Performance Analysis of Construction Cost Prediction Using Neural Network for Multioutput Regression

Abstract

In a construction project, construction cost estimation is very important, but construction costs are affected by various factors, so they are difficult to predict accurately. However, with the recent development of ANN technology, it has become possible to predict construction costs with consideration of various influencing factors. Unlike previous research cases, this study aimed to predict the total construction cost by predicting seven sub-construction costs using a multioutput regression model, not by predicting a single total construction cost. In addition, analysis of the change in construction cost prediction performance was conducted by scaling and regularization. We estimated the error rate of predicting construction costs through sub-construction cost prediction to be 16.80%, a level similar to that of the total construction cost prediction error rate of 17.67%. This study shows that the construction cost can be calculated by predicting detailed cost factors at once, and it is expected that various types of construction costs or partial construction costs can be predicted using the predicted detailed cost elements. As a result of predicting several sub-construction costs using multioutput-based ANN, it was found that the prediction error rate varies depending on the type of construction. To improve accuracy, it is necessary to supplement influencing factors suitable for the construction features.