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

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