Formalizing Construction Sequencing Knowledge and Mining Company-Specific Best Practices from Past Project Schedules

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering 2019
Subtitle of host publicationVisualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
EditorsYong K. Cho, Fernanda Leite, Amir Behzadan, Chao Wang
PublisherAmerican Society of Civil Engineers
Pages215-223
Number of pages9
ISBN (Electronic)9780784482421
DOIs
StatePublished - Jan 1 2019
EventASCE International Conference on Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation, i3CE 2019 - Atlanta, United States
Duration: Jun 17 2019Jun 19 2019

Publication series

NameComputing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019

Conference

ConferenceASCE International Conference on Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation, i3CE 2019
CountryUnited States
CityAtlanta
Period6/17/196/19/19

ASJC Scopus subject areas

  • Computer Science(all)
  • Civil and Structural Engineering

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  • Cite this

    Amer, F., & Golparvar-Fard, M. (2019). Formalizing Construction Sequencing Knowledge and Mining Company-Specific Best Practices from Past Project Schedules. In Y. K. Cho, F. Leite, A. Behzadan, & C. Wang (Eds.), Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (pp. 215-223). (Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019). American Society of Civil Engineers. https://doi.org/10.1061/9780784482421.028