Download PDFOpen PDF in browserInvestigation of Machine Learning for Clash Resolution Automation9 pages•Published: June 9, 2021AbstractVarious research work has recently investigated the utilization of Machine Learning for automating the process of clash resolution during design review and coordination of BIM models on construction projects. Literature review shows that current research work focuses on using Supervised Learning for automation of clash resolution. Individual implementation of Supervised Learning has its drawbacks. The automated model trained through Supervised Learning will only be able to resolve clashes similar in nature to those clashes used to train the model. This paper proposes a new approach that integrates Supervised and Reinforcement Learning to overcome these limitations. Reinforcement Learning will assist in overcoming the dependency of Supervised Learning on training data, while Supervised Learning will reduce the time for Reinforcement Learning by eliminating iteration with low rewards or illogical solution. The proposed approach will be able to assist industry practitioners in resolving clashes efficiently and effectively.Keyphrases: clash resolution, design coordination, machine learning, reinforcement learning, supervised learning In: Tom Leathem, Anthony Perrenoud and Wesley Collins (editors). ASC 2021. 57th Annual Associated Schools of Construction International Conference, vol 2, pages 228-236.
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