AI-BASED EARLY WARNING SYSTEMS FOR PREDICTING CONSTRUCTION PROJECT SCHEDULE DELAYS

Abstract 

Construction projects all over the world are still suffering from serious schedule delays due to the complexity of operations, uncertainties in resources, and dynamic site conditions. Traditional identification methods for delay risks include CPM, PERT, and Earned Value Management, most of which detect risks only after major disruptions have occurred, leading to cost overruns and losses in productivity. Artificial Intelligence, in turn, provides a disruptive way to build Early Warning Systems that can predict such schedule delays before they become critical. Based on this, this study is undertaken to explore the design and implementation of an AI-driven Early Warning System in forecasting construction project delays using machine learning algorithms and real-time project data.It integrates crucial delay-influencing factors, namely labor productivity, material availability, equipment utilization, weather conditions, contractor performance, and financial risks, into the predictive models. It evaluates different machine learning techniques, such as Random Forest, Artificial Neural Networks (ANN), Gradient Boosting, and Long Short-Term Memory networks (LSTM), in determining the most accurate algorithm for delay prediction. The results clearly indicate that AI models outperform traditional analytical methods by providing timely, data-driven insights and generating predictive alerts to help project managers take necessary preventive actions. This AI-based EWS framework has improved decision-making through continuous monitoring of project variables to identify risk patterns and issue early warnings if detected with any deviation from the schedule baseline.This research has contributed to the literature on construction management with a comprehensive model that integrates historical project data, real-time monitoring, and AI analytics. The results substantiate the potential of AI-powered warning systems in leading to improvements in the reliability of schedules, reduction of delays, optimization of resources, and enhancement of overall project efficiency. Future research may expand the model by incorporating BIM integration, digital twins, and IoT-based sensor data for more advanced real-time forecasting capabilities.