Effective Modeling for Construction Activities of Recycled Aggregate Concrete Using Artificial Neural Network

Abstract

Recycled aggregate concrete (RAC) technology is broadly adopted in the construction industry. However, such technology tends to promisingly be implemented only in countries with developed economies, leaving behind countries with emerging economies. To increase the utilization of RAC in these emerging economy countries, this research program aims to investigate the applicability of using the artificial neural network (ANN) technique to predict onsite construction activities of RAC. The construction activities are modeled for the 909 dataset, which includes costs, concrete volume for construction, and total construction time. The results indicate that the mean absolute percentage error values of the RAC cost, concrete volume for construction, and total construction time (including recycled aggregate production and RAC production processes) are 1.98, 28.21, and 2.96, respectively. The mean squared error values of RAC cost, concrete volume for construction, and total construction time are 56,979, 20.9, and 0.56, respectively. Moreover, the coefficient of determination (R2) of RAC cost, concrete volume, and construction time of concrete were calculated at 0.999, 0.976, and 0.968, respectively. Both statistical values indicate that the ANN modeling technique is well implemented for constructing RAC. The results also indicate that ANN modeling can be effectively used in time series for predicting the construction activities of making RAC products. The outcomes offer benefits to stakeholders in construction activities, including improved cost estimations, reduced waste from less concrete going unused, and more accurate project scheduling. ANN modeling represents a relatively simple prediction and can be adopted in preconstruction stages, such as project planning and investment decision making, leading to sustainable construction.