Machine learning Applications for Automated Constructability Reviews

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by Kamolwan Lueprasert, Purdue Univ, West Lafayette, United States,
Mirosław J. Skibniewski, (M.ASCE), Purdue Univ, West Lafayette, United States,

Document Type: Proceeding Paper

Part of: Computing in Civil Engineering

Abstract: Knowledge acquisition of the project design and construction site conditions is essential for determining construction project complexity. An effective knowledge acquisition method is necessary to achieve this objective. Machine learning technology can be applied as a knowledge acquisition tool for this purpose. A preliminary test of the machine learning feasibility in Automated Constructability Review System has been conducted. It is concluded that the machine learning approach can lead to efficient constructability knowledge compilation in the form of rules. A preliminary test of feasibility is performed by using several constructability examples. The obtained constructability rules are evaluated to review the feasibility of the machine learning approach to the knowledge acquisition in this complex domain.

Subject Headings: Construction equipment | Artificial intelligence | Constructability | Data collection | Data processing | Construction sites | Feasibility studies |

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