Estimate final cost of roads using support vector machine

• Autorzy: Hasan Musaab Falih , Hammody Oday , Albayati Khaldoon Satea

Identyfikatory

DOI

10.24425/ace.2022.143061

• Języki publikacji: EN

Abstrakty

EN

The cost overrun in road construction projects in Iraq is one of the major problems that face the construction of new roads. To enable the concerned government agencies to predict the final cost of roads, the objective this paper suggested is to develop an early cost estimating model for road projects using a support vector machine based on (43) sets of bills of quantity collected in Baghdad city in Iraq. As cost estimates are required at the early stages of a project, consideration was given to the fact that the input data for the support vector machine model could be easily extracted from sketches or the project's scope definition. The data were collected from contracts awarded by the Mayoralty of Baghdad for completed projects between 2010-2013. Mathematical equations were constructed using the Support Vector Machine Algorithm (SMO) technique. An average of accuracy (AA) (99.65%) and coefficient of determination (R2) (97.63%) for the model was achieved by the created prediction equations.

Słowa kluczowe

EN

cost estimate; road; support vector machine

PL

szacowanie kosztów; droga; maszyna wektorów nośnych

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2022
• Tom: Vol. 68, nr 4
• Strony: 669--682
• Opis fizyczny: Bibliogr. 16 poz., il., tab.

Twórcy

autor

Hasan Musaab Falih

• General Directorate of Education Baghdad Rusafa First, Ministry of Education, Iraq

• https://orcid.org/0000-0001-7036-0106

autor

Hammody Oday

• Civil Engineering Department, University of Technology, Baghdad, Iraq

• https://orcid.org/0000-0001-5002-3882

autor

Albayati Khaldoon Satea

• Iraqi Reinsurance Company, Ministry of Finance, Iraq

• https://orcid.org/0000-0002-7317-6264

Bibliografia

• [1] M.Y. Cheng, Y.W. Wu, “Construction conceptual cost estimates using support vector machine”, in 22nd International Symposium on Automation and Robotics in Construction ISARC’05, Ferrara, Italy, 2005, pp. 1-5.
• [2] K. Yan, C. Shi, “Prediction of elastic modulus of normal and high strength concrete by support vector machine”, Construction and Building Materials, 2010, vol. 24, no. 8, pp. 1479-1485; DOI: 10.1016/j.conbuildmat.2010.01.006.
• [3] T. Mahfouz, “A productivity decision support system for construction projects through machine learning (ml)”, in Proceedings of the CIB W78 2012: 29th International Conference-Beirut, Lebanon, 17-19 October, 2012.
• [4] Y.R. Wang, C.Y. Yu, H.H. Chan, “Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models”, International Journal of Project Management, 2012, vol. 30, no. 4, pp. 470-478; DOI: 10.1016/j.ijproman.2011.09.002.
• [5] S. Petruseva, V. Zileska-Pancovska, V. Zujo, “Predicting construction project duration with support vector machine”, International Journal of Research in Engineering Technology, 2013, vol. 11, no. 2, pp. 12-24.
• [6] M. Wauters, M. Vanhoucke, “Support vector machine regression for project control forecasting”, Automation in Construction, 2014, vol. 47, pp. 92-106; DOI: 10.1016/j.autcon.2014.07.014.
• [7] N.I. El-Sawalhi, “Support vector machine cost estimation model for road projects”, Journal of Civil Engineering Architecture, 2015, vol. 9, pp. 1115-1125.
• [8] F.K. Jaber, F.M. Al-Zwainy, S.W. Hachem, “Optimizing of predictive performance for construction projects utilizing support vector machine technique”, Cogent Engineering, 2019, vol. 6, no. 1, pp. 1-13; DOI: 10.1080/23311916.2019.1685860.
• [9] C.F. Lin, S.D. Wang, “Fuzzy support vector machines”, IEEE Transactions on Neural Networks, 2002, vol. 13, no. 2, pp. 464-471; DOI: 10.1109/72.991432.
• [10] K. Gopalakrishnan, S. Kim, “Support vector machines approach to HMA stiffness prediction”, Journal of Engineering Mechanics, 2011, vol. 137, no. 2, pp. 138-146; DOI: 10.1061/(ASCE)EM.1943-7889.0000214.
• [11] N. Tabatabaee, M. Ziyadi, and Y. Shafahi, “Two-stage support vector classifier and recurrent neural network predictor for pavement performance modeling”, Journal of Infrastructure Systems, vol. 19, no. 3, pp. 266-274, 2013; DOI: 10.1061/(ASCE)IS.1943-555X.0000132.
• [12] V. Kecman, “Learning and soft computing: support vector machines, neural networks, and fuzzy logic models”, MIT press, 2001.
• [13] Y. Shi, Y. Tian, G. Kou, Y. Peng, and J. Li, “Optimization based data mining: theory and applications”, Springer Science & Business Media, 2011.
• [14] C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines”, ACM transactions on intelligent systems technology, vol. 2, no. 3, pp. 1-27, 2011; DOI: 10.1145/1961189.1961199.
• [15] I. Peško, M. Trivunić, G. Cirović, and V. Mučenski, “A preliminary estimate of time and cost in urban road construction using neural networks”, Tehnički vjesnik, vol. 20, no. 3, pp. 563-570, 2013.
• [16] Z. S. Khaled, R. S. A. Ali, and M. F. Hassan, “Predicting the Delivery Time of Public School Building Projects Using Nonlinear Regression”, Eng. &Tech.Journal, vol. 34, no. 8, pp. 1538-1548, 2016.