An Integrated Supervised Reinforcement Machine Learning Approach for Automated Clash Resolution
Publication: Construction Research Congress 2022
ABSTRACT
During design coordination, identified relevant clashes are discussed in detail, and design changes and modifications are made to resolve the clashes prior to the construction. Currently, clash resolution is a slow manual process. Recent research focused on using supervised machine learning to automate the clash resolution process shows potential results to improve the efficiency and effectiveness of clash resolution. However, the model trained using supervised learning is limited in its effectiveness by the quality of training data provided. To overcome this limitation, the paper proposes a machine learning method that integrates supervised and reinforcement learning. In the proposed model, supervised learning will be used to establish the initial relationship between the clash information and the clash resolution decision. This relationship will act as pre-training for reinforcement learning, which will improve the relationship with subsequent iterations of the learning process, generating a more effective clash resolution policy than the initial relationship.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Akponeware, A. O., and Adamu, Z. A. (2017). “Clash detection or clash avoidance? An investigation into coordination problems in 3D BIM.” Buildings, 7(3), 75.
Arantes, A., Silva, P. F. D., and Ferreira, L. M. D. F. “Delays in construction projects - causes and impacts.” Proc., 2015 International Conference on Industrial Engineering and Systems Management (IESM), 1105–1110.
Hsu, H.-C., Chang, S., Chen, C.-C., and Wu, I. C. (2020). “Knowledge-based system for resolving design clashes in building information models.” Automation in Construction, 110, 103001.
Hu, Y., and Castro-Lacouture, D. (2019). “Clash relevance prediction based on machine learning.” Journal of computing in civil engineering, 33(2), 04018060.
Hu, Y., Castro-Lacouture, D., Eastman, C. M., and Navathe, S. B. (2020). “Automatic clash correction sequence optimization using a clash dependency network.” Automation in Construction, 115, 103205.
Huang, Y., and Lin, W. Y. “Automatic Classification of Design Conflicts Using Rulebased Reasoning and Machine Learning-An Example of Structural Clashes Against the MEP Model.” IAARC Publications, 324–331.
Kangin, D., and Pugeault, N. Continuous Control with a Combination of Supervised and Reinforcement Learning. IEEE, 1–8.
Lee, G., and Kim, J. W. (2014). “Parallel vs. Sequential Cascading MEP Coordination Strategies: A Pharmaceutical Building Case Study.” Automation in Construction, 43, 170–179.
Liu, J., Liu, P., Feng, L., Wu, W., Li, D., and Chen, Y. F. (2020). “Automated clash resolution for reinforcement steel design in concrete frames via Q-learning and Building Information Modeling.” Automation in Construction, 112, 103062.
Sutton, R. S., and Barto, A. G. (2018). Reinforcement learning: An introduction, MIT press.
Theobald, O. (2017). Machine learning for absolute beginners: a plain English introduction, Scatterplot press.
Tommelein, I. D., and Gholami, S. “Root causes of clashes in building information models.” Proc., Proceedings for the 20th Annual Conference of the International Group for Lean Construction, IGLC San Diego, LA, 10.
Information & Authors
Information
Published In
History
Published online: Mar 7, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.