Predictive Construction Equipment Maintenance Using Neural Networks

Construction equipment is subject to several types of breakdown throughout the project duration. The availability of operational data is crucial to understand the breakdown patterns and implement effective maintenance strategies. However, projects often operate with tight profit margins and limited resources; and therefore, access to such data is not readily available. The aim of this study is to establish a predictive maintenance framework based on machine learning (ML) that leverages historical breakdown data with the absence of information relating to the condition of the equipment and any output extracted from monitoring devices and sensors. The proposed approach entails developing and applying a multilayer perceptron (MLP) neural network to a real-life infrastructure project in the Middle East. The collected data include an equipment maintenance log database. The results show a significant improvement in accuracy compared to linear and non-linear models reported in the literature. The proposed framework helps enhancing the overall productivity of construction equipment by minimizing their breakdown rate, thereby reducing the associated operating costs.