Prediction of Building Construction Project Cost Using Support Vector Machine

Document Type : Regular Article

Authors

1 Ph.D. Student, Department of Civil Engineering, Sardar Patel College of Engineering, Andheri, Mumbai 400058, Maharashtra, India

2 Associate Professor, Department of Civil Engineering, Sardar Patel College of Engineering, Andheri, Mumbai 400058, Maharashtra, India

10.22115/iesm.2021.297399.1015

Abstract

Assessing construction costs with a more noteworthy level of accuracy during the early phase of construction is a basic element in building construction projects. This paper aims to develop a prediction model for building construction projects cost in India utilizing support vector machine (SVM) analysis. Complete 78 datasets of building construction projects were gathered from the Mumbai region of India for the development of the cost predictive model. The linear, Radial Basis Function (RBF), Polynomial, and sigmoid kernel function are applied for the development of the SVM model. The results of the developed Epsilon-SVR RBF kernel function-based SVM model show that better performance over the other models. The straight connection between actual construction cost and the predicted cost was likewise characterized by the coefficient of determination (R2), which was 94.29% along with the lowest error criteria. This research benefits the Indian construction industry by giving a general idea about the project cost prediction that will be useful to financial backers.

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