Employing IOT and Machine Learning Together to Improve Constructive's Predictive Maintenance

Abstract

A vital component of guaranteeing the effectiveness and economy of construction operations is predictive maintenance. This study investigates how machine learning and the Internet of Things (IoT) could change predictive maintenance in the construction sector. This method enables precise equipment failure and maintenance forecasting by utilizing real-time data from sensors and machine learning algorithms. This paper outlines the potential of utilizing the Internet of Things (IoT) and Machine Learning (ML) to improve a predictive maintenance program for construction sites. The combination of an IoT system, consisting of connected sensors, and algorithms developed through ML can lead to significant improvements in 78the maintenance of construction sites and better outcomes for construction professionals and developers. The use of an IoT and ML framework provides visibility to the various operational factors of a construction project and then applies various models to predict and provide alerts on potential issues. This combination of technologies enables construction professionals to mitigate risks, increase productivity, and lower operational costs associated with maintenance. Furthermore, it provides unprecedented insights into identifying potential sources of problems that would otherwise be difficult to discern. The paper also considers the potential of the technology’s ability to autonomously make decisions and even possibly self-heal. The paper concludes with a discussion of the probable future applications of the combined technologies.