Leveraging Artificial Intelligence for Predictive and Adaptive Decision-Making in Construction Project Management

Abstract

The construction sector is characterized by systematic problems of project delays, cost, safety hazards, as well as inefficiencies in decision making processes. The classical methods of project management fail to react to new circumstances and become more complex in reaction to the constantly evolving construction projects. This paper discusses how artificial intelligence (AI) can be used to help in improving predictive and adaptive decision-making within the construction project management. Using data-led models and incorporating real-time analytics, AI allows project managers to be proactive regarding the prospect of risks, control costs and timeframes and adjust resource allocation dynamically. The study focuses on how machine learning, predictive analytics, and reinforcement learning have been applied to enhance the accuracy of planning, performance monitoring, and responsiveness of operation. A conceptual framework is created that allows showing how AI systems are capable of learning on the basis of the data stream on an ongoing project to support proactive and adaptive management strategies. Results indicate the transformative AI potential to diminish uncertainty, enhance efficiency, and maximize project results, whereas indicating the implementation issues of data integration, model interpretability, and organizational readiness. The research paper is one step towards the improvement of intelligent decision-support systems in the construction industry, which encourages a transition toward more information-intensive and adaptive and resilient project management practices.