A Predictive Analytics Framework for Mobile Crane Configuration Selection in Heavy Industrial Construction Projects

Abstract

Predictive analytics have been used to improve efficiency and productivity in the construction industry by leveraging the insights from historical data with a variety of applications in project management. In the planning process of heavy industrial construction projects, mobile crane selection plays a critical role in the project’s success, and poor choice of mobile crane configurations can lead to unnecessary cost-overrun and delayed schedules. In this research, the authors propose a predictive analytics framework for crane configuration selection using combined heuristic search and artificial neural network (ANN) approaches for heavy industrial construction projects. The heuristic search allows the practitioners to select the crane configurations based on engineering rules, while the ANN model utilizes the historical project data to help select crane configurations. The K-fold cross-validation is conducted to validate the designed ANN model and improve the accuracy of predictions. The results from the cross-validation test set have shown 70% accuracy.