Sheikhkhoshkar, Moslem ORCID: https://orcid.org/0000-0001-9067-2705, El-Haouzi, Hind Bril, Aubry, Alexis, Hamzeh, Farook and Rahimian, Farzad (2025) A data-driven and knowledge-based decision support system for optimized construction planning and control. Automation in Construction, 173:106066. ISSN 0926-5805 (Print), 1872-7891 (Online) (doi:10.1016/j.autcon.2025.106066)

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Abstract

Despite the use of various construction planning and control systems, no prior data-driven and knowledge-based system provides optimized solutions based on specific project team needs and applications. This paper presents a data-driven and knowledge-based decision support system that utilizes a knowledge database constructed from experts' experience and proposes multi-level and integrated systems for planning and control of construction projects. A mixed-method approach gathers data from industry professionals, develops a knowledge repository based on Rough Set Theory (RST), launches an inference engine using the Pyke package, and integrates these insights into a decision support system optimized by a multi-objective mathematical model. The developed system considers the functional requirements of the project team and suggests an optimized and fit-for-purpose planning and control system. To demonstrate its practicality, it applies to a real-world renovation project. This paper contributes to enhancing systematic and data-driven decision-making for planning and control systems based on expert knowledge and the specific needs of the project team.

Item Type:	Article
Uncontrolled Keywords:	decision support system, planning and control system, rough set theory, knowledge repository, mathematical model
Subjects:	Q Science > QA Mathematics T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	17 Mar 2025 13:01
URI:	http://gala.gre.ac.uk/id/eprint/49952

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