Early-Stage Prediction of Steel Weight in Industrial Buildings Using Neural Networks

In industrial building projects, steel is the main material used to create sturdy structures that have large open spaces without many columns in the center of the building. To estimate the cost of constructing a building before it enters the detailed design stage, engineers and stakeholders must have the right tools and guidelines. Steel is an important construction material used at high volumes in industrial buildings, and it plays a significant role in determining the total cost of a project. This study develops and evaluates an artificial neural network (ANN) model based on multilayer perceptron (MLP) to predict the weight of steel structures in industrial buildings. The data collected include actual projects from 180 industrial building projects, using parameters that influence the weight of steel. The findings show that the ANN method can accurately estimate the weight of steel at an early stage in the building project, even before the detailed design phase. It was found that ANN has the ability to predict the weight of steel for industrial buildings with an excellent degree of accuracy, with a coefficient of correlation (R2) of 94.85% and prediction accuracy (PA) of 94.23%. This indicates that the relationship between the independent and dependent variables of the developed models is good and the predicted values from the forecast model fit with the real-life data.