Jie He, Zhiguo Qi, Wen Hang, Chihang Zhao, Mark King - Predicting freeway pavement construction cost using a back-propagation neural network: a case study in Henan, China (Vol IX, No 1)

Vilnius Gediminas Technical UniversityRiga Technical UniversityTallinn University of TechnologyBaltic Road Association

 

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2015 Impact Factor: 0.519 ©2015 Thomson Reuters, 2015 Journal Citation Report®

ISSN 1822-427X print
ISSN 1822-4288 online
 

 

 
 

"The Baltic Journal of Road and Bridge Engineering"
Vilnius: Technika, 2014, Vol IX, No 1, p. 66-76


Jie He, Zhiguo Qi, Wen Hang, Chihang Zhao, Mark King

Predicting freeway pavement construction cost using a back-propagation neural network: a case study in Henan, China

DOI: 10.3846/bjrbe.2014.09
 
The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.
 
Keywords: pavement, construction cost, Neural Network, predicting model, Matlab.

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