Generative Design Solution Space Parsing: An Evaluation of User Experience, Workload, and Performance

Abstract

Generative design systems produce myriad design alternatives that comply with stated requirements. Since generative design systems yield the greatest benefits during conceptual design, requirements are often ambiguous and are comprised of mixed variables (e.g., categorical, continuous, etc.), which leads to a generative design solution space with a plethora of options for the user to review. With a plethora of design alternatives to choose from, many of which are similar, increased user workload leads to inefficient design selection processes. Subsequently, inefficient design selection processes could result in a negative user experience and improper design alternative selection. Therefore, it is imperative that generative design systems leverage parsing methods that methodologically reduce the quantity of design options that are presented to the user, while retaining novel designs from distinct solution space regions. Although parsing a solution space can yield smaller subsets of design alternatives, it is also imperative to consider how the subsets are presented to the user. A user study (N=49) was performed to evaluate user performance, workload, and experience during a generative design selection process, given manipulation of both the quantity and filtering of parsed subsets of alternatives. Subsets were filtered using cluster analysis using one of seven parameters, where participants experienced two filters across seven iterations each. Results show that cognitive workload is reduced when a design solution space consists of 50 to 100 design alternatives, with a clustering parsing method that considers all design alternative variables. Study findings can further be applied to other domains where a user is presented with a plethora of alternative options, requiring a method for improving the decision-making process.

Keywords

Designer / cognition / cognitive load / usability / cluster analysis

References

[1] AliN G, AbedS D, ShabanF A J, TongkachokK, RayS, JaleelR A. Hybrid of K-means and partitioning around medoids for predicting COVID-19 cases: Iraq case study. Periodicals of Engineering and Natural Sciences, 2021, 9(4): 569-579

[2] BentleyP, CorneDAn introduction to creative evolutionary systems, 20021-75

[3] BevanN. Classifying and selecting UX and usability measures. Proceedings of the International Workshop of Meaningful Measures: Valid Useful User Experience Measurement (VUUM), 200815-18

[4] BhatA. K-medoids clustering using partitioning around medoids for performing face recognition. International Journal of Soft Computing, Mathematics and Control, 2014, 3(3): 1-12

[5] BotyarovM, MillerE E. Partitioning around medoids as a systematic approach to generative design solution space reduction. Results in Engineering, 2022, 15: 100544

[6] BuHamdan S, Alwisy A, Bouferguene A (2021). Generative systems in the architecture, engineering and construction industry: A systematic review and analysis. International Journal of Architectural Computing 19(3).

[7] CaganJ, GrossmannI E, HookerJ. A conceptual framework for combining artificial intelligence and optimization in engineering design. Research in Engineering Design, 1997, 9: 20-34

[8] ChandrasegaranS K, RamaniK, SriramR D, HorváthI, BernardA, HarikR F, GaoW. The evolution, challenges, and future of knowledge representation in product design systems. Computer-Aided Design, 2013, 45(2): 204-228

[9] Chien S F, Flemming U (2002). Design space navigation in generative design systems. Automation in Construction 11(1).

[10] Dos SantosD H, SoaresA G M, LimaR S D A D, MirandaE T C, Dos SantosC G R, MeiguinsB S. Analyzing the effect of different partial overlap sizes in perceiving visual variables. Proceedings of the International Conference on Information Visualisation, 201937-43

[11] GrierR A. How high is high? A Meta-analysis of NASA-TLX global workload scores. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2015, 59(1): 1727-1731

[12] HartS G, StavelandL E. Development of NASA-TLX (task load index): Results of empirical and theoretical research. Advances in Psychology, 1988, 52: 139-183

[13] ISO 17488:2016. Road vehicles – Transport information and control systems – Detection-response task (DRT) for assessing attentional effects of cognitive load in driving. International Organization for Standardization, Geneva, Switzerland.

[14] JennessJ W, BoyleL N, LeeJ D, MillerE E, YahoodikS, HueyR, LeeJ Y, BenedickA K, PetragliaEInvehicle voice control interface evaluation: Preliminary driver workload and risk analysis, 2020, Washington, DC, National Highway Traffic Safety Administration (NHTSA)DOT HS 812 813

[15] JohnsonB RDesign computing: An overview of an emergent field, 2016

[16] Korner-NievergeltF, RothT, FeltenS, GuélatJ, AlmasiB, Korner-NievergeltP. Chapter 7: Linear mixed effects models. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN Including Comparisons to Frequentist Statistics, 2015

[17] KhanS, AwanM. A generative design technique for exploring shape variations. Advanced Engineering Informatics, 2018, 38: 712-724

[18] KnollD, FortinC, GolkarA. Review of concurrent engineering design practice in the space sector: State of the art and future perspectives. Proceedings of the 4th IEEE International Symposium on Systems Engineering, ISSE 2018, 2018

[19] KrishS. A practical generative design method. Computer-Aided Design, 2010, 43: 88-100

[20] KrupaG P. Application of agile model-based systems engineering in aircraft conceptual design. Aeronautical Journal, 2019, 123(1268): 1561-1601

[21] ManningM L, FinlayD C, NeillR A, FrostB G. Detection threshold differences to crossed and uncrossed disparities. Vision Research, 1987, 27(9): 1683-1686

[22] McKnightM. Generative design: What it is? How is it being used? Why it’s a game changer!. The International Conference on Design and Technology, 2017176-181

[23] MillerE E, BoyleL N, JennessJ W, LeeJ D. Voice control tasks on cognitive workload and driving performance: Implications of modality, difficulty, and duration. Transportation Research Record, 2018, 2672(37): 84-93

[24] MountstephensJ, TeoJ. Progress and challenges in generative product design: A review of systems. Computers, 2020, 9(4): 80

[25] NtiI K, AdekoyaA F, WeyoriB A, Nyarko-BoatengO. Applications of artificial intelligence in engineering and manufacturing: A systematic review. Journal of Intelligent Manufacturing, 2022, 33(6): 1581-1601

[26] OxmanR. The thinking eye: Visual re-cognition in design emergence. Design Studies, 2002, 23: 135-164

[27] PanchalJ H, FugeM, LiuY, MissoumS, TuckerC. Machine learning for engineering design. Journal of Mechanical Design, 2019, 141(11): 110301

[28] Prabaswari A, Basumerda C, Utomo B (2019). The mental workload analysis of staff in study program of private educational organization. IOP Conference Series: Materials Science and Engineering: 528.

[29] PhamD T, PhamP T N. Artificial intelligence in engineering. International Journal of Machine Tools and Manufacture, 1999, 39(6): 937-949

[30] Piasecki M, Hanna S (2011). A redefinition of the paradox of choice. Design Computing and Cognition: 347–366.

[31] PignatielloG, RichardM, HickmanR. Decision fatigue: A conceptual analysis. Journal of Health Psychology, 2020, 25(1): 123-135

[32] PolmanE, VohsK. Decision fatigue, choosing for others, and self-construal. Social, Psychological, and Personality Science, 2016, 7(5): 471-478

[33] Scheidl R, Winkler B (2010). Model relations between conceptual and detail design. Mechatronics 20(8).

[34] Schulz A, Wang H, Grinspun E, Solomon J, Matusik W (2018). Interactive exploration of design trade-offs. ACM Transactions on Graphics 37(4).

[35] SinghV, GuN. Towards an integrated generative design framework. Design Studies, 2012, 33(2): 185-207

[36] StojmenovaK, SodnikJ. Detection-response task – Uses and limitations. Sensors, 2018, 18(2): 594

[37] TroianoL, BirtoloC. Genetic algorithms supporting generative design of user interfaces: Examples. Information Sciences, 2014, 259: 433-451

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Systems Engineering Society of China and Springer-Verlag GmbH Germany