Construction and Demolition Waste Generation Prediction by Using Artificial Neural Networks and Metaheuristic Algorithms

Abstract

In the actual estimation of construction and demolition waste (C&DW), it is significantly relevant to effective management, design, and planning at project stages, but the lack of reliable estimation methods and historical data prevents the estimation of C&DW quantities for both short- and long-term planning. To address this gap, this study aims to predict C&DW quantities in construction projects more accurately by integrating the gray wolf optimization algorithm (GWO) and the Archimedes optimization algorithm (AOA) into an artificial neural network (ANN). This study uses data concerning the actual quantities of work in 200 real-life construction and demolition projects performed in the Gaza Strip. Different performance parameters, such as mean absolute error (MAE), mean square error (MSE), root mean squared error (RMSE), and the coefficient of determination (R2), are used to evaluate the effectiveness of the models developed. The results of this study have shown that the AOA-ANN model outperforms the other models in terms of accuracy (R2 = 0.023728, MSE = 0.00056304, RMSE = 0.023728, MAE = 0.0086648). Moreover, this new hybrid model yields more accurate estimations of C&DW quantities with minimal input parameters, making the process of estimation more feasible.