Data-Driven Machine Learning Approach to Integrate Field Submittals in Project Scheduling

AbstractConstruction projects are data-rich environments. However, those data are usually captured for site-specific reasons, e.g., the filing and approval of inspection requests, with little regard to how they can be leveraged for improved project management. Typically, scheduling techniques rely o...

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Bibliographic Details
Published in:Journal of management in engineering 2021-01, Vol.37 (1)
Main Authors: Awada, Mohamad, Srour, F. Jordan, Srour, Issam M
Format: Article
Language:English
Subjects:
Construction sites
Critical path
Critical path method
Data transmission
Inspection
Machine learning
Prediction models
Project management
Schedules
Scheduling
Statistical analysis
Technical Papers
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Summary:AbstractConstruction projects are data-rich environments. However, those data are usually captured for site-specific reasons, e.g., the filing and approval of inspection requests, with little regard to how they can be leveraged for improved project management. Typically, scheduling techniques rely on general probability estimates, which do not capture the details of the site processes causing schedule deviations. This paper illustrates how machine learning techniques can mine project data to forecast delay in the midst of the project. The proposed method uses concrete pouring requests as an example of a site data stream and implements a random forest predictive model to forecast the likelihood of acceptance for these requests. Embedded in the proposed approach is an analysis that allows for the addition of probabilistic time delays associated with the forecast of rejected requests. The methodology was tested on a real-world case study, allowing for the comparison between a project duration estimate based on critical path method (CPM) with static buffers and a project duration obtained using the proposed method. The results show a difference of 10% between the two durations. The paper shows how using data streams from a construction site with machine learning techniques can enhance project duration estimates in execution.
ISSN:0742-597X
1943-5479
DOI:10.1061/(ASCE)ME.1943-5479.0000873