Comparative study of artificial neural networks and multiple regression analysis for predicting hoisting times of tower cranes

This paper aims to develop a quantitative model for predicting the hoisting times of tower cranes for public housing construction using artificial neural network and multiple regression analysis. Firstly, based on data collected from crane operators and site managers in seven construction sites, the basic factors affecting the hoisting times for tower cranes are identified. Then, artificial neural networks (ANN) and the multiple regression analysis (MRA) are used to model the hoisting time, and from the results, the neural network model and the multiple regression model of hoisting time are established. The modeling methods and procedures are explained. These two kinds of models are then verified by data obtained from an independent site, and the predictive behaviors of the two kinds of models are compared and analyzed. Furthermore, the predictive behaviors of the neural network model are also investigated by a sensitivity analysis. Finally, the modeling methods, predictive behaviors and the advantages of each model are discussed.