Roads Cost Estimation: Comparison of the Accurateness of MLP and Linear SVR

Abstract

Cost estimation in the early stages of construction projects is one of the crucial problems of project sustainability because costs are an integral component of any construction project contract; the completion of a project can be affected by the accuracy with which construction costs dose projected. Various machine learning algorithms were employed for estimate purposes, but neither of the techniques can be considered the best for all circumstances. This research used actual project data for rural roads in Iraq to predict the target variable actual construction cost of road structures based on machine learning techniques. For more accurate cost value two algorithms were compared: the linear support vector regression (SVR) model and Multilayer Perceptron Neural Network (MLP). The highest accuracy has been obtained with linear SVR model. The result R2=0.999 about (100 %), and MAPE=0.00001 shows excellent predictive capabilities of the SVR, regarding that these results are for real problems from the practice. When the outcomes of the models were compared, it was discovered that forecasting with SVR was much more accurate.

Keywords

• Construction, Roads, Cost estimation, Machine learning, Linear SVR