ASCE International Conference on Computing in Civil Engineering 2019
Formalizing Construction Sequencing Knowledge and Mining Company-Specific Best Practices from Past Project Schedules
Publication: Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation
ABSTRACT
In this paper, we present a machine-learning based method that allows company-specific construction knowledge to be automatically learned from past project schedules and weekly work plans without the need for any manual human input. The proposed model is built using long short-term memory recurrent neural networks (LSTM-RNNs) and is trained on construction sequences extracted from previous project schedules. While training, the model learns the likelihoods of different successor alternatives given a sequence of previous schedule activities. Experimental results on 12 real-world schedules show accurate and consistent predictions of potential future activities at various stages of construction. Results also demonstrate the method’s ability to formalize sequencing logic and mine what we call dynamic means and methods templates (DMMTs) from previous projects. When used as the engine for a project controls system, this solution has potential to automatically generate schedules using work templates; validate the correctness in the logic of an existing schedule; and revise look-ahead schedules.
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ACKNOWLEDGEMENT
This work was supported by the National Science Foundation through grant CMMI-1446765. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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Information & Authors
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Published In
Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation
Pages: 215 - 223
Editors: Yong K. Cho, Ph.D., Georgia Institute of Technology, Fernanda Leite, Ph.D., University of Texas at Austin, Amir Behzadan, Ph.D., Texas A&M University, and Chao Wang, Ph.D., Louisiana State University
ISBN (Online): 978-0-7844-8242-1
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© 2019 American Society of Civil Engineers.
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Published online: Jun 13, 2019
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