Feasibility of an Integrated Heuristic and Machine Learning Approach for Schedule Health Monitoring in Construction
Publication: Construction Research Congress 2022
ABSTRACT
Project planning and controls requires planners to continuously revise project schedules to meet evolving requirements and constraints during a construction. Such an activity is usually performed under strict deadlines and planners are often forced to set aside good planning principles to deliver the updated schedules on time. To assist planners with validating their schedules, this paper explores the feasibility of using an integrated approach based on heuristics and machine learning methods to check the quality of a construction schedule. Specifically, building on the predefined rules and heuristics formulated in the Defense Contract Management Agency (DCMA)’s 14 Point Schedule Quality Assessment, this paper explores the feasibility of heuristic-based and deep learning methods to assess a project schedule health from qualitative and quantitative perspectives. Experimental results from thirty-five real-world projects are presented which demonstrate the feasibility of these underlying methods in highlighting schedule deviations from industry guidelines as well as following the best planning practices. A path forward toward a completely automated schedule health assessment system is discussed in detail.
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Construction Research Congress 2022
Pages: 351 - 360
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Published online: Mar 7, 2022
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