Abstract:

The construction industry has been under increasing pressure to improve maintenance practices and reduce equipment downtimes because of its reliance on both Planned and Corrective Maintenance. This trend is associated with the changes in the construction business model that demand greater asset dependability. As an answer to this issue, the combination of Machine Learning (ML) and the Internet of Things (IoT) has proven to be a radical solution for predictive maintenance, offering unparalleled advantages over traditional techniques based on historical records and intuition. This research examines the application that IoT and ML have concerning predictive maintenance in the construction industry, specifically looking into the prediction of asset failures, maintenance scheduling, and operational interference minimization. The use of IoT sensors on heavy machinery and structural components enables the collection of huge amounts of operational data, including but not limited to vibration, temperature, and usage cycles. Advanced ML algorithms powerful enough to recognize patterns or anomalies that suggest incipient failures can process this data. The ML-IoT framework was able to enhance accuracy in fault detection, mitigate unplanned outages, and generate savings on a number of active construction sites. Unplanned outages led to savings in expenditure. These experimental insights stemmed from a combination of exploratory case studies on active construction sites and pilot implementations mixed with construction benchmarking processes. These case studies provided valuable lessons on various data integration requirements, modeling precision, cross-competence, and specialization resource gaps that need to be filled in order to unlock multi-disciplinary synergies. The construction sector as a whole ensures that efficient system scaling, intelligent infrastructure management, and system digitization can be achieved on a project-to-project basis, shifting operational focus to the sequential deconstruction of robust predictive maintenance architectures. The research serves as a step forward towards systematizing construction processes and developing intelligent infrastructure management systems.