Unveiling the Chaos: Automated Monitoring for Enhanced Construction Project Performance Prediction

Construction projects frequently suffer from delays, cost overruns, and quality defects, often due to the limitations of traditional, manual monitoring methods. This research presents an automated monitoring system leveraging machine learning and IoT devices to predict project performance. By continuously collecting and analyzing real-time data, the system enables early issue identification and timely intervention. Results demonstrate significantly improved prediction accuracy and timeliness compared to traditional approaches. This study highlights the transformative potential of automated monitoring for construction project management, offering reduced delays and cost overruns and enhanced decision-making. Key challenges for implementation, including data quality and technology adoption, are also discussed.

کلیدواژه ها:

Construction Project Performance ، Automated Monitoring System ، Machine Learning ، Real-time Data Analysis ، System Dynamics Modeling