TY - GEN
T1 - Formalizing Construction Sequencing Knowledge and Mining Company-Specific Best Practices from Past Project Schedules
AU - Amer, Fouad
AU - Golparvar-Fard, Mani
N1 - Funding Information:
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.
Publisher Copyright:
© 2019 American Society of Civil Engineers.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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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U2 - 10.1061/9780784482421.028
DO - 10.1061/9780784482421.028
M3 - Conference contribution
AN - SCOPUS:85068803606
T3 - Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
SP - 215
EP - 223
BT - Computing in Civil Engineering 2019
A2 - Cho, Yong K.
A2 - Leite, Fernanda
A2 - Behzadan, Amir
A2 - Wang, Chao
PB - American Society of Civil Engineers
T2 - ASCE International Conference on Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation, i3CE 2019
Y2 - 17 June 2019 through 19 June 2019
ER -