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

Abstract

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.