Prediction of Building Construction Project Cost Using Support Vector Machine

Document Type : Regular Article


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


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.


Main Subjects

[1]     Lin T, Yi T, Zhang C, Liu J. Intelligent prediction of the construction cost of substation projects using support vector machine optimized by particle swarm optimization. Math Probl Eng 2019;2019.
[2]     Juszczyk M. On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach. Buildings 2019;10:2.
[3]     Cheng M-Y, Peng H-S, Wu Y-W, Chen T-L. Estimate at Completion for construction projects using Evolutionary Support Vector Machine Inference Model. Autom Constr 2010;19:619–29.
[4]     Juszczyk M. Residential buildings conceptual cost estimates with the use of support vector regression. MATEC Web Conf 2018;196:04090.
[5]     Jaber FK, Al-Zwainy FMS, Hachem SW. Optimizing of predictive performance for construction projects utilizing support vector machine technique. Cogent Eng 2019;6.
[6]     Petruseva S, Sherrod P, Pancovska VZ, Petrovski A. Predicting bidding price in construction using support vector machine. Tem J 2016;5:143.
[7]     Petrusheva S, Car-Pušić D, Zileska-Pancovska V. Support Vector Machine Based Hybrid Model for Prediction of Road Structures Construction Costs. IOP Conf. Ser. Earth Environ. Sci., vol. 222, IOP Publishing; 2019, p. 12010.
[8]     Petruseva S, Zileska-Pancovska V, Zujo V. Predicting construction project duration with support vector machine. Int J Res Eng Technol 2013;11:12–24.
[9]     Hong T, Wang Z, Luo X, Zhang W. State-of-the-art on research and applications of machine learning in the building life cycle. Energy Build 2020;212:109831.
[10]   Yan K, Shi C. Prediction of elastic modulus of normal and high strength concrete by support vector machine. Constr Build Mater 2010;24:1479–85.
[11]   Patil MPA, Salunkhe MA. Comparative analysis of construction cost estimation using artificial neural networks. J Xidian Univ 2020;14:1287–305.
[12]   Cheng M-Y, Wu Y-W. Construction conceptual cost estimates using support vector machine. 22nd Int. Symp. Autom. Robot. Constr. ISARC, Citeseer; 2005, p. 1–5.
[13]   Qin Z, Lei X, Meng L. Research on forecasting the cost of residential construction based on PCA and LS-SVM. Proc. Int. Conf. Electron. Mech. Cult. Med. (EMCM 2015), 2016.
[14]   Vahdani B, Mousavi SM, Mousakhani M, Sharifi M, Hashemi H. A neural network model based on support vector machine for conceptual cost estimation in construction projects 2012.
[15]   Kim G-H, Shin J-M, Kim S, Shin Y. Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine. J Build Constr Plan Res 2013;01:1–7.
[16]   Meharie MG, Shaik N. Predicting highway construction costs: comparison of the performance of random forest, neural network and support vector machine models. J Soft Comput Civ Eng 2020;4:103–12.
[17]   Son H, Kim C, Kim C. Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project planning variables. Autom Constr 2012;27:60–6.
[18]   Wang Y-R, Yu C-Y, Chan H-H. Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models. Int J Proj Manag 2012;30:470–8.
[19]   V. K. Support Vector Machines – An Introduction, Auckland, New Zealand. Springer 2005.