Artificial neural network predictive modelling for on-site reinforced concrete column production rate

Abstract

This research presents the development and evaluation of an artificial neural network (ANN) predictive model for on-site reinforced concrete column production rate. The goal of this study was to explore the potential of using ANN modelling techniques to accurately predict the rate of concrete column production, which is a critical aspect of construction planning and scheduling. The ANN model was trained using a dataset consisting of various input variables such as column size, shape, weather, temperature, and crew size, along with corresponding production rates. The results of the model were compared with actual production data to evaluate its accuracy and performance. The findings demonstrate that the developed ANN model can effectively predict the production rate of reinforced concrete columns with a high level of accuracy. The outcomes of this study have significant implications for construction industry professionals, as they can use the developed model to optimize production scheduling and improve the overall efficiency of on-site reinforced concrete column production.