Application of Artificial Intelligence in Design Automation: A Two-Stage Framework for Structure Configuration and Design
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 8
Abstract
Civil engineering design problems are inherently complex, characterized by iterative processes, multiple criteria, and time-consuming manual design work. Traditional methods often struggle to rapidly reach optimal designs, lacking guarantees of achieving optimality. With the advent of recent advances in artificial intelligence (AI), this study attempts to answer the research question: How AI algorithms can expedite the civil engineering design process, enhancing efficiency and accuracy in reaching optimal solutions with fewer resources. The research employs a Markov decision process-based AI framework, integrating configuration design and refinement in a unified approach. The methodology begins with the Markov decision-making process to mathematically model the design process, followed by reinforcement learning for automatic design and refinement of solutions. Applied to a planar truss bridge design problem, the AI design agent produced feasible truss designs under various constraints efficiently, demonstrating superior capability and flexibility. The results indicate an average improvement of 12% in accuracy and 88% in computational efficiency over traditional methods. The meaning and significance of the results lie in the innovative integration of Markov decision-making and reinforcement learning into a unified two-stage design framework, significantly advancing the body of knowledge in civil engineering design automation. The speed and accuracy of the AI design agent validate the feasibility of the proposed model and highlight its potential in effectively solving complex civil engineering design problems. The directions for follow-up research are suggested to extend this framework to a wider array of design challenges and to refine the AI agent’s adaptability in more diverse design contexts.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, including reinforcement learning and genetic algorithm models.
Acknowledgments
This paper is based upon work supported by the National Science Foundation under Grant No. 2246298.
References
AASHTO. 2020. AASHTO LRFD bridge design specifications. Washington, DC: AASHTO.
Ampanavos, S., M. Nourbakhsh, and C. Y. Cheng. 2021. “Structural design recommendations in the early design phase using machine learning.” In Proc., Int. Conf. on Computer-Aided Architectural Design Futures, 190–202. Singapore: Springer.
Aytug, H., and G. J. Koehler. 2000. “New stopping criterion for genetic algorithms.” Eur. J. Oper. Res. 126 (3): 662–674. https://doi.org/10.1016/S0377-2217(99)00321-5.
Berquand, A., F. Murdaca, A. Riccardi, T. Soares, S. Gerené, N. Brauer, and K. Kumar. 2018. “Towards an artificial intelligence based design engineering assistant for the early design of space missions.” In Proc., 69th Int. Astronautical Congress. Paris: International Astronautical Federation.
Brown, N. C., V. Jusiega, and C. T. Mueller. 2020. “Implementing data-driven parametric building design with a flexible toolbox.” Autom. Constr. 118 (Oct): 103252. https://doi.org/10.1016/j.autcon.2020.103252.
Brown, N. C., and C. T. Mueller. 2017. “Automated performance-based design space simplification for parametric structural design.” In Vol. 2017 of Proc., IASS Annual Symposia, 1–10. Madrid, Spain: International Association for Shell and Spatial Structures.
Brütting, J., G. Senatore, and C. Fivet. 2022. “MILP-based discrete sizing and topology optimization of truss structures: New formulation and benchmarking.” Struct. Multidiscip. Optim. 65 (10): 277. https://doi.org/10.1007/s00158-022-03325-7.
Cabi, S., et al. 2019. “Scaling data-driven robotics with reward sketching and batch reinforcement learning.” Preprint, submitted September 26, 2019. https://arxiv.org/abs/1909.12200.
De Boissieu, A. 2021. “Introduction to computational design: Subsets, challenges in practice and emerging roles.” In Industry 4.0 for the built environment: Methodologies, technologies and skills, 55–75. Cham, Switzerland: Springer.
Duman, E., and M. H. Ozcelik. 2011. “Detecting credit card fraud by genetic algorithm and scatter search.” Expert Syst. Appl. 38 (10): 13057–13063. https://doi.org/10.1016/j.eswa.2011.04.110.
Fairclough, H., and M. Gilbert. 2020. “Layout optimization of simplified trusses using mixed integer linear programming with runtime generation of constraints.” Struct. Multidiscip. Optim. 61 (5): 1977–1999. https://doi.org/10.1007/s00158-019-02449-7.
Fairclough, H. E., L. He, T. J. Pritchard, and M. Gilbert. 2021. “LayOpt: An educational web-app for truss layout optimization.” Struct. Multidiscip. Optim. 64 (4): 2805–2823. https://doi.org/10.1007/s00158-021-03009-8.
Hayashi, K., and M. Ohsaki. 2020. “Reinforcement learning and graph embedding for binary truss topology optimization under stress and displacement constraints.” Front. Built Environ. 6 (Apr): 59. https://doi.org/10.3389/fbuil.2020.00059.
Hayashi, K., M. Ohsaki, and M. Kotera. 2022. “Assembly sequence optimization of spatial trusses using graph embedding and reinforcement learning.” J. Int. Assoc. Shell Spatial Struct. 63 (4): 232–240. https://doi.org/10.20898/j.iass.2022.016.
Holubar, M. S., and M. A. Wiering. 2020. “Continuous-action reinforcement learning for playing racing games: Comparing SPG to PPO.” Preprint, submitted January 15, 2020. https://arxiv.org/abs/2001.05270.
Hong, T., Z. Wang, X. Luo, and W. Zhang. 2020. “State-of-the-art on research and applications of machine learning in the building life cycle.” Energy Build. 212 (Apr): 109831. https://doi.org/10.1016/j.enbuild.2020.109831.
Ji, T. 2020. Structural design against deflection. Boca Raton, FL: CRC Press.
Kalyuzhnaya, A. V., N. O. Nikitin, A. Hvatov, M. Maslyaev, M. Yachmenkov, and A. Boukhanovsky. 2021. “Towards generative design of computationally efficient mathematical models with evolutionary learning.” Entropy 23 (1): 28. https://doi.org/10.3390/e23010028.
Karan, E., and S. Asadi. 2019. “Intelligent designer: A computational approach to automating design of windows in buildings.” Autom. Constr. 102 (Jun): 160–169. https://doi.org/10.1016/j.autcon.2019.02.019.
Kawamura, H., H. Ohmori, and N. Kito. 2002. “Truss topology optimization by a modified genetic algorithm.” Struct. Multidiscip. Optim. 23 (6): 467–473. https://doi.org/10.1007/s00158-002-0208-0.
Keshavarzi, M., C. Hotson, C. Y. Cheng, M. Nourbakhsh, M. Bergin, and M. Rahmani Asl. 2021. “Sketchopt: Sketch-based parametric model retrieval for generative design.” In Proc., Extended Abstracts of the 2021 CHI Conf. on Human Factors in Computing Systems, 1–6. New York: Association for Computing Machinery.
Law, M. V., A. Kwatra, N. Dhawan, M. Einhorn, A. Rajesh, and G. Hoffman. 2020. “Design intention inference for virtual co-design agents.” In Proc., 20th ACM Int. Conf. on Intelligent Virtual Agents, 1–8. New York: Association for Computing Machinery.
Liao, W., X. Lu, Y. Huang, Z. Zheng, and Y. Lin. 2021. “Automated structural design of shear wall residential buildings using generative adversarial networks.” Autom. Constr. 132 (Dec): 103931. https://doi.org/10.1016/j.autcon.2021.103931.
Luo, R., Y. Wang, Z. Liu, W. Xiao, and X. Zhao. 2022a. “A reinforcement learning method for layout design of planar and spatial trusses using kernel regression.” Appl. Sci. 12 (16): 8227. https://doi.org/10.3390/app12168227.
Luo, R., Y. Wang, W. Xiao, and X. Zhao. 2022b. “AlphaTruss: Monte Carlo tree search for optimal truss layout design.” Buildings 12 (5): 641. https://doi.org/10.3390/buildings12050641.
McKenna, F. 2011. “OpenSees: A framework for earthquake engineering simulation.” Comput. Sci. Eng. 13 (4): 58–66. https://doi.org/10.1109/MCSE.2011.66.
Miller, S. W., M. A. Yukish, and T. W. Simpson. 2018. “Design as a sequential decision process.” Struct. Multidiscip. Optim. 57 (1): 305–324. https://doi.org/10.1007/s00158-017-1756-7.
Mohr, F., and J. N. van Rijn. 2022. “Learning curves for decision making in supervised machine learning—A survey.” Preprint, submitted January 28, 2022. https://arxiv.org/abs/2201.12150.
Moniruzzaman, P. K. M., T. Biswas, A. F. Farah, and F. M. Omar. 2015. Space truss bridge optimization by dynamic programming and linear programming. Dhaka, Bangladesh: Bangladesh Group of IABSE.
Mukherjee, S., D. Lu, B. Raghavan, P. Breitkopf, S. Dutta, M. Xiao, and W. Zhang. 2021. “Accelerating large-scale topology optimization: State-of-the-art and challenges.” Arch. Comput. Methods Eng. 28 (7): 4549–4571. https://doi.org/10.1007/s11831-021-09544-3.
Nagy, D., D. Lau, J. Locke, J. Stoddart, L. Villaggi, R. Wang, D. Zhao, and D. Benjamin. 2017. “Project discover: An application of generative design for architectural space planning.” Accessed December 20, 2023. https://www.researchgate.net/profile/Karam-Al-Obaidi/publication/317089493_The_Thermal_Performance_Exploration_of_Outdoor_and_Indoor_Spaces_Using_IES_ENVI-met/links/597eb33c458515687b4998a3/The-Thermal-Performance-Exploration-of-Outdoor-and-Indoor-Spaces-Using-IES-ENVI-met.pdf#page=70.
Nauata, N., K. H. Chang, C. Y. Cheng, G. Mori, and Y. Furukawa. 2020. “House-GAN: Relational generative adversarial networks for graph-constrained house layout generation.” In Proc., European Conf. on Computer Vision, 162–177. Cham, Switzerland: Springer.
Nian, R., J. Liu, and B. Huang. 2020. “A review on reinforcement learning: Introduction and applications in industrial process control.” Comput. Chem. Eng. 139 (Aug): 106886. https://doi.org/10.1016/j.compchemeng.2020.106886.
Niknam, M., and S. Karshenas. 2017. “A shared ontology approach to semantic representation of BIM data.” Autom. Constr. 80 (Aug): 22–36. https://doi.org/10.1016/j.autcon.2017.03.013.
Ororbia, M. E., and G. P. Warn. 2022. “Design synthesis through a Markov decision process and reinforcement learning framework.” J. Comput. Inf. Sci. Eng. 22 (2): 021002. https://doi.org/10.1115/1.4051598.
Pan, Y., and L. Zhang. 2021. “Roles of artificial intelligence in construction engineering and management: A critical review and future trends.” Autom. Constr. 122 (Feb): 103517. https://doi.org/10.1016/j.autcon.2020.103517.
Raina, A., C. McComb, and J. Cagan. 2019. “Learning to design from humans: Imitating human designers through deep learning.” J. Mech. Des. 141 (11): 111102. https://doi.org/10.1115/1.4044256.
Rozvany, G. I. N. 2009. “A critical review of established methods of structural topology optimization.” Struct. Multidiscip. Optim. 37 (3): 217–237. https://doi.org/10.1007/s00158-007-0217-0.
Sahachaisaree, S., P. Sae-Ma, and P. Nanakorn. 2020. “Two-dimensional truss topology design by reinforcement learning.” In Proc., ICSCEA 2019: Proc., Int. Conf. on Sustainable Civil Engineering and Architecture, 1237–1245. Singapore: Springer.
Schulman, J., F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. 2017. “Proximal policy optimization algorithms.” Preprint, submitted July 20, 2017. https://arxiv.org/abs/1707.06347.
Sutton, R. S., and A. G. Barto. 2018. Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
Sutton, R. S., D. McAllester, S. Singh, and Y. Mansour. 1999. “Policy gradient methods for reinforcement learning with function approximation.” In Vol. 12 of Proc., Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.
Torii, A. J., R. H. Lopez, and F. Biondini. 2012. “An approach to reliability-based shape and topology optimization of truss structures.” Eng. Optim. 44 (1): 37–53. https://doi.org/10.1080/0305215X.2011.558578.
Xia, W., H. Li, and B. Li. 2016. “A control strategy of autonomous vehicles based on deep reinforcement learning.” In Vol. 2 of Proc., 2016 9th Int. Symp. on Computational Intelligence and Design (ISCID), 198–201. New York: IEEE.
Zegard, T., and G. H. Paulino. 2015. “GRAND3—Ground structure-based topology optimization for arbitrary 3D domains using MATLAB.” Struct. Multidiscip. Optim. 52 (6): 1161–1184. https://doi.org/10.1007/s00158-015-1284-2.
Zhan, Z.-H., L. Shi, K. C. Tan, and J. Zhang. 2022. “A survey on evolutionary computation for complex continuous optimization.” Artif. Intell. Rev. 55 (1): 59–110. https://doi.org/10.1007/s10462-021-10042-y.
Zhang, X. S., G. H. Paulino, and A. S. Ramos. 2018. “Multi-material topology optimization with multiple volume constraints: A general approach applied to ground structures with material nonlinearity.” Struct. Multidiscip. Optim. 57 (1): 161–182. https://doi.org/10.1007/s00158-017-1768-3.
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© 2024 American Society of Civil Engineers.
History
Received: Aug 19, 2023
Accepted: Feb 12, 2024
Published online: May 25, 2024
Published in print: Aug 1, 2024
Discussion open until: Oct 25, 2024
ASCE Technical Topics:
- Aging (material)
- Artificial intelligence and machine learning
- Automation and robotics
- Bridge design
- Business management
- Computer programming
- Computing in civil engineering
- Decision making
- Design (by type)
- Deterioration
- Engineering fundamentals
- Markov process
- Materials characterization
- Materials engineering
- Mathematics
- Practice and Profession
- Probability
- Stochastic processes
- Structural engineering
- Structural members
- Structural systems
- Systems engineering
- Trusses
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