by which the number, distribution, shape and size of structural elements, and their connectivity is determined so that a given design objective is achieved while meeting a number of constraints of serviceability and resistance. The objective can be the minimization of material consumption but in practice, it is more likely to be related to cost minimization and to involve trade-offs between manufacturing, logistical and sometimes sustainability considerations. At the beginning of the structural design process, human engineers are usually provided with the overall geometry—through Building Information Models ( Jung and Joo, 2011 ), for example—and their task is to come up with specifications of the distribution of structural elements including their materials and sections. This process is carried out using a diverse collection of computational tools, from information modelling to structural analysis; sampling from catalogues involving hundreds of structural sections and with constant reference to thousands of pages of codes of practice. Consequently, as it stands today, structural design entails a significant and oftentimes tedious solution-searching process involving various complex and non-fully overlapping multi-dimensional domains, multiple constraints and large uncertainties, whereby arriving to a global optima would be a prohibitively time-consuming endeavour. Therefore, more often than not, the engineer’s search will be brief and they will settle for the first sub-optimal design that satisfies all the hard constraints. Unsurprisingly, a range of tools have been proposed to carry out the optimization of some of the better-posed problems involving a relatively low number of structural elements, e.g., ( Jewett and Carstensen, 2019 ; Amir and Shakour, 2018 ; Tsavdaridis et al., 2015 ); and more recently these tools have started to incorporate additional and more realistic complexities like dynamic actions ( Giraldo-Londoño and Paulino, 2021 ), manufacturing processes ( Zegard and Paulino, 2016 ; Carstensen, 2020 ), etc. However, the emphasis of this paper is not on the generation of targeted topology-optimized solutions for which excellent review articles can be found elsewhere, e.g., ( Thomas et al., 2021 ). Instead, this opinionated review concentrates on the exploration of large and complex integrated design spaces with the aid of artificial intelligence (AI) and, more specifically, the increasing role that Machine Learning (ML) algorithms are playing in this search.

Artificial Intelligence (AI) is the branch of science that is concerned with the re-creation of human cognitive functions by artificial means. Although this is most commonly attempted via digital computers, other media, notably biological systems ( Qian et al., 2011 ; Sarkar et al., 2021 ), have been and continue to be used with this purpose. This paper, however, focuses on the role of intelligent algorithms for digital computers; or more precisely, algorithms whose distinctive feature is their ability to learn. In this context, Machine Learning (ML) is a branch of AI whose central advantage is its potential to automatically detect patterns in data under uncertainty ( Murphy, 2012 ). This uncertainty arises inevitably from the limited size of the datasets employed but it also reflects errors in data collection (including measurement) as well as hard epistemic paucities.

One of the first approaches to replicate human cognition was to organize “knowledge” as a collection of mutually related facts. Once a database of facts was built, so the belief went, inference rules could be used to query it, revealing the interconnections and allowing questions, including those related to engineering design, to be answered. The use of this type of AI in structural design was discussed as early as 1978 by Fenves and Norabhoompipat (1978) and application examples appeared in the early 1980s. For example, Bennett et al. (1978) developed a program consisting of 170 production rules and 140 consultation parameters to assist the engineer in the application of Finite Element Analysis (FEA) to the design of building structures. Also, Maher and Fenves (1985) constructed an expert system for the preliminary design of high-rise framed buildings. They used weighing factors to compare different gravity and lateral resisting structural systems highlighting the “best” design according to the criterion of a linear evaluation function. Other researchers like Ishizuka et al. (1981) used rule-based systems to infer seismic damage on the basis of a database of earthquake accelerograms and visual inspection reports. However, it soon became apparent that hard rules can not replicate the human inferential process and that their contribution to design would be limited, not least because the world for which engineers design is brimming with uncertainty but also because exceptions to the rule are all too common. Logic-based AI was abandoned.

With the passage of time, probabilistic reasoning made its way into ML and message passing architectures, which model intelligence on the basis of human neural information passing ( Rumelhart et al., 1986 ), started to take the computational demands on storage and processing down to manageable levels. By the end of the 1980s, Bayesian Networks (BN) had become a practical scheme for ML ( Pearl, 1988 ). BN have proven useful in evaluating the reliability of structures and infrastructure systems with multiple components and multiple failure sequences ( Mahadevan et al., 2001 ). And Naive Bayes classifiers have been used to construct damage fragilities, e.g. ( Kiani et al., 2019 ), predict the strength of structural components, e.g. ( Mangalathu and Jeon, 2018 ), or estimate structural failure modes, e.g. ( Mangalathu et al., 2020 ).

Meanwhile, Artificial Neural Networks, or Neural Networks (NN) for short, started to be used in all branches of engineering design. One of the first studies to apply back-propagation NN—an approach initially devised by Rumelhart et al. (1986) —to structural engineering was conducted by Vanluchene and Sun (1990) . In their pioneering study, Vanluchene and Sun (1990) applied NN to the pattern recognition of a loaded beam, to the design of a simply supported reinforced concrete beam and to the structural analysis of a plate. NNs are abstractions of the functioning of the human brain that aim to replicate its ability to acquire knowledge through learning and storing in the form of interconnecting synaptic weights. In true fashion of the process originally hypothesised by Rumelhart et al. ( Figure 1 ) the network takes a set of features as inputs and applies complex feature fusion operations through a series of layers of neurons. The final layer outputs the end response either as a prediction or as a form of classification.

FIGURE 1 . ( Rumelhart, 1994 ) Message Center near the end of processing when the semantics of the imput have been well defined.

NN models (and their deep learning variants) have become extremely popular nowadays driven by the media coverage of their superb feature recognition capabilities and the notorious increase in computational power together with the wide accessibility of tools and libraries. Accordingly, NN have been used in seismic response prediction, e.g., Morfidis and Kostinakis (2017) ; Lagaros and Fragiadakis (2007) , system identification, e.g., Sivandi-Pour et al. (2020) , damage localization, e.g., Bani-Hani et al. (1999) ; Gharehbaghi et al. (2021) and in structural control, e.g., ( Khalatbarisoltani et al., 2019 ; Suresh et al., 2010 ), among other structural engineering tasks. The literature on NN (and indeed ML) applications to structural engineering is vast. Sun et al. (2021) provide a comprehensive review of ML methods used to predict and asses structural performance and to identify structural conditions. Some of these can be used in support of structural design but do not directly deal with structural design per se , defined in the form presented earlier in this paper. In fact, issues related to ML and structural design, as defined above, are not particularly well covered in the literature despite the proven potential brought about by leveraging AI technologies and ML algorithms to improve the exploration of design alternatives beyond current human cognitive levels.

It follows from the previous discussion that existing design optimization methods concentrate on individual structural subassemblies and do not serve to automate the design of entire structures. By contrast, this paper will explore the use of ML algorithms to automate structural designs stricto sensu . To this end, this paper proceeds to review a selected number of studies on the application of ML in structural engineering design. A discussion of specific challenges and future needs is presented with emphasis on the much exalted roles of ‘engineering intuition’ and ‘creativity’. Finally, the conclusion section of the paper compiles the findings and outlines the challenges and future research directions. But first, the paper will provide a general introduction to AI and ML methods.

2 Background on AI and ML

As mentioned above, central to AI and ML algorithms is the ability to learn, potentially achieving the super-human ability of recognising patters in high-dimensional datasets that have remained impenetrable to the human mind. Figure 2 compares the way traditional and AI software operate. In a traditional piece of software, the coder writes a “comprehensive” set of rules that the program must follow. Therefore, it is the sole responsibility of the programmer to consider all possible scenarios and to hard-code into the algorithm all the appropriate responses to these scenarios. It should be possible, in principle, to arrive to the precise output by following the path through the code given a specific input. By contrast, in AI algorithms the rules are created by the algorithm itself and the coder only provides the scaffold (or architecture) and feeds data into it. The AI algorithm will analyse the data and fill this scaffold with its own through training. Once those rules are established, they can then be used in the traditional way to predict other outputs given an input. The fact that the coder is exempt from considering and including all potential scenarios makes AI particularly useful when dealing with large datasets or complex processes.

FIGURE 2 . Traditional vs. AI algorithms.

The differences in construction and operation between traditional and AI software express themselves in a number of ways. Traditional code is naturally transparent and generally easy to predict while ML can be obscure and may produce unexpected results or include biases that are not always easy to detect. On the other hand, traditional algorithms will be limited to what the coder has predicted at first, while AI software is in principle easy to adapt without significant changes in the code. Traditional software demands the coder to capture carefully and accurately all the potential scenarios, while AI can handle complex problems more efficiently than humans, especially when they involve multiple dimensions or large datasets.

Broadly speaking, ML algorithms can be categorized in three main groups: supervised, unsupervised and reinforcement learning, depicted in Figure 3 . Supervised learning is probably the closest to human learning. A series of “examples” is used by the ML algorithm to build “knowledge” about a given task in a similar way to how humans build and use “past experience” ( Dietterich, 1996 ) like when small children are guided in their association of words to meanings. To this end, supervised models are given a set of features as input and labels as output. Then, the models attempt to find a set of rules to match a given set of features to the correct label guided by some measure of success. The process employs statistical methods for the learning operations and manual adjustments are usually not required. However, supervised ML relies on large amounts of correctly labelled input data, in quantities that can be significantly larger than those required by humans ( Kühl et al., 2020 ).

FIGURE 3 . Categories of ML algorithms.

On the other hand, unsupervised learning can be applied to different data types. In this approach, labels are not required, just features. The model is given those features and its algorithm then groups them according to some unknown property. In general, unsupervised models try to do one of three things: either cluster the data provided, find an anomaly in it, or reduce the number of dimensions in which to express the dataset. Grouping works by clustering data points that share some features without knowing what labels or indeed what categories are present. In anomaly detection or pattern recognition, a defining set of features is found and the model classifies the data point as either part of the set or as an anomaly. This is very helpful in failure identification or structural characterization. Reinforcement learning builds on these ideas and sometimes uses the algorithms developed for supervised and unsupervised learning. It is used in situations where it is difficult to get perfectly correct labels. In such cases, the algorithm is provided with an input and a reward function that gives an indication of how well or bad the algorithm is doing. The algorithm then learns how to maximise the reward.

In general, the creation of a typical AI algorithm involves four main stages. It starts with the data preparation. This is a crucial stage that can take longer than the others. It involves the acquisition of data, its analysis and pre-processing. The quality and quantity of data are determinant for a good output of the model. The second stage is the design of the model, which is followed by the third stage of training and evaluation. It is not uncommon that at the end of this process, the coder realises that changes are required in the data or the model architecture, and the design should be re-adjusted. Once the model is considered well designed and trained it is ready to enter its final stage of deployment.

3 AI and the Design of Spatial Structures

Although shells, vaults and other spatial structures are already among the most efficient structural forms and have a notoriously complex structural response, they have been fertile ground for many structural design optimization explorations. This may be because shells can be discretised as meshes with known support locations which, despite requiring hundreds of variables, are usually single-layered and lend themselves more easily to parametrization than the reticulated multi-storey frames with a multitude of potential element locations, sizes and connection types used in buildings. However, even if a highly parametrized design space is used, its sheer size still makes it trackless to the human mind. Therefore, the basic capability of machine learning to discover and rebuild complicated underlying connections between input and output variables from a relatively big dataset ( Liu et al., 2020 ) can be of great use while designing spatial structures.

Mirra and Pugnale (2021) examined AI-generated design spaces built using Variational Autoencoder (VAE) models, and compared their outputs with those coming from a human-generated explicit definition of design variables. Two relatively simple but realistic cases were explored by Mirra and Pugnale involving triangular and square footprints. A dataset of 800 depth maps obtained from 3D models were used to train the VAE. Three objectives were set for the optimization, including: 1) the maximisation of the structural performance, quantified in terms of deformations obtained from Finite Element Analysis (FEA), 2) the maximisation of the height of the shell openings, and 3) the minimisation of the difference between the final and target footprints. They found that the AI-generated outputs had a greater diversity and responded better to the performance criteria in comparison with the solutions obtained from human-defined generative designs. Besides, AI solutions included structural configurations that would not have been possible to find within the human-defined design space. This hints to one of the main advantages of using AI in design: the possibility of exploring design options beyond those traditionally developed by human intelligence ( Mueller, 2014 ).

The exploration of diverse design options brought about by AI was also exploited by Maqdah et al. (2021) and Palmeri et al. (2021) while studying the provision of structurally-efficient regolith-based arch forms for extraterrestrial construction. They built unsupervised machine learning models (Convolutional Autoencoders, CAE) capable of detecting patterns and differentiating between arch geometries and their stress and deformation contours ( Figure 4 ). These models were then used to search for optimal sectional geometries considering the effects of extreme thermal changes and seismic action under low-gravity conditions. Various datasets, each one with over 500 thermal and static FEA analysis and a 60–40% training-validation split were constructed for this purpose. Although the optimal configurations found resembled those obtained by more traditional approaches ( McLean et al., 2021 ), the possibility of including a diversity of design actions (gravity, thermal, and seismic) and a substantial number of dimensions that are then reduced to a smaller latent space where a holistic search process can be used was featured as a clear contribution of AI. Moreover, Maqdah et al. (2021) and Palmeri et al. (2021) were able to elucidate some of the dependencies of the latent space (reduced) dimensions on geometric and structural parameters which can be helpful in making informed (partially explainable) searches. Alongside the CAE, regression models were used to allow the visualisation of the changes in the arch shape and stress fields when moving towards a certain direction in the design space.

FIGURE 4 . Latent space representation of the data points coloured depending on the occupancy of the arches (a measure of the material usage) for the seismic design scenario. The latent space (A) is presented together with the plot of the mean occupancy of each cluster (B) and sample shapes from the best selected cluster (C) . Adapted from Palmeri et al. (2021) based on the CAE model of Maqdah et al. (2021) .

The works of Zheng et al. (2020) and Fuhrimann et al. (2018) have explored the use of ML in leveraging the fundamental relationship between force and form in shells. Zheng et al. (2020) trained a NN model to predict the relations between subdivision rules and structural and constructional performance metrics on the basis of graphic statics results. This surrogate use of ML models to enable a rapid exploration of design spaces constitutes one of many important attempts to improve the machine-human collaboration. Unfortunately, the parameters employed; notably for constructibility (i.e., number of faces with areas greater than a given threshold), may seem too simple proxies to capture the complexities of the manufacturing and construction challenges. On the other hand, Fuhrimann et al. (2018) also explored the potential of combining form-finding with ML in the form of Combinatorial Equilibrium Modelling and Self Organizing Maps. Central to these works is the need to grasp a complex space of solutions in order to both increase its diversity and to make it manageable to the designer.

The previously mentioned works have highlighted the basic capability of ML to discover and rebuild complicated underlying connections between input and output variables and to find relationships between structural shape and performance. Once those relationships are established, the corresponding optimization of the structural configuration is simplified ( Liu et al., 2020 ). However, to set an optimization process where the design parameters are chosen automatically by the machine (algorithm) without human intervention remains difficult. This is because these parameters must exist in a low-dimensional space that can be optimized while not sacrificing their representational capacity. An issue that was also observed while optimizing the design of materials ( Xue et al., 2020 ).

An alternative approach was followed by Danhaive and Mueller (2021) who tackled the design of a long span roof structure. For this purpose, they used variational auto encoders (VAE) to train low-dimensional (2D) models that are intuitive to explore by the human engineer. By conditioning the models on different performance indicators, the models can adapt their mappings. A new performance-driven sampling algorithm was proposed to generate databases that are biased towards design regions with high performing structures. The structural performance indicators employed in the case study are only mass dependent and are normalized so they are evenly distributed on the unit segment. A total of 36 design variables, mainly topological, were used in the design and dimensioning of the truss elements using the cross-section optimizer available in Karamba ( Preisinger and Heimrath, 2014 ). The salient feature of this approach is that it gives the human designer a greater control over performance trade-offs standing in the middle between optimization methods, on the one side, and undirected search algorithms, on the other.

The support provided by ML algorithms to the design of spatial structures are not conscripted to structural calculations but can include the quantification of traditionally less quantifiable metrics such as aesthetics. For example, Zheng (2019) developed a NN that could be used to quantitatively evaluate the personal taste of an architect. By using force diagrams of polyhedral geometries with unique and distinguishable forms and a clear data structure and asking the human architect to score the inputs, a NN was trained to learn their design preferences. The results, which may seem unsurprising at first sight, put in evidence the capability of ML to express what may be considered as inexplicit. In doing so, Zheng demonstrated not only that solutions with higher scores can be generated with a higher probability of satisfying any personal design taste, but what is more important, that ML can learn relationships that may be difficult to articulate in human parlance. It should be noted that, given the natural difficulties human designers face when asked to score many forms consistently to the same standard. In these cases, the scores were mapped into a grading scale, from A to D, which considers the number of times the forms have been selected. This explains the final selection presented in Figure 5 where a structure with an initial score of 0.729 is chosen on top of another with a score of 0.864. This is a compromised solution, but one that massively narrows down the variety of forms from which the designer has to choose. Thus, the door is open to integrate both mechanistic and quantifiable metrics with other kinds of design considerations and to apply this to a diversity of design tasks.

FIGURE 5 . Selection of form-found structures considering user taste by Zheng (2019) .

4 AI Applied to the Design of Building Structures

The rationalization of the design process of building structures, within a structural optimization framework, has usually been separated into three components ( Havelia, 2016 ): 1) topology, which involves decisions on the number and connectivity of members, usually done without optimizing the connection itself; 2) shape, which involves decisions related to the location of elements and the layout of joints; and 3) sizing, which involves defining member cross sections. More often than not, these components are treated separately in the scientific literature, however, they are strongly interrelated and decisions involving one will greatly affect the others. Usually, the layout space is reduced by architectural considerations, but it will still encompass a large number of potential locations that are difficult to explore without any pre-determining guiding principle. Besides, early estimates of the building cost are usually based on weight, however, the majority of the total cost can sometimes be attributed to fabrication and erection which are not always directly proportional to weight ( Kang and Miranda, 2005 ) In addition, material costs depend not only on tonnage, but also on the type and size of cross sections utilized and erection costs are also highly contingent on geography and local market conditions Klanšek and Kravanja (2006) . These facts will automatically render impractical most topology optimization studies carried out to date.

Some studies have incorporated, albeit in a simplified manner, the design complexities outlined above. For example, Torii et al. (2016) developed an optimization algorithm that penalizes the number of members and joints in the structure in proportion to the number of connected elements. Unfortunately, this was only applied to trusses and no consideration was given to the fact that the connection type is determinant in their cost. Hassett and Putkey (2002) collected a comprehensive list of cost drivers and their values for the most common moment-resisting and pinned connections in the AISC catalogue. And Zhu et al. (2014) considered constructibility issues in the optimization of frames and demonstrated that some structures with a less efficient load path can improve constructibility and lead to overall lower costs. Zolfagharian and Irizarry (2017) used Principal Component Analysis, a clustering ML technique, to group constructibility factors into six major categories. To this end, they assembled a dataset, via industry interviews, on 79 different constructibility factors with given scores. As the design space increases exponentially with the number of structural elements, the number of structural typologies analysed, their connectivity and the constructibility considerations, most currently available optimization methods are rendered impractical for full-scale real implementation. Other proposals, like that of Havelia (2016) have used methods based on topology and sizing optimization within a multi-disciplinary architecture suitable for 2D steel framed buildings. Again, Havelia’s study showed that a heavier structure can be more economical than its lighter counterpart when connection and fabrication costs are taken into account. One drawback of this study is that serviceability constraints like maximum deflection or vibrations are not considered and therefore its applicability to real designs is hampered. On the other hand, high profile applications of structural optimization like the Chicago 800 West Fulton Market or Shezhen’s Financial Center do not aim to optimize the whole building economy or constructibility but are concerned with only a small proportion of its load carrying elements.

One of the first studies that departs from the above mentioned trend is that of Ranalli (2021) who proposed a new AI-based optimization module for the design of a flooring system with varying degrees of composite action. User-defined variables employed include the depth of the slab, the height of the steel deck, the properties of concrete, a range of possible cambers, the option to use shoring during construction, the degree of composite action, and the range of wide flange sections. The optimization framework iterates through each beam and girder, automatically determines its static scheme, computes the governing moment and deflection demands under the applied loads, and efficiently iterates through the set of available design options to find the most economical and feasible solution. Serviceability limits are considered and material and labour rates are assigned to arrive to an optimal solution through a scenario exploration. However, the gravity resisting columns are not considered, nor are issues related to their continuity and the rotational restraint (or flexibility) they provide to the floor. Nevertheless, the main strengths of Ranalli’s AI-driven optimization framework are its computational scalability and its readiness of applicability to new steel frame designs with minimal pre-processing efforts.

Another interesting work was performed by Chang and Cheng (2020) who re-formulate building frames as graphs ( Figure 6 )and use Graph NN (or GNN) trained on simulation results that can learn to suggest optimal beam and column cross-sections. This is one of the first attempts to use GNN in the realm of design optimization aided by differentiable approximators. The optimization objective employed by Chang and Cheng (2020) is simplistic, involving only mass minimization, but a variety of constraints is considered together with serviceability limits to produce optimal designs. The results are reported to be consistent with typical engineering designs and also comparable to outputs from Genetic Algorithm optimizations. The main limitations of this work are related to the absence of slab continuity effects and the treatment of the building skeleton as an input. However, the possibility of implementing a graph representation and generation algorithm in the initial phases of design to provide an end-do-end solution generating tool is worth exploring further.

FIGURE 6 . An example building structure and its structural graph representation suitable for analysis by GNN, from Chang and Cheng (2020) .

Similarly, Ampanavos et al. (2021) developed a ML system for the automatic generation of building layouts aimed at helping architects present structurally feasible solutions during the early stages of the project. A peculiarity of the system is that it does not aim to estimate the full structure to start with, but uses an iterative approach where the neural network gradually extends the solution as necessary. In this way, the NN has better changes of identifying patterns on a small building area at each step. However, this approach is also prone to error accumulation for large structures, although this error is dependent on the size of the training dataset. Besides, the column positioning can be noisy. However, future combinations of this approach with element sizing tools and more sound structural considerations are likely to produce a scalable and helpful methodology.

In his thesis, Ranalli (2021) , mentioned above, also considered the problem of sizing lateral load resisting systems against strong loads typical of earthquakes. The author treated this problem in two iterative phases, the first of which searches for the most economical solution that meets strength, constructibility and ductility criteria. The second phase checks for lateral drift compliance and design load combinations. An energy based analysis is performed in case particular floors need to be adapted to comply with the drift limits. The strength of this study is that is able to combine commonly used analysis tools and relatively justified cost functions to provide a whole-encompassing approach to building design. It is also worth noting that a high variance of cost across different design scenarios was observed highlighting the important role of even small changes in the variables on the overall building cost.

The above mentioned studies are mainly devoted to steel framed solutions, where the domain is discrete since only a certain number of steel sections are available. This may simplify and reduce the design space and facilitate the consideration of constructibility functions. By contrast, designing concrete structures may introduce additional complications since a relatively broader design space is to be considered with added variations in member detailing. These issues were approached by Pizarro and Massone (2021) who aimed at supporting the design of reinforced concrete buildings by keeping track of previously accepted design solutions, in contrast with other topology optimization methods based on more heuristic approaches like those proposed by Zhang and Mueller (2017) , which do not have this feature.

Pizarro and Massone (2021) proposed a predictive model for the length and thickness of reinforced concrete building walls based on Deep NN trained with 165 Chilean residential projects. The walls were described in both geometrical and topological domains and three variations of the data, achieved by modifying the building plan angle and its scale, were considered. Highly accurate predictions of wall thickness and length were obtained and the authors recommend the method to provide the engineer with a preliminary but reliable wall plan. Although not holistic in its scope, this work stresses the potential of ML-based tools to enhance the engineer-architect interaction via the machine. Besides, although important in number, the database of 165 building designs employed puts in evidence the small-data nature of most structural engineering problems. In addition, the regressive model proposed by Pizarro and Massone (2021) does not incorporate contextual information and can lead to poor estimations of wall translation.

In a companion paper, Pizarro et al. (2021) improve upon their previous work and present Convolutional NN models that take the architectural data as input and can output the final engineering floor plan. To this end, two regressive models are used to predict the thickness, length, and translations of the wall. A second prediction of plan is obtained by using a model that generates a likely image of each wall. Both independently predicted plans are combined to lead to the final engineering design as shown in Figure 7 . This methodology was proven to be a feasible option to accelerate decisions regarding the building layout and can be adapted to incorporate estimations of building drift demands or force distributions.

FIGURE 7 . Predicted plan obtained by Pizarro et al. (2021) .

Along the same vein as the above-mentioned studies, the work of Liao et al. (2021) uses generative adversarial networks (GAN), that have been previously used to generate building floor plans ( Chaillou, 2020 ), to perform structural designs of shear wall residential buildings. To this end, the authors use a semantic process to extract essential architectural and structural features from technical drawings of around 250 pairs of architectural-structural human designs. The outputs of the GAN model are evaluated in two case studies where their safety and economy are compared against designs carried out by competent human engineers. It is concluded that GAN-generated designs can improve significantly the speed at which new designs are generated without compromising the quality of building structures. Similarly, Lou et al. (2021) optimized the shear wall layout of high-rise buildings through a tabu search algorithm. Support vector machines (SVM) were used to construct surrogate models and speed-up the analysis time. Their objective was to minimize the structural weight with constraints on the period ratio and story drift. Through a series of case studies, the authors showed that the proposed approach works well. In this case, however, a meta-heuristic algorithm was used for the optimization part and the ML model was employed only to reduce the computational cost due to repetitive structural analyses.

5 The Grails of Creativity and Intuition

Modelling human intelligence on the perceived way we process and understand information has lead to remarkable tools that can augment the engineers’ design skills, allowing them to operate over large datasets and make ever more accurate predictions of response and performance. However, understanding and reasoning are not the only, or even the most frequent, ways engineers use to solve problems ( Graziano and Leone, 2019 ). Intuition, understood as “a form of recognition” ( Simon, 1995 ), or the ability to understand something almost instinctively without concious reasoning, plays an important role in engineering decisions. In fact, engineers, who may prefer to call it judgement, use intuition even when developing computer models such as when framing the design question the model is set to answer or deciding what to include and what to leave out of that question. Appeals to recognize the importance of intuition in engineering design have grown almost in parallel with the proliferation of computational tools in engineering ( Young, 2018 ).

Recent pioneering research has started to look at ways to integrate intuition into AI and ML with encouraging results in areas as diverse as chemical engineering ( Duros et al., 2019 ), automated planning ( Kim et al., 2017 ), and mathematics ( Davies et al., 2021 ). In all these cases, the authors propose schemes for the incorporation of a human experimenter as part of the solution-generation process. For example, Davies et al. (2021) approach is akin to a “test bed for intuition” where ML algorithms guide the experimenter by: 1) verifying the existence of a hypothesized mathematical pattern using supervised ML; and 2) if the pattern exists, by helping in understanding it using Attribution Techniques. Likewise, Duros et al. (2019) propose the integration of human and machine in the selection of potential chemical experiments within a single decision-making loop. In all these cases, by making human and machine work together, a significantly higher performance is achieved than either of them could achieve individually.

In the structural engineering field, a relatively similar approach has been attempted by Danhaive and Mueller (2021) . In their work, briefly described in the previous section, Danhaive and Mueller allow the design engineer access to a family of 2D latent spaces that can be adapted by changing the user-defined performance condition. This feature encourages designers to investigate different trade-offs between performance and other design features and opens the door for a more integrated machine-designer collaboration that does not aim to replace intuition with deterministic and quantitative rules but instead to incorporate it within the design process. However, to make the latent space intuitive and apt for human exploration, Danhaive and Mueller have to limit it to two dimensions. This highlights a defining feature of human intuition: that it emerges from the natural inability of the human mind to process scenarios with multiple variables ( Halford et al., 2005 ). It is when faced with high uncertainties and multiple unknowns that the engineer resorts to intuition to be able to define a direction of exploration without getting boggled by the details. One would expect that the growing ability of AI to identify complex patterns in high-dimensional spaces will supersede the advantages of rules of thumb and educated guesses in determining high level features of the design process. Until then, the integration of human and machine intelligence offers a promising alternative. In addition, intuition’s deciding role during the initial design stages fades down as the design is gradually informed by mechanics and structural analyses. Nevertheless, intuition remains as one of the last strongholds of traditional structural engineering practice as it adapts and responds to the challenges of digitalization. The other being creativity.

Creativity is usually defined as the generation of novel and useful ideas ( Jung et al., 2013 ). This immediately invokes the existence of a judge, a person to whom the idea, or in our case the design, would appear novel or useful. It is perhaps this subjective strength of the term the reason for its recent prominence in the discussions around the training of the next generation of structural engineers ( Ibell, 2015 ) where it is usually pitted against the more quantifiable (and declining) numerical skills. However, this subjectivity is not amorphous or ethereal since creativity does not emerge in the vacuum but is rather tied to socially contextualized phenomena ( Kaufman and Sternberg, 2010 ). As such it will appear that creativity can be taught and learnt, if by humans also by machines. In this regard, the examples presented in previous sections have highlighted the possibility of incorporating measures of taste in ML tools and algorithms have been shown to enhance the diversity of the solutions found. In this context, it has been argued that novelty constitutes a critical issue to address with computational approaches, e.g., ( Amabile, 2020 ). This is due to the fact that training of ML models usually relies on minimizing a loss expectation function and therefore the model is encouraged to perform well in the most common elements of already established knowledge.

A number of approaches could be taken to improve the “creativity” of ML algorithms ( Boden, 1998 ), namely: 1) by producing novel designs from the combination of familiar solutions, 2) by discovering new paths in conceptual spaces, and 3) by disrupting the design space with solutions that were not previously considered. Consequently, it would seem that there are yet many routes to encourage artificial creativity. These aspects are in fact being developed within (and are probably more suited to) reinforcement learning approaches. Similarly, efforts to incorporate heuristic thinking into AI have been trialled in other branches of design ( Nanda and Koder, 2010 ) and it may be beneficial to explore those in structural engineering also. At the end of the day, heuristics (intuition) is already routinely used by engineers to reduce the search space of potentially feasible designs, e.g., ( Maqdah et al., 2021 ; Palmeri et al., 2021 ; Danhaive and Mueller, 2021 ). A perceived hurdle, however, comes form the fact that much of the progress of ML and AI has come from the formalization of mathematical and logical approaches aiming at well defined problems with clear goals. To answer this, may be the distinction between: 1) algorithms that search the entire decision space, and 2) those that perform bounded searches to provide satisfactory solutions ( Simon, 2019 ) can be helpful here. Ultimately, much to the regret of the new breed of curriculum transformation proposers, computer programs constitute a body of empirical phenomena to which the student of design can address himself and which he can seek to understand. There is no question, since these programs exist, of the design process hiding behind the cloak of “judgment” or “experience” ( Simon, 2019 ). To which we may add:“ or creativity”.

None of the above mentioned explorations to embed artificial intuition or to enhance artificial creativity in machine intelligence has yet been fully explored in structural engineering design. This constitutes an area of great research potential. Since much of the ML research has been based on mimicking the theories of human cognition it is entirely possible that the restrictions of human creativity and intuition are in turn limiting machine intelligence. This calls for a re-evaluation of the human-machine creative partnership. New investigations that take at face value the human-machine duo, like it has been done in other creative industries ( Nika and Bresson, 2021 ), are likely to benefit the realm of structural design with fresh and surprising views. So it seems that in the short term we may be seeing more design cooperation between human and machine where the role of ML, however, is not circumscribed to repetitive tasks but can assist in the creative work itself.

6 Conclusion

It has been suggested ( Gero, 1994 ) that there are three views that can be taken about artificial intelligence in design: 1) AI as a framework in which to explore ideas about design; 2) AI as provider of a schema to model human design; and 3) AI as a means to allow the development of tools for human designers. This review paper has concerned itself with a strong version of the third view, by highlighting the path not only towards the development and proliferation of ML tools but also towards the automation of entire parts of the design process. In fact, a multitude of ML tools have been proposed aimed at different individual tasks along the design chain (like predicting the strength or condition of a given element, or the optimization of a section or connection). Design, however, is more complex than any of these individual tasks and ML methods aimed at it are more scarce.

It has been shown that ML tools have now started to appear that allow engineers to access complex multi-dimensional spaces beyond the ability of human intelligence alone. It was argued that the defining characteristic of ML to identify complex patterns and use those to predict or propose new engineering design solutions will form the basis for the automatization of increasingly large portions of the design endeavour. Importantly, these ML-enabled explorations can include not only hard mechanistic constraints but also metrics of taste and intuition. Indeed, although currently still producing timid results, the learning capacity of ML algorithms can be used to incorporate aesthetic and creative criteria that is sometimes difficult to articulate but which nevertheless the machine can learn. In addition, this learning can feed not only from engineering precedents at large but from the “best” precedents we currently have.

Another advantage of ML algorithms applied to design is found in the increased diversity of outputs produced. ML algorithms have been shown to increase the design diversity by recombining the features that characterise individual designs producing solutions beyond those which would have been imagined by human engineers. This recombination is usually neglected in engineering designs due to the large demands of data and time associated with it. However, with the use of data augmentation tools and computer simulation, it is expected that this hurdle will be solved sooner rather than later.

Nonetheless, the data requirements of ML algorithms will continue to be a limiting factor, particularly in the structural engineering field. If the ML-enabled design automation is to be attained, larger datasets of real-world designs should be made freely available. Most of the ML algorithms reviewed herein have used training datasets in the order of the hundreds. This is “small data” science and requires specific data augmentation techniques that the focus on “big data” is currently concealing. Data acquisition and curation is indeed the single most important step in the development of ML models. Robust, complete and reliable data sources should be produced and shared. Echoing current public demands in the sustainability and industrial ecology quarters of the design enterprise (in terms of environmental impact, LCA, etc.) ( D’Amico et al., 2019 ) the field of structural ML design also needs all its stakeholders to contribute their design databases. Only then, truly optimal and “out of the box” ML-enabled design solutions can be realistically proposed paving the way towards more resilient, economical and sustainable new structures.

All in all, we should continue to guard against the well known dangers lurking around ML implementation. To this end, issues of interpretability and overfitting should continue to be raised and efforts made to increase model explainability (by conducting and reporting sensitivity tests and marginal effects studies for example), increase data sources, improve noise filtering processes and carefully select the ML models (to reduce overfitting) should carry on. Finally, it has been said that ML tremendous success so far has been achieved by showing that some cognitive processes thought to be complex and difficult are, in fact, not so. This, taken together with the acceptance that routine design is broadly defined as that activity that occurs when all the necessary knowledge is available ( Gero, 1994 ); should prepare us well to be less surprised when the next generation of ML tools hits the structural design enterprise with the automation of large portions of the design process. Hence the question of how long until, not if, the human engineer is superseded in structural design.

Author Contributions

CM-C contributed to conception and design of the study, it organisation, wrote the paper, read and revised it.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: artificial intelligence, machine learning, structural design, structural engineering, design space

Citation: Málaga-Chuquitaype C (2022) Machine Learning in Structural Design: An Opinionated Review. Front. Built Environ. 8:815717. doi: 10.3389/fbuil.2022.815717

Received: 15 November 2021; Accepted: 13 January 2022; Published: 09 February 2022.

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*Correspondence: Christian Málaga-Chuquitaype , [email protected]

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Experimental Structural Model: From Manual Paper Garment to Fabrication as an Architectural Practice-Based Approach for Fashion Design Education

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The study presents the integration of architectural design approaches in the fashion design process suggesting a new educational method based on structural model fabrication. The paper addresses the output of an experimental collaborative practice-based workshop titled ‘Fashion Clash’ that mixes both architects and fashion designers. The workshop focused on testing self-structural garments following a manual workflow which is divided into three main phases, (1) modeling and form-finding, (2) assembly, and (3) fabrication. Paper-based materials are used for transforming full-scale garments into textiles. The results presented seven garments displayed at a fashion show that show the effect of the folding techniques in reaching stability and highlighting the interdisciplinary integration of architects and fashion designers. The study concludes that implementing a parametric design logic based on architectural perspective in fashion would generate innovative ways of testing self-supporting geometry. Digitally computing the forces and structure before fabrication are left for further research.

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Introduction

The relationship between architecture and fashion continues to evolve surprisingly through serving the same function of sheltering and protecting the human body against nature. The human scale and their thermal comfort are considered the center of both disciplines, where fashion deals with the direct body as clothes, while architecture deals with the body inside the space (Ertas and Samlioglu 2015 ). Both fields are dealing with space, mass, and structure using geometry, which consequently shows the visible relationship of their design process (Hedayat 2012 ) in turning the two-dimensional surface into a three-dimensional form. In fashion design, the conventional process uses pattern-cutting to flat fabric cut, assemble, and sew generating the 3D garments (Valle-noronha et al. 2020 ). Although this process produces waste, yet, the folding technique can consider one of the most excellent educational experiences to reach zero-waste reduction (Rissanen and McQuillan 2016 ; McQuillan 2020 ) for both fields. This hands-on technique can be reached without using advanced tools (Jackson 2011 ), which is based on undergoing multiple trials and errors to achieve a self-supporting model.

Both fields are shaped and influenced by cultural, social, economical, and historical factors. One of the gaps that this research tackles is integrating architecture in fashion education as a learning tool from a structural and algorithmic point of view, not as a visualization form. New changes in fashion design should be seen in the teaching and the learning process through the knowledge of multidisciplinary approach integration (Erminia 2019 ). With the limited integration of merging other disciplines in fashion education, the role of educators should fall into the realm to prepare and equip young designers with the necessary knowledge and skills to be aware of the new challenges that might face traditional education methods (Faerm 2015 ; Murzyn-Kupisz and Hołuj 2021 ). This study focuses on non-traditional educational methods in fashion design which will be based on ‘learning by doing’ that is applied in architectural curricula and commonly discussed in the scholarly literature (Özkar 2007 ; Doyle and Senske 2017 ; Nicholas and Oak 2020 ). The learning-by-doing method gives the experience of model prototyping that uses educational prototypes at early design stages. Following the educational strategy done by Lee (Lee et al. 2018 ), ensures that participants will not rely on the visualization tool during the design process in both disciplines. This allows dealing with problem-solving through sensing the materials’ behavior during creating a space, unlike the garments industry which is designed based on visual appearance.

The paper aims at reaching a teaching design process to assist fashion designers in generating garments using architectural principles. As a way of exploring innovative ways of testing geometry with several folding processes in fashion, the paper presents an approach that merges architecture and fashion design. This paper explores how the traditional fashion design process can integrate with an architectural design approach. It discusses the design process and the clashes between both fields through a new educational model based on the practice of collaborative and innovative experimental workshop that develops a parametric design approach based on architectural design principles. The workshop combines the skills of both architects and fashion designers to design full-scale garments to transfer some practice of producing, making, and fabricating to fashion designers. The workshop aims at challenging the current technology-driven paradigm of architectural design that relies heavily on 3D modeling by passing the need for digital software. Instead of the traditional 2D drawings that the fashion design starts within the design process, in this workshop, they will start directly with the fabrication of a 3D unit out of paper based on architectural principles. The workflow of the workshop is aiming to make sure that all participants have equality in the resources they are using to follow the same method of fabrication to assess the same criteria of the final output, especially by giving them more hands-on experience in dealing with the folding techniques through a trial-and-error process. The paper concludes the gap between materials and their shaping processes using paper as a 3D form.

Literature Review

Architecture and fashion design intersections in the digital age.

Clothes and buildings are considered the protective envelope, analogous to skins, for our bodies against the external environment (Crewe 2010 ). Correspondingly, the design responded more to human needs (Paksoy and Yalçın 2005 ) and appeared in form, aesthetic, and structure represented in columns and walls in buildings, and necklines, sleeves, and trimming in fashion (Kim and Cho 2000 ). Similar factors affect the appearance and production process of both clothes and buildings as climate, culture, material, and technology (Hedayat 2012 ), where common vocabularies began to appear reflecting the changes in environment and society (Miles 2008 ). In our context, this relation can obviously be seen in old Islamic Egypt, where the Mashrabiya—a window element—made of shuttered lattice wood was created for the cultural and social aspects. This element has a privacy feature that allows the insider to clearly observe the street while preventing the outsider to see through. Similarly, for the same purpose, women in that era used to cover their faces with a piece of fabric called ‘Burqa’ which has the same essential design and function as Mashrabiya as stated by the architect Abd Rabboh (Yasser 2015 ).

Although the fashion industry is more about aesthetic expression (Hallnäs 2009 ), zero-waste concepts started to appear due to the markets’ need for economical problem-solving. This concept became a practice in relation to climate crisis solutions (McQuillan 2020 ) through waste reduction, which in parallel changes the sequences of traditional fashion production. The appearance of this concept exists in both fashion and architecture fields that can be seen in resource reduction, unlike conventional techniques (McQuillan 2020 ). As a result of waste reduction, there has been a rapid rise in the use of new digital fabrication tools which assisted both designers to optimize their designs before the fabrication and production process for precise rapid prototypes. Increasingly, the aid of new digital tools introduced new shapes and echoes in both disciplines which provided innovations in texture, form, and volume in non-conventional ways. However, the digital tool should be taken as an addition to and not a replacement of analogue tools.

In the past years, the computational age had witnessed a fascinating intersection in architecture and fashion design in the design concepts, design process, vocabularies, languages, theories, geometry, materials, and digital tools (Zunde and Bougdah 2006 ; Hedayat 2012 ; Quinn 2003 ; Valle-noronha et al. 2020 ). Coco Chanel, a fashion designer, believed in the similarities of both fields when she stated, ‘Fashion is architecture: It is a matter of proportion .’ Since then, buildings became more fluid and soft as clothes, while clothes became more rigid and kinetic as buildings (Karimi and Bavar 2018 ). This can be seen in Rebal Jber's work, a Syrian architect who challenged hard materials like wood and marble to seem soft and liquid as a neural network and fabric in his wall panel collections titled ‘Traces of nature’ which was presented at Beit Beirut exhibition in 2018 (Rebal 2018 ). His inspiration was based on using organic forms that match forms from nature.

The evolutionary growth of new technologies and materials shifted the fashion industry from couture and mass production to a multifaceted process. Although it is difficult to predict the deformable flexible fabrics in clothes (Tanaka et al. 2007 ), each material property can be controlled (Bugg and Ziesche 2013 ; Brändle 2004 ). The fabric application in architecture has been integrated since the use of tents, animal skins, and bones in construction. It has expanded with the discovery of smart and hybrid responsive materials that open the doors for body adaptation. Fabrics as a formwork give stability to free-form structures found in the work of Mark West, Miguel Fisac, Sergio Prego, Massimo Moretti, Richard Bush, among others. In modern textile-based constructions, fabrics gradually started to appear again not only as an aesthetic element but as a part of the manufacturing and structural element that gives the flexibility of non-conventional forms (Kuusisto 2009 ). The traditional workflow of the fashion industry follows several processes as stated by McQuillan starting with design, which includes ideation, concept, and sketching, followed by making, which includes testing different iterations and patterns, and ending with a production sample, factory sample, and the final production piece (McQuillan 2020 ). Although sketching is considered a main essential technique during pattern-cutting and maker-making of garments (McQuillan 2020 ), the repetition of the trial and error process to generate the desired designs consumes a lot of resources. Thus, the revolution in the digital age shifted the design process to integrate more digital software which became a tool of prototyping to assist in zero-waste practice for reducing the waste of resources. Correspondingly, digital tools go back and forth in the design process which allows more visualization moving between the 3D sketch, 2D pattern, and 3D sample (McQuillan 2020 ). Certainly, digital tools play an important role in reaching better results, yet, the workshop in this paper aimed to minimize the use of digital tools during the design process depending on a traditional tool for experimenting with folded units. Material and structural stability tests for folding techniques are hard to test digitally without experience in analysis software. Accordingly, this workshop focused on testing the units on the manikin dealing directly with the body to understand different dimensions and scales which can save time unlike if they were just designed digitally. This highlights the valuation of the hand-made exploration and the making process of craftsmanship more than depending on digital tools.

Folding as a Design Process in Architecture and Fashion Design

In educational hands-on experiences, the folding technique is one of the easiest tools to generate 3D volumes without using advanced tools (Jackson 2011 ). Peter Jackson argued in his book titled ‘Folding techniques for designers from sheet to form’ that the importance of the folding technique highlighted that it is rarely an inspiration for designers. He presented several techniques for modeling complex shapes by hand (Jackson 2011 ). He concluded with some techniques for transforming two-dimensional paper sheets into three-dimensional forms based on simple folding rules such as creasing, pleating, bending, collapsing, curving, and wrapping (Biagini and Donato 2013 ). Taking the advantage of this technique, both fields are sharing the same production process of transforming the 2D surfaces into 3D volume space hosting the body as the main element (Miles 2008 ). Folding in architecture is used as a structural and visual interest to manipulate the volumetric forms of the building, while in fashion, it gives both structure and stability to the garments. The different materials' behaviors in the garment industry allow main extensive techniques to appear as knitting and weaving, yet, they had a slow development in the textiles industry (Popescu et al. 2016 ).

Following mathematical and algorithmic rules extracted from nature, this technique can afford multiple patterns which introduces the concept of biomimicry that allows mimicking nature and responding to the external environment (Pearson 2001 ). Hence, a wide range of contemporary examples inspired architects and fashion designers to shift their work towards mimicking nature for a higher level of responsiveness to environmental conditions (Bugg 2011 ).

Technological Applications in the Works of Architects and Fashion Designers

The increasing technology integration in textiles as sensors have embedded electric functionality and generated new types of smart textiles (Li et al. 2019 ). These textiles are used in fashion and architecture and can be programmed through controlled properties pixel by pixel responding to environmental changes. Fabrication tools such as 3D printing, laser cutting, and Computerized Numerical Control milling (CNC), all based on the computerized manufacturing process and software, allow for controlling the parameters shifting people from product-oriented industries to the service-minded economy (Kuusk et al. 2012 ). The emergence of integrating sensors and motors inside materials resulted in wearable technology, which its origin in the military, healthcare, and space travel (Smelik 2017 ), yet, it has not been integrated widely into our daily fashion. Paulina,—a Dutch designer who is specialized in wearable technology—argued that the design is not about the technology per se, but a reaction of the moving body in space (Dongen 2020 ). This argument clarifies the similarities found in architecture and fashion where both are trying to respond to human bodies taking the surroundings as the main driver. Thus, the fashion industry started to focus on materials that can absorb the energy exuded from the body permeated through fabrics. This shifted the clothing to become living organisms that can sense and track, temperature, touch, sound, humidity, pressure, light, etc. (Kuusk et al. 2012 ). Therefore, many examples show how technology and textiles can combine to create architectural spaces or clothes respond to the environment. Several architects nowadays started to revive the fabric's power as a low-cost technique (Popescu et al. 2018 ).

Below are selections of some architects and fashion designers who integrated fabrics using different design methods and fabrication processes in their designs innovatively such as Block, Ahlquist, Chalayan, Gao and Bugg, Waibel and Jeon, and Herpen.

The KnitCandela is a concrete shell pavilion that integrated fabrics in architectural applications, designed by the Block Research Group at ETH Zurich with the collaboration of Zaha Hadid Architects. This pavilion integrated both architecture and fashion computationally, where the structure was inspired by the Spanish Mexican shells of Felix Candela, while the materials were stimulated by Jalisco's traditional dress in Mexico. The pavilion merged both a flexible cable-net from the outside and knitted-fabric work from the inside as a base to get the needed curvature from its flexibility (Popescu et al. 2020 ). This method was used to generate the double-curved surface based on knitted materials that can be tailored to three-dimensional forms without the need for gluing, welding, or cutting. The Mobius Rib-Knit is another project done by Sean Ahlquist which is based on material behavior that responded to imposed tensile forces of textile hybrid materials. The features of these materials were found among the high strength with low bending stiffness generating tensile and membrane surfaces. Ahlquist used software to generate a computational model that utilized a simulation for woven and weft-knitted textiles (Ahlquist 2015 ).

Moving to fashion designers using interactive garments, Hussein Chalayan, has pushed the boundaries of his collection towards turning them from static to responsive as architecture, besides responding to both external environment and internal body factors. He has integrated LED technology into some of his collections using climate as a metaphor to reflect feelings toward the weather. Thus, his strategy goes beyond the making of dresses to look like architecture to understand the environmental and functional factors (Quinn 2002 ). Jessica Bugg and Ying Gao focus on creating interactive breathing garments that can respond according to human movement. Bugg oriented her research and practice in developing methods for embodied clothing design and communication with the interdisciplinary practice of fashion, fine art, and performance. Her clothes fall into the realm of the body's performance, movement, and dancing body through sensory and embodied experience (Bugg 2014 ). The structure of Gao's collections are inspired by the social and urban environment transformations. For instance, one of her garments changes its form by twisting and curling when people come closer, while colors change when staring at them (Gao 2020 ). Yet, both designers are dealing with very high-tech material sensors which require some skills in mechanical engineering.

Jule Waibel and Eunjeong Jeon based their collections on the folding process. Waibel, a German designer, created around 25 dresses based on folded papers for Bershka's brand, produced by manual hand-pleating of large two-dimensional paper sheets into three-dimensional volumetric forms that fit the body (Howarth 2014 ). Although her non-architecture background resulted in creating more trial and error processes which is time-consuming due to some structural failure, yet her process of folding a full-scale garment was well-grounded. While Jeon, a Korean designer, used a module-based felted unit segmented by craft skills using folding and sewing techniques. She integrated, in one of her most famous collections ‘Trans-For-M-Otion,’ human feelings such as fear, happiness, and emotional response. When the wearer feels insecure ‘due to environmental changes in heartbeat, body temperature, respiration, or muscle tension, correspondingly, small air cells’ in the fabric fill up causing the garment to fit closer to the body providing protection (Hrga 2019 ) with the volumetric transformation (Jeon 2009 ) (Jeon 2013 ). The uniqueness of the structure can be seen in the simple repetitive 3D polygon-shaped unit that added a volume without any extra weight.

More recent evidence for a Dutch fashion designer Iris Van Herpen who continuously pushed the boundaries of fashion by integrating the 3D printing method in her collections (Scaturro and Tonkin 2017 ). Her designs are more oriented to organic futurism related to science and technology that disrupts conventional manufacturing. Herpen considered using nature as a guiding principle in her work resulting in unfamiliar forms which then are translated into adaptive clothes (Herpen 2019 ). In her project 'Sensory Seas,' she started to mimic some marine organisms to generate a 3D twisted vortex model with the assistance of Rhino and Grasshopper software using 3D printing and laser cutting to slice the different layers of fabrics (Herpen 2019 ). Each layer was then embellished by hand after form folding. Her special designs were found not while using traditional materials, but hard ones such as plastic, polyester, hi-the fabric, and metal (Smelik 2017 ). Although her designs were oriented more toward technology to overcome the limitation of the traditional technique, she used a hybrid system where craftsmanship remains important in the production of her garments given the final manual touch (Smelik 2017 ). She claims that there is still a gap between computer processes and traditional craftsmanship, yet she succeeded in proving her findings by trying to bind them. Thus far, she relies too heavily on technology as a driver more than an educational tool.

In the light of the dramatic technological transformation of design profession practices, Salama and Soliman argued that digital fabrication is reacting in a slow manner in Egyptian Universities. Thus, it is not yet widely reflected in the architectural curriculum especially the undergraduates (Salama and Crosbie 2010 ; Soliman et al. 2019 ). Accordingly, the concept of 'learning by doing' can start to shed light on the trial-and-error processes that are considered excellent educational experiences to sense the material behavior and the structure of the model.

Although the previous works showed the integration of advanced technology during the making process, however, understanding the basic process of computational design and the rules behind it before using any digital tools is the driver of this paper. The results of the workshop followed in this paper tried to answer questions that revolve around the validity of architecture in fashion design education, and how to investigate an experimental alternative that can enhance fashion design education rather than the traditional methods. The resulting garments are not competing with the market but presenting a new educational additive process inspired by architecture for fashion designers that weighs heavily on their process-based visualization tool and pattern cut.

Methods and Materials

To reach a design process for assisting fashion designers in generating garments using architectural principles, the paper analysed the results of a collaborative practice-based workshop titled ‘Fashion Clash’ that took place at a design studio in Cairo. The workshop lasted for two months and its scope falls into the realm of testing structural models to generate self-supporting garments manual fabrication without any software. The novelty of the workshop aimed to take a new look at exploring innovative ways of transforming the two-dimensional unit into a 3D self-supported model that starts with paper-based and ends with fabric. The participants were selected based on practice interviews through drawing a conceptual sketch for a futuristic garment by an architect and a fashion designer after which ten participants were selected from both disciplines. The age of the participants varied from 18 to 25 including both undergraduates and professionals. Mixed groups were formed that contained one architect and one fashion designer.

An evaluation of the design garments output was done with the participants through an in-depth interview by expert jurors from both fields. The assessment criteria were based on five main aspects with a total of eleven points; (1) the implementation of (inspiration, creativity, and structural model), (2) the unit (geometry, scalability, and expandability/repetition), (3) connectivity/joints, (4) garment (stability, wearability, and aesthetic), and (5) quality of fabrication. The assessment criteria were evaluated based on a rating scale of a score from 1 to 5 where 1 is the lowest and 5 is the highest. The resulting garments shared in a fashion show where real models wore them. The method followed is divided into three phases that are discussed briefly in this section.

This section shows the three phases that were carried out during the workshop (Fig.  1 ), (1) modeling and form-finding, (2) the assembly of self-supported form, and (3) fabrication by textile. The chart shows the difference between the traditional fashion design workflow and the new integrations that emerged from the workshop.

The traditional fashion design and the workshop workflow

The first phase focused on the theoretical background of the process of extracting rules and patterns from nature based on a parametric design approach to invite participants to extract their own rules. A projection was then displayed on a manikin to visualize the effect of complex patterns on different curved surfaces in the body (EL-Sayed 2012 ) to translate them into a full-scale garment. This phase assisted the fashion designers to explore folding techniques by hand with the help of architects. For selecting the suitable technique, some traditional methods used in fashion were revisited that Valle named a (Valle-noronha et al. 2020 ) (1) pattern-cut, which is difficult for visualizing the garment; (2) draping, which needs a body as a support for the design; and (3) tailoring, which is based on drawing patterns directly on the fabric. Those three methods were not adaptable to generate a structure model test. Accordingly, origami—the art of folding paper—as a folding method was tested manually giving new skills and different exposure to fashion designers. Using paper, the 2D flat surfaces turned into 3D modular unit/repetitive pattern. The challenge was to reach a scalable and flexible modular unit for extendability to be assemblied by studying the connections and joints for a self-supported model. Another challenge was to design joints from the same materials with less use of any external pins.

The second phase focused on turning the structured model into a self-supporting garment manually. To ensure the garment stability, the units/shapes were distributed and assembled to cover the needed part of the body using a manikin. In this stage, the self-supporting model assessed the designers to reach a self-stable form based on the topography of the body.

The third phase focused on transforming the structural paper garments into a real wearable textile after testing different fabrics' behaviors followed by jurors' evaluation. The resulting prototypes shared in a fashion exhibition highlighted the interdisciplinary approach where architects and fashion designers cooperated showing a strong relationship through full-scale fabricated garments with both paper-based and textile garments.

The main material used was paper due to its flexibility and capacity to reach self-supporting stable volumetric garments based on folding methods, unlike the traditional processes that use paper sketching and pattern-cutting on fabric directly which were resources-consuming. Although fibers show consistent behavior in both paper and textile, their properties are different. For instance in fabric, both knitting and weaving are responsible for obtaining higher strength and stability to produce textiles, yet they are different. The woven structure is based on interlacing two yarns that cross each other making it rigid (Kuusisto 2009 ), unlike knitting where the yarns are stitched/interlocked in multiple loops that give higher flexibility and elasticity to be more stretchy. During folding, (Fig.  2 b) the tied and weaved fibers caused gaps which gave the flexibility and softness required for bending without sharp edges unless strength was provided through layering, ironing, heating, sewing, or tailoring. In the case of paper (Fig.  2 a), the fibers were compacted and glued together without any spaces which gives strength and rigidity allowing sharp bending and causing a self-stable structure.

The fibres under the microscope × 50 and × 100 magnification; a compacted fibres in paper with no voids; b the voids in the textile

Modeling and Form-Finding

Interestingly, during this phase, the projected geometries on the manikin (Fig.  3 ) illustrated the deformation of the units across different curvatures of the body. The form-finding phase resulted in a various number of shapes that were geometrically adapted based on the body and translated by folding paper. This versatility sparked the curiosity to understand the folding processes' possibility to achieve self-supporting dresses based on repetitive units assembled on a modular grid. The contribution of the architects in each team was more substantial in this phase because of their experience in generating self-standing units, unlike fashion designers. Folded units were generated based on folding 2D surfaces with some cuts to create 3D volumes (Fig.  4 ) that were assembled to generate self-supporting models. Interestingly, the trial-and-error processes of the hands-on experience with physical models gave a different understanding of the structure for the fashion designers turning the 2D flat paper into a 3D unit, unlike their traditional methods.

The deformation of different patterns projected on the manikin. Image: AbdRabboh-s Design House 2014

The fabrication process and the folding transformation of the units

The Assembly of Self-Supporting Form

In this phase, the contribution of the fashion designers was higher transferring the self-supporting models to be assembled forming 3D garments on a manikin. The toile phase was essential where the units were assembled on the whole body. Different trial and error processes experimented with different folding and assembly techniques to match the manikin. Two approaches were used to reach tessellated units, (1) repetitive shapes with different connections and scales fitting perfectly together with no gaps in between (Fig.  5 a); (2) one continuous paper sheet without any joints based on origami technique (Fig.  5 b). Some problems regarding wearing garments occurred when fitting them on real models. Figure  5 c shows the results of the paper-based prototypes on a real model displayed at the fashion show.

a The self-supporting model and the garments using the same units and method; b the origami process made from one continuous sheet; c the final results of the paper-based garments, image: AbdRabboh-s Design House 2015

The Evaluation Process of the Paper Garments

With six expert jurors, three in each field, the paper garments were assessed based on the criteria mentioned in the method. The evaluation process was based on a score from 1 to 5, where each juror recorded his/her number. Figure  6 shows the evaluation of the even garments where it is noticed that the higher score were dresses no. 4, 5, and 6. These dresses succeeded because their structural model in the design phase was stable enough to become a self-supporting garment, the ability to be expandable in a repetitive way, and the strength in the connectivity, especially in dresses no. 5 and 6 where the origami continuous sheet assessed in this strength. In addition, they reached a good unit transition that is strong enough to hold the weight of the other materials through the way of the joints they created. Regardless of the fabrication quality, dresses 1, 5, 6, and 7 seemed to be of the best quality because of the minimum voids between the units. Dresses 1, 2, and 7 got a higher score in the expandability and the quality of fabrication, yet, their scalability and connections were too low compared to the other dresses.

The evaluation process of the jurors for the seven garments based on the listed criteria

This can be noticed because the units were joined together either with glue or by pins, unlike the other dresses where the units either interlocked or acted as contentious folded sheets. Some struggles were noticed in the structural model part from the fashion designer’s point of view because it was different than the traditional methods that rely on 2D drawing. However, they mentioned that this way of thinking based on 3D allowed them to be more creative to think about the process, not only the final form. It was more appealing to have a direct relation of visually mimicking forms in 3D by physically sensing the paper's fragility, lightness, and stability in different conditions without the need for any software.

Fabrication of Textile Garments

In this phase, the fashion designers turned the self-supporting paper dresses into real fabric, copper, and plastic dresses where they started to deal with textiles as a 3D form (Fig.  7 a). The conflict between the architects and the fashion designers started to appear in this stage where the designers struggled with transforming the same units into fabric. This stage needed multiple corrections to develop the main design unit. The challenge was to fold the textile and get the same results as the paper's sharp edges. Although fashion designers used to deal with paper pattern-cutting, dealing with 3D units made of paper was a new experience. The different material behavior during the transformation of the 3D unit into fabric resulted in inaccurate translations of the folded sharp lines in some garments (Fig.  7 b). Slight changes occurred in the final form because of the textile behaviors, where new gaps were found during the folding process. As stated previously, the fibers gaps in the textile caused smoother surfaces forcing adding more layers of fabric upon each unit besides using ironing to give it the rigidity of paper. This phase ended with a fashion show that presented the garments and provided the experience to fashion designers on basic architectural and structural principles that assisted them in reaching the results.

a The transforming process from paper-unit into a fabric; b The transformation of the self-supported model into garments; c the paper garments on real models, image: AbdRabboh-s Design House 2015

This section elaborates on the challenges faced during the integration of the two disciplines during the workshop. The durability and the self-supported garments were achieved by using paper-based structures, unlike the conventional process of starting with fabrics. Experimental models using paper at the early stage were essential and offered the possibility to create 3D geometries out of 2D flat surfaces that can reduce fabric waste and reach non-conventional forms. This process helped fashion designers to think differently with 3D structural models instead of 2D drawings. It was noticed that the modular unit made of paper helped reach flexibility with different scales and openings from the same modular unit reaching the extendability of the garments. The initial ideas resulting from the ‘paper-folding’ exercise generated different alternatives depending on the connections. Hence, the novelty of the resulting garments can be seen in the transition of the unit from paper to textile, which accelerated the fabrication process, unlike starting with fabric which slowed the production process. The fabric garments were affected by the materials' behaviour and the folding techniques (Fig.  8 ), where both depended on each other based on the fibres’ compaction that allowed generating sharp or soft edges. Therefore, the matter knowledge of materials is an essential requirement for predicting the feasibility of the result and also in raising many constraints in the fabrication and assembly process. Thus, several fabrication processes were used in the textile such as; creasing, pleating, bending, knotting, hinging, corrugating, twisting, crumpling, curving, and wrapping. Such processes enabled the designers to achieve similar self-supporting garments which highlighted the cross-disciplinary method in the two fields.

Left: Two dresses made out of paper; Right: dresses out of fabric. Images:AbdRabboh-s Design House 2015

Each group had a limited budget for the fabric garment to think economically about material reduction. Another challenge was to reach the same desired results by fashion designers when turning the rigid paper units into textiles, especially in the origami process based on one sheet. This challenge can be solved in further applications through 3D printing on the fabric using other rigid materials that strengthen the edges.

Some clashes occurred between the participants in both disciplines for the difference in the design process and workflow that were based on 3D units, unlike the traditional 2D pattern-cutting process. However, the manual fabrication of the structural models enhanced the participants' quality of engagement and skills in architecture with architects' assistance. This allowed the designers to generate non-conventional dresses based on a self-supporting structure. The integration of the roles of both architects and fashion designers, during the different phases, showed the innovative solutions of integrating their strategies resulting in garments with both paper and textiles.

The results of the garments were not accurate enough, nevertheless, digital fabrication tools would enhance the results and reduce waste materials. The assistance of software can easily create folded patterns which can be implemented from the beginning in an upcoming workshop to ensure that the time frame and all participants from different disciplines have the same level of ability to use a specific software.

The paper discussed the implementation of parametric design logic as the main driver without using digital software to build full-scale garments which gave participants the knowledge to analyze mathematical rules manually. The focus of the workshop depended on proposing a computational thinking process for fashion designers on how to translate a self-supporting model into a real self-supporting garment based on folding techniques.

The research presented an innovative manual workflow that fostered new methods for designers and followed empirical experimentation highlighting the intersection between architects and fashion designers. The paper showed the potential for exploring innovative ways of testing geometries which highlighted the potential of developing a way of creating clothing based on folding techniques. The manual process allowed fashion designers to deal with fabrics differently, turning the architectural self-supporting prototype from 2D into 3D structured garment papers followed by 3D textiles. In a wider sense, a new model of computational thinking has been discussed, and while not completely new for architects, fashion designers learned to use nature as a source of inspiration.

So far, the investigation of this paper had only been on customized dresses, yet, mass customization needs wider treatment. In the future, this workflow can be applied digitally to compute the forces, strengths, weaknesses, and structure, besides testing the assembly before fabrication. Thus, our results encouraged the validation of digital sample sizes comparing the waste reduction of both techniques. Further work needs to integrate a hybrid design practice process to study the surfaces which can increase space for creativity while reducing structure and material usage. Finally, integrating digital technologies in fashion based on science, technology, and craftsmanship will offer new opportunities to generate smart textiles and wearable technologies that are mainly driven by material sciences and advanced technology. This can transform embodied experience according to the external environment to enable new relationships between people and clothing. Yet, some social interactions and cultural practices need to be taken into consideration to add value to clothing. This will fill the gap between the computer process and traditional manual craftsmanship which opens the door toward new developing techniques that mimic traditional crafts.

Data availability

Data generated during this research are available from the corresponding author upon request.

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Acknowledgements

All images/figures are by the author except images in figures; 3, 5-c, 7-c, and 8 by AbdRabboh-s Design House. The author would like to thank architect Mahmoud AbdRabboh, AbdRabboh-s Design House for the invitation to be part of this workshop in cooperation with Moja "Mohamed Khafagi and Jasmine Abdelwahab". Many thanks to Ghadeer Khaled, Daniel Salib for setting the shooting of the models under the supervision of AbdRabboh-s Design House and the participants from both architecture and fashion design fields who were the main active part of this challenging experience.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

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El-Mahdy, D. Experimental Structural Model: From Manual Paper Garment to Fabrication as an Architectural Practice-Based Approach for Fashion Design Education. Nexus Netw J 25 , 1015–1032 (2023). https://doi.org/10.1007/s00004-023-00732-1

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News Roundup Spring 2024

CEGE Spring Graduation Celebration and Order of the Engineer

Forty-seven graduates of the undergraduate and grad student programs (pictured above) in the Department of Civil, Environmental, and Geo- Engineering took part in the Order of the Engineer on graduation day. Distinguished Speakers at this departmental event included Katrina Kessler (MS EnvE 2021), Commissioner of the Minnesota Pollution Control Agency, and student Brian Balquist. Following this event, students participated in the college-wide Commencement Ceremony at 3M Arena at Mariucci. 

UNIVERSITY & DEPARTMENT

The University of Minnesota’s Crookston, Duluth, and Rochester campuses have been awarded the Carnegie Elective Classification for Community Engagement, joining the Twin Cities (2006, 2015) and Morris campuses (2015), and making the U of M the country’s first and only university system at which every individual campus has received this selective designation. Only 368 from nearly 4,000 qualifying U.S. universities and colleges have been granted this designation.

CEGE contributed strongly to the College of Science and Engineering’s efforts toward sustainability research. CEGE researchers are bringing in over $35 million in funded research to study carbon mineralization, nature and urban areas, circularity of water resources, and global snowfall patterns. This news was highlighted in the Fall 2023 issue of  Inventing Tomorrow  (pages 10-11). https://issuu.com/inventingtomorrow/docs/fall_2023_inventing_tomorrow-web

CEGE’s new program for a one-year master’s degree in structural engineering is now accepting applicants for Fall 2024. We owe a big thanks to DAN MURPHY and LAURA AMUNDSON for their volunteer work to help curate the program with Professor JIA-LIANG LE and EBRAHIM SHEMSHADIAN, the program director. Potential students and companies interested in hosting a summer intern can contact Ebrahim Shemshadian ( [email protected] ).

BERNIE BULLERT , CEGE benefactor and MN Water Research Fund founder, was profiled on the website of the University of Minnesota Foundation (UMF). There you can read more about his mission to share clean water technologies with smaller communities in Minnesota. Many have joined Bullert in this mission. MWRF Recognizes their Generous 2024 Partners. Gold Partners: Bernie Bullert, Hawkins, Inc., Minnesota Department of Health, Minnesota Pollution Control Agency, and SL-serco. Silver Partners: ISG, Karl and Pam Streed, Kasco, Kelly Lange-Haider and Mark Haider, ME Simpson, Naeem Qureshi, Dr. Paul H. Boening, TKDA, and Waterous. Bronze Partners: Bruce R. Bullert; Brenda Lenz, Ph.D., APRN FNP-C, CNE; CDM Smith; Central States Water Environment Association (CSWEA MN); Heidi and Steve Hamilton; Jim “Bulldog” Sadler; Lisa and Del Cerney; Magney Construction; Sambatek; Shannon and John Wolkerstorfer; Stantec; and Tenon Systems.

After retiring from Baker-Tilly,  NICK DRAGISICH  (BCE 1977) has taken on a new role: City Council member in Lake Elmo, Minnesota. After earning his BCE from the University of Minnesota, Dragisich earned a master’s degree in business administration from the University of St. Thomas. Dragisich retired in May from his position as managing director at Baker Tilly, where he had previously served as firm director. Prior to that, he served as assistant city manager in Spokane, Washington, was the city administrator and city engineer in Virginia, Minnesota, and was mayor of Chisholm, Minnesota—all adding up to more than 40 years of experience in local government. Dragisich was selected by a unanimous vote. His current term expires in December 2024.

PAUL F. GNIRK  (Ph.D. 1966) passed away January 29, 2024, at the age of 86. A memorial service was held Saturday, February 24, at the South Dakota School of Mines and Technology (SDSM&T), where he started and ended his teaching career, though he had many other positions, professional and voluntary. In 2018 Paul was inducted into the SDSM&T Hardrocker Hall of Fame, and in 2022, he was inducted into the South Dakota Hall of Fame, joining his mother Adeline S. Gnirk, who had been inducted in 1987 for her work authoring nine books on the history of south central South Dakota.

ROGER M. HILL  (BCE 1957) passed away on January 13, 2024, at the age of 90. His daughter, Kelly Robinson, wrote to CEGE that Roger was “a dedicated Gopher fan until the end, and we enjoyed many football games together in recent years. Thank you for everything.”

KAUSER JAHAN  (Ph.D. 1993, advised by Walter Maier), PE, is now a civil and environmental engineering professor and department head at Henry M. Rowan College of Engineering. Jahan was awarded a 3-year (2022- 2025), $500,000 grant from the U.S. Department of Environmental Protection Agency (USEPA). The grant supports her project, “WaterWorks: Developing the New Generation of Workforce for Water/Wastewater Utilities,” for the development of educational tools that will expose and prepare today’s students for careers in water and wastewater utilities.

SAURA JOST  (BCE 2010, advised by Timothy LaPara) was elected to the St. Paul City Council for Ward 3. She is part of the historic group of women that make up the nation’s first all-female city council in a large city.

The 2024 ASCE Western Great Lakes Student Symposium combines several competitions for students involved in ASCE. CEGE sent a large contingent of competitors to Chicago. Each of the competition groups won awards: Ethics Paper 1st place Hans Lagerquist; Sustainable Solutions team 1st place overall in (qualifying them for the National competition in Utah in June); GeoWall 2nd place overall; Men’s Sprint for Concrete Canoe with rowers Sakthi Sundaram Saravanan and Owen McDonald 2nd place; Product Prototype for Concrete Canoe 2nd place; Steel Bridge (200 lb bridge weight) 2nd place in lightness; Scavenger Hunt 3rd place; and Aesthetics and Structural Efficiency for Steel Bridge 4th place.

Students competing on the Minnesota Environmental Engineers, Scientists, and Enthusiasts (MEESE) team earned second place in the Conference on the Environment undergraduate student design competition in November 2023. Erin Surdo is the MEESE Faculty Adviser. Pictured are NIKO DESHPANDE, ANNA RETTLER, and SYDNEY OLSON.

The CEGE CLASS OF 2023 raised money to help reduce the financial barrier for fellow students taking the Fundamentals of Engineering exam, a cost of $175 per test taker. As a result of this gift, they were able to make the exam more affordable for 15 current CEGE seniors. CEGE students who take the FE exam pass the first time at a rate well above national averages, demonstrating that CEGE does a great job of teaching engineering fundamentals. In 2023, 46 of 50 students passed the challenging exam on the first try.

This winter break, four CEGE students joined 10 other students from the College of Science and Engineering for the global seminar, Design for Life: Water in Tanzania. The students visited numerous sites in Tanzania, collected water source samples, designed rural water systems, and went on safari. Read the trip blog: http://globalblogs.cse.umn.edu/search/label/Tanzania%202024

Undergraduate Honor Student  MALIK KHADAR  (advised by Dr. Paul Capel) received honorable mention for the Computing Research Association (CRA) Outstanding Undergraduate Research Award for undergraduate students who show outstanding research potential in an area of computing research.

GRADUATE STUDENTS

AKASH BHAT  (advised by William Arnold) presented his Ph.D. defense on Friday, October 27, 2023. Bhat’s thesis is “Photolysis of fluorochemicals: Tracking fluorine, use of UV-LEDs, and computational insights.” Bhat’s work investigating the degradation of fluorinated compounds will assist in the future design of fluorinated chemicals such that persistent and/or toxic byproducts are not formed in the environment.

ETHAN BOTMEN  (advised by Bill Arnold) completed his Master of Science Final Exam February 28, 2024. His research topic was Degradation of Fluorinated Compounds by Nucleophilic Attack of Organo-fluorine Functional Groups.

XIATING CHEN , Ph.D. Candidate in Water Resources Engineering at the Saint Anthony Falls Laboratory is the recipient of the 2023 Nels Nelson Memorial Fellowship Award. Chen (advised by Xue Feng) is researching eco-hydrological functions of urban trees and other green infrastructure at both the local and watershed scale, through combined field observations and modeling approaches.

ALICE PRATES BISSO DAMBROZ  has been a Visiting Student Researcher at the University of Minnesota since last August, on a Doctoral Dissertation Research Award from Fulbright. Her CEGE advisor is Dr. Paul Capel. Dambroz is a fourth year Ph.D. student in Soil Science at Universidade Federal de Santa Maria in Brazil, where she studies with her adviser Jean Minella. Her research focuses on the hydrological monitoring of a small agricultural watershed in Southern Brazil, which is located on a transition area between volcanic and sedimentary rocks. Its topography, shallow soils, and land use make it prone to runoff and erosion processes.

Yielding to people in crosswalks should be a very pedestrian topic. Yet graduate student researchers  TIANYI LI, JOSHUA KLAVINS, TE XU, NIAZ MAHMUD ZAFRI  (Dept.of Urban and Regional Planning at Bangladesh University of Engineering and Technology), and Professor Raphael Stern found that drivers often do not yield to pedestrians, but they are influenced by the markings around a crosswalk. Their work was picked up by the  Minnesota Reformer.

TIANYI LI  (Ph.D. student advised by Raphael Stern) also won the Dwight David Eisenhower Transportation (DDET) Fellowship for the third time! Li (center) and Stern (right) are pictured at the Federal Highway Administration with Latoya Jones, the program manager for the DDET Fellowship.

The Three Minute Thesis Contest and the Minnesota Nice trophy has become an annual tradition in CEGE. 2023’s winner was  EHSANUR RAHMAN , a Ph.D. student advised by Boya Xiong.

GUANJU (WILLIAM) WEI , a Ph.D. student advised by Judy Yang, is the recipient of the 2023 Heinz G. Stefan Fellowship. He presented his research entitled Microfluidic Investigation of the Biofilm Growth under Dynamic Fluid Environments and received his award at the St. Anthony Falls Research Laboratory April 9. The results of Wei's research can be used in industrial, medical, and scientific fields to control biofilm growth.

BILL ARNOLD  stars in an award-winning video about prairie potholes. The Prairie Potholes Project film was made with the University of Delaware and highlights Arnold’s NSF research. The official winners of the 2024 Environmental Communications Awards Competition Grand Prize are Jon Cox and Ben Hemmings who produced and directed the film. Graduate student Marcia Pacheco (CFANS/LAAS) and Bill Arnold are the on-screen stars.

Four faculty from CEGE join the Center for Transportation Studies Faculty and Research Scholars for FY24–25:  SEONGJIN CHOI, KETSON ROBERTO MAXIMIANO DOS SANTOS, PEDRAM MORTAZAVI,  and  BENJAMIN WORSFOLD . CTS Scholars are drawn from diverse fields including engineering, planning, computer science, environmental studies, and public policy.

XUE FENG  is coauthor on an article in  Nature Reviews Earth and Environment . The authors evaluate global plant responses to changing rainfall regimes that are now characterized by fewer and larger rainfall events. A news release written at Univ. of Maryland can be found here: https://webhost.essic. umd.edu/april-showers-bring-mayflowers- but-with-drizzles-or-downpours/ A long-running series of U of M research projects aimed at improving stormwater quality are beginning to see practical application by stormwater specialists from the Twin Cities metro area and beyond. JOHN GULLIVER has been studying best practices for stormwater management for about 16 years. Lately, he has focused specifically on mitigating phosphorous contamination. His research was highlighted by the Center for Transportation Studies.

JIAQI LI, BILL ARNOLD,  and  RAYMOND HOZALSKI  published a paper on N-nitrosodimethylamine (NDMA) precursors in Minnesota rivers. “Animal Feedlots and Domestic Wastewater Discharges are Likely Sources of N-Nitrosodimethylamine (NDMA) Precursors in Midwestern Watersheds,” Environmental Science and Technology (January 2024) doi: 10.1021/acs. est.3c09251

ALIREZA KHANI  contributed to MnDOT research on Optimizing Charging Infrastructure for Electric Trucks. Electric options for medium- and heavy-duty electric trucks (e-trucks) are still largely in development. These trucks account for a substantial percentage of transportation greenhouse gas emissions. They have greater power needs and different charging needs than personal EVs. Proactively planning for e-truck charging stations will support MnDOT in helping to achieve the state’s greenhouse gas reduction goals. This research was featured in the webinar “Electrification of the Freight System in Minnesota,” hosted by the University of Minnesota’s Center for Transportation Studies. A recording of the event is now available online.

MICHAEL LEVIN  has developed a unique course for CEGE students on Air Transportation Systems. It is the only class at UMN studying air transportation systems from an infrastructure design and management perspective. Spring 2024 saw the third offering of this course, which is offered for juniors, seniors, and graduate students.

Research Professor  SOFIA (SONIA) MOGILEVSKAYA  has been developing international connections. She visited the University of Seville, Spain, November 13–26, 2023, where she taught a short course titled “Fundamentals of Homogenization in Composites.” She also met with the graduate students to discuss collaborative research with Prof. Vladislav Mantic, from the Group of Continuum Mechanics and Structural Analysis at the University of Seville. Her visit was a part of planned activities within the DIAGONAL Consortium funded by the European Commission. CEGE UMN is a partner organization within DIAGONAL, represented by CEGE professors Mogilevskaya and Joseph Labuz. Mantic will visit CEGE summer 2024 to follow up on research developments and discuss plans for future collaboration and organization of short-term exchange visits for the graduate students from each institution. 

DAVID NEWCOMB  passed away in March. He was a professor in CEGE from 1989–99 in the area of pavement engineering. Newcomb led the research program on asphalt materials characterization. He was the technical director of Mn/ROAD pavement research facility, and he started an enduring collaboration with MnDOT that continues today. In 2000, he moved from Minnesota to become vice-president for Research and Technology at the National Asphalt Pavement Association. Later he moved to his native Texas, where he was appointed to the division head of Materials and Pavement at the Texas A&M Transportation Institute, a position from which he recently retired. He will be greatly missed.

PAIGE NOVAK  won Minnesota ASCE’s 2023 Distinguished Engineer of the Year Award for her contributions to society through her engineering achievements and professional experiences.

The National Science Foundation (NSF) announced ten inaugural (NSF) Regional Innovation Engines awards, with a potential $1.6 billion investment nationally over the next decade. Great Lakes ReNEW is led by the Chicago-based water innovation hub,  Current,  and includes a team from the University of Minnesota, including PAIGE NOVAK. Current will receive $15 mil for the first two years, and up to $160 million over ten years to develop and grow a water-focused innovation engine in the Great Lakes region. The project’s ambitious plan is to create a decarbonized circular “blue economy” to leverage the region’s extraordinary water resources to transform the upper Midwest—Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin. Brewing one pint of beer generates seven pints of wastewater, on average. So what can you do with that wastewater?  PAIGE NOVAK  and her team are exploring the possibilities of capturing pollutants in wastewater and using bacteria to transform them into energy.

BOYA XIONG  has been selected as a recipient of the 2024 40 Under 40 Recognition Program by the American Academy of Environmental Engineers and Scientists. The award was presented at the 2024 AAEES Awards Ceremony, April 11, 2024, at the historic Howard University in Washington, D.C. 

JUDY Q. YANG  received a McKnight Land-Grant Professorship Award. This two-year award recognizes promising assistant professors and is intended to advance the careers of individuals who have the potential to make significant contributions to their departments and their scholarly fields. 

Professor Emeritus CHARLES FAIRHURST , his son CHARLES EDWARD FAIRHURST , and his daughter MARGARET FAIRHURST DURENBERGER were on campus recently to present Department Head Paige Novak with a check for $25,000 for the Charles Fairhurst Fellowship in Earth Resources Engineering in support of graduate students studying geomechanics. The life of Charles Fairhurst through a discussion with his children is featured on the Engineering and Technology History Wiki at https://ethw.org/Oral-History:Charles_Fairhurst#00:00:14_INTRODUCTION

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Microsoft Research Blog

Microsoft at chi 2024: innovations in human-centered design.

Published May 15, 2024

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The ways people engage with technology, through its design and functionality, determine its utility and acceptance in everyday use, setting the stage for widespread adoption. When computing tools and services respect the diversity of people’s experiences and abilities, technology is not only functional but also universally accessible. Human-computer interaction (HCI) plays a crucial role in this process, examining how technology integrates into our daily lives and exploring ways digital tools can be shaped to meet individual needs and enhance our interactions with the world.

The ACM CHI Conference on Human Factors in Computing Systems is a premier forum that brings together researchers and experts in the field, and Microsoft is honored to support CHI 2024 as a returning sponsor. We’re pleased to announce that 33 papers by Microsoft researchers and their collaborators have been accepted this year, with four winning the Best Paper Award and seven receiving honorable mentions.

This research aims to redefine how people work, collaborate, and play using technology, with a focus on design innovation to create more personalized, engaging, and effective interactions. Several projects emphasize customizing the user experience to better meet individual needs, such as exploring the potential of large language models (LLMs) to help reduce procrastination. Others investigate ways to boost realism in virtual and mixed reality environments, using touch to create a more immersive experience. There are also studies that address the challenges of understanding how people interact with technology. These include applying psychology and cognitive science to examine the use of generative AI and social media, with the goal of using the insights to guide future research and design directions. This post highlights these projects.

Microsoft Research Podcast

Collaborators: Renewable energy storage with Bichlien Nguyen and David Kwabi

Dr. Bichlien Nguyen and Dr. David Kwabi explore their work in flow batteries and how machine learning can help more effectively search the vast organic chemistry space to identify compounds with properties just right for storing waterpower and other renewables.

Best Paper Award recipients

DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing   Priyan Vaithilingam, Elena L. Glassman, Jeevana Priya Inala , Chenglong Wang   GUIs used for editing visualizations can overwhelm users or limit their interactions. To address this, the authors introduce DynaVis, which combines natural language interfaces with dynamically synthesized UI widgets, enabling people to initiate and refine edits using natural language.  

Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking   Nikhil Sharma, Q. Vera Liao , Ziang Xiao   Conversational search systems powered by LLMs potentially improve on traditional search methods, yet their influence on increasing selective exposure and fostering echo chambers remains underexplored. This research suggests that LLM-driven conversational search may enhance biased information querying, particularly when the LLM’s outputs reinforce user views, emphasizing significant implications for the development and regulation of these technologies.  

Piet: Facilitating Color Authoring for Motion Graphics Video   Xinyu Shi, Yinghou Wang, Yun Wang , Jian Zhao   Motion graphic (MG) videos use animated visuals and color to effectively communicate complex ideas, yet existing color authoring tools are lacking. This work introduces Piet, a tool prototype that offers an interactive palette and support for quick theme changes and controlled focus, significantly streamlining the color design process.

The Metacognitive Demands and Opportunities of Generative AI   Lev Tankelevitch , Viktor Kewenig, Auste Simkute, Ava Elizabeth Scott, Advait Sarkar , Abigail Sellen , Sean Rintel   Generative AI systems offer unprecedented opportunities for transforming professional and personal work, yet they present challenges around prompting, evaluating and relying on outputs, and optimizing workflows. This paper shows that metacognition—the psychological ability to monitor and control one’s thoughts and behavior—offers a valuable lens through which to understand and design for these usability challenges.  

Honorable Mentions

B ig or Small, It’s All in Your Head: Visuo-Haptic Illusion of Size-Change Using Finger-Repositioning Myung Jin Kim, Eyal Ofek, Michel Pahud , Mike J. Sinclair, Andrea Bianchi   This research introduces a fixed-sized VR controller that uses finger repositioning to create a visuo-haptic illusion of dynamic size changes in handheld virtual objects, allowing users to perceive virtual objects as significantly smaller or larger than the actual device. 

LLMR: Real-time Prompting of Interactive Worlds Using Large Language Models   Fernanda De La Torre, Cathy Mengying Fang, Han Huang, Andrzej Banburski-Fahey, Judith Amores , Jaron Lanier   Large Language Model for Mixed Reality (LLMR) is a framework for the real-time creation and modification of interactive mixed reality experiences using LLMs. It uses novel strategies to tackle difficult cases where ideal training data is scarce or where the design goal requires the synthesis of internal dynamics, intuitive analysis, or advanced interactivity. 

Observer Effect in Social Media Use   Koustuv Saha, Pranshu Gupta, Gloria Mark, Emre Kiciman , Munmun De Choudhury   This work investigates the observer effect in behavioral assessments on social media use. The observer effect is a phenomenon in which individuals alter their behavior due to awareness of being monitored. Conducted over an average of 82 months (about 7 years) retrospectively and five months prospectively using Facebook data, the study found that deviations in expected behavior and language post-enrollment in the study reflected individual psychological traits. The authors recommend ways to mitigate the observer effect in these scenarios.

Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming   Hussein Mozannar, Gagan Bansal , Adam Fourney , Eric Horvitz   By investigating how developers use GitHub Copilot, the authors created CUPS, a taxonomy of programmer activities during system interaction. This approach not only elucidates interaction patterns and inefficiencies but can also drive more effective metrics and UI design for code-recommendation systems with the goal of improving programmer productivity. 

SharedNeRF: Leveraging Photorealistic and View-dependent Rendering for Real-time and Remote Collaboration   Mose Sakashita, Bala Kumaravel, Nicolai Marquardt , Andrew D. Wilson   SharedNeRF, a system for synchronous remote collaboration, utilizes neural radiance field (NeRF) technology to provide photorealistic, viewpoint-specific renderings that are seamlessly integrated with point clouds to capture dynamic movements and changes in a shared space. A preliminary study demonstrated its effectiveness, as participants used this high-fidelity, multi-perspective visualization to successfully complete a flower arrangement task. 

Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination   Ananya Bhattacharjee, Yuchen Zeng, Sarah Yi Xu, Dana Kulzhabayeva, Minyi Ma, Rachel Kornfield, Syed Ishtiaque Ahmed, Alex Mariakakis, Mary P. Czerwinski , Anastasia Kuzminykh, Michael Liut, Joseph Jay Williams   In this study, the authors explore the potential of LLMs for customizing academic procrastination interventions, employing a technology probe to generate personalized advice. Their findings emphasize the need for LLMs to offer structured, deadline-oriented advice and adaptive questioning techniques, providing key design insights for LLM-based tools while highlighting cautions against their use for therapeutic guidance.

Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration   Haotian Li, Yun Wang , Huamin Qu This paper evaluates data storytelling tools using a dual framework to analyze the stages of the storytelling workflow—analysis, planning, implementation, communication—and the roles of humans and AI in each stage, such as creators, assistants, optimizers, and reviewers. The study identifies common collaboration patterns in existing tools, summarizes lessons from these patterns, and highlights future research opportunities for human-AI collaboration in data storytelling.

Learn more about our work and contributions to CHI 2024, including our full list of publications , on our conference webpage .

Related publications

Big or small, it’s all in your head: visuo-haptic illusion of size-change using finger-repositioning, llmr: real-time prompting of interactive worlds using large language models, reading between the lines: modeling user behavior and costs in ai-assisted programming, observer effect in social media use, where are we so far understanding data storytelling tools from the perspective of human-ai collaboration, the metacognitive demands and opportunities of generative ai, piet: facilitating color authoring for motion graphics video, dynavis: dynamically synthesized ui widgets for visualization editing, generative echo chamber effects of llm-powered search systems on diverse information seeking, understanding the role of large language models in personalizing and scaffolding strategies to combat academic procrastination, sharednerf: leveraging photorealistic and view-dependent rendering for real-time and remote collaboration, continue reading.

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