Empirical exploration of predictive maintenance in concrete manufacturing: Harnessing machine learning for enhanced equipment reliability in construction project management

Abstract

Predictive Maintenance (PdM) in concrete manufacturing, particularly when viewed through construction project management, is increasingly recognized for its significance. The potential for PdM to predict structural inadequacies and systematically plan machinery maintenance is essential for seamless construction workflows. The primary focus of this investigation was to utilize the capabilities of machine learning (ML) algorithms to develop and validate sophisticated predictive models. These models aimed to proactively identify maintenance needs, thereby reducing unexpected downtimes and malfunctions in machinery vital to the concrete production cycle. Within this investigation, seven predominant ML classifiers –Logistic Regression (LR), Decision Tree (DT), Artificial Neural Networks (ANN), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost) and Categorical boosting (CatBoost) and Random Forest (RF) – were thoroughly evaluated. Their effectiveness was gauged using several metrics, including the F1-score, Accuracy, Recall, and the area under the Receiver Operating Characteristic (ROC) curve. Notably, the CatBoost surpassed its peers, achieving an F1-score of 0.985, an accuracy of 0.984, a Recall of 0.983, and an ROC curve area of 0.984. Additionally, certain indicators (i.e., 24-hour mean vibration, 24-hour mean pressure, Error3-count, and 24-hour mean volt) were identified as pivotal in predicting machinery failure within the concrete manufacturing framework. Insights from this empirical study further underscore the importance of embedding ML into construction project management methodologies. Insights from this empirical study further emphasize the importance of embedding ML into construction project management methodologies. Such integration enhances the precision of maintenance forecasts and bolsters equipment dependability, ensuring that the concrete manufacturing paradigm is both effective and optimally beneficial.