A data-driven and knowledge-based decision support system for optimized construction planning and control

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. • Formalize experts knowledge in project planning and control to build a knowledge repository. • Develop a data-driven and knowledge-based DSS to recommend planning and control systems based on project needs. • Demonstrate the proposed solution's practicality through a case study. • Propose a methodology for data collection, analysis, and implementation, of a data-driven and knowledge-based DSS. • Develop a multi-objective mathematical model to optimize the DSS's results.