Machine Learning based Material Demand Prediction of Construction Equipment for Maintenance

Abstract

Construction managers faced the Construction Equipment (CE) challenges related to running repair and replacement of spare part materials as well as shortage of materials, sudden damage of spare parts and unavailability of necessary materials at job sites frequently. Regular follow up and track of materials availability and their usage at each stage of requirement phase becomes essential. This study presents Machine Learning (ML) based material demand prediction. Training of ML models utilizes historical maintenance, and procurement periodic data related to materials of the CE. This study highlights the use of Multiple Linear Regression (MLR), Support Vector Regression (SVR), Decision Tree (DT) Regressor and ensemble boosting models as Random Forest (RF) Regressor and Gradient Bosting Regressor (GBR). According to the performance measurement of each model, RF performs better and is used for prediction. Material demand prediction helps in maintenance and operational planning of CE. Subsequently, approach assists in addressing issues early by involving operators and site owners, enabling preventive actions to be taken before the scheduled procurement process. This study addresses the corrective measurement of the model using periodic data. The model performance results indicate that early prediction of maintenance costs based on the quantity of essential materials withdrawn from demand is helpful for budgeting expenditures.