IoT-Enabled Real-Time Construction Project Monitoring and Predictive Management System for Schedule, Cost, and Resource Optimization
DOI:
https://doi.org/10.68104/ijasit.v1.i4.55Keywords:
Internet of Things (IoT), Construction 4.0, Predictive Analytics, Machine Learning, Building Information Modeling (BIM), Digital Twins, Earned Value Management (EVM), Project ControlsAbstract
In the construction industry, severe schedule and cost overruns, retrospective performance tracking, and fragmented communication are persistent problems. The Construction 4.0 paradigm has encouraged a trend toward data-driven, real-time project management that depends on the Internet of Things (IoT), artificial intelligence (AI), and building information modelling (BIM). This systematic evaluation, which focuses on multidimensional resource optimization, schedule, and cost, critically evaluates the use of IoT technology into construction project management. By combining content primarily released between 2019 and 2026, the analysis looks at how continuous sensor data transforms traditional, reactive management frameworks—such as Earned Value Management (EVM) and the Critical Path Method (CPM)—into proactive, predictive control systems. Evidence indicates that while individual sensor technologies and machine learning delay-prediction algorithms have reached a high degree of maturity, there are currently few comprehensive platforms connecting these various data silos. This paper outlines a comprehensive nine-layer conceptual framework for fully integrated predictive construction management, identifies major implementation challenges, and explains sensor architectures, predictive models, and digital twin integrations that are revolutionizing the sector.
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