Enhancing Construction Project Outcomes through Machine Learning and Real-Time Data Analytics: A Framework for Proactive Risk Management and Decision Support

Abstract

The construction industry faces ongoing challenges in managing risks and making timely decisions due to the inherent complexities and uncertainties of projects. This paper proposes a framework that leverages machine learning and real-time data analytics to enhance construction project outcomes through proactive risk management and decision support. By integrating advanced machine learning algorithms with real-time data collection and analysis, the framework aims to predict potential risks, optimize resource allocation, and support informed decision-making processes. The proposed approach also includes a real-time monitoring system to continuously track project progress and adjust strategies as needed. Case studies and simulations demonstrate the framework's effectiveness in improving project performance, reducing delays, and enhancing overall efficiency. The findings highlight the transformative potential of combining machine learning and real-time data analytics in construction project management, providing a robust solution for navigating project complexities and achieving better outcomes.