Cost Estimation Model for I-Girder Bridge Superstructure Using Multiple Linear Regression and Artificial Neural Network


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One of the owner’s common problem before executing construction projects is the complexity in estimating the project cost in an early stage. Inaccurate cost estimation will force the owner to make further arrangement to the project budget. This study aims to develop an initial cost estimation model for superstructures of Precast I-girder Bridge. Cost estimation model was developed based on thirteen data of detail engineering design of I-girder bridge in Daerah Istimewa Yogyakarta (DIY). Factors influencing the cost of the superstructures of the I-girder bridge were identified. Bridge span and width, the size of the sidewalk, and railing’s type are considered as variables affecting the cost of superstructures. These variables are then arranged into two different analysis Multiple Linear Regression (MLR) analysis and Artificial Neural Network (ANN), in order to obtain the best estimation model. The results of the analysis showed that bridge span and width were the significant factors influencing cost. The correlation value of bridge span is 89.0%, bridge width is 74.2%, the size of the sidewalk is 66.1%, and railing’s type is 46.1% as identified factors that affect the cost of the superstructure. A comparative model of two approaches shows that the ANN has better accuracy than that of MLR, although the difference was not significant.



Edited by:

Djoko Legono, Radianta Triatmaja, Prof. Priyosulistyo, Veerasak Likhitruangsilp, Lim Pang Zen, Teuku Faisal Fathani, Ali Awaludin, Intan Supraba, Imam Muthohar, Dr. Endita, Fikri Faris and Dr. Inggar Septhia Irawati




I. Winalytra et al., "Cost Estimation Model for I-Girder Bridge Superstructure Using Multiple Linear Regression and Artificial Neural Network", Applied Mechanics and Materials, Vol. 881, pp. 142-149, 2018

Online since:

May 2018




* - Corresponding Author

[1] H.S. Ji, M. Park, H.S. Lee, Data preprocessing based parametric cost model for building projects: Case studies of Korean construction projects, J. Constr. Eng. Manage. 136 (2010) 844-853.


[2] D.J. Lowe, M.W. Emsley, A. Harding, Predicting construction cost using multiple regression techniques, J. Constr. Eng. Manage. 132 (2006) 750-758.


[3] S. Hwang, Dynamic regression models for prediction of construction costs, J. Constr. Eng. Manage. 135 (2009) 360-367.


[4] M. Bouabaz, M. Hamami, A cost estimation model for repair bridges based on artificial neural network, American Journal of Applied Sciences. 5 (2008) 334-339.


[5] C.G. Wilmot, B. Mei, Neural network modeling of highway construction cost, J. Constr. Eng. Manage. 131(2005) 765-771.


[6] N. Fragkakis, S. Lambropoulos, J.P. Pantouvakis, A cost estimate method for bridge superstructures using regression analysis and bootstrap, An International Journal, Organization, Technology, and Management In Construction. 2 (2010) 183-191.

[7] D.A. Hollar, W. Rasdorf, M. Liu, J.E. Hummer, I. Arocho, S.M. Hsiang, Preliminary engineering cost estimation model for bridge projects, J. Constr. Eng. Manage. 139 (2013) 1259-1267.


[8] A.R. Muis, Preliminary Cost Estimation for Bridge Construction Implementation (in Indonesia), Master Thesis, Institut Teknologi Bandung, Bandung, (1995).

[9] G. Giwangkoro,Y. Latief, W. Isvara, Conceptual Cost Estimation for Fly Over Construction Using Artificial Neural Network (in Indonesia), Graduate Thesis, Universitas Indonesia, Jakarta, (2013).

[10] G.F. Sirca Jr, H. Adeli, Cost optimization of prestressed concrete bridges, J. Struct. Eng. 131(2005) 380-388.


[11] A. Hermawan, Artificial Neural Network (in Indonesia), ANDI, Yogyakarta, (2006).

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