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

He, Jie, Qi, Zhiguo, Hang, Wen, Zhao, Chihang, & King, Mark J. (2014) Predicting freeway pavement construction cost using a back-propagation neural network : a case study in Henan, China. The Baltic Journal of Road and Bridge Engineering, 9(1), pp. 66-76.

View at publisher


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.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 70244
Item Type: Journal Article
Refereed: Yes
Keywords: Pavement, Construction cost, Neural network, Predicting model, Matlab
DOI: 10.3846/bjrbe.2014.09
ISSN: 1822-4288
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200) > Applied Mathematics not elsewhere classified (010299)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Infrastructure Engineering and Asset Management (090505)
Divisions: Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2014 Vilnius Gediminas Technical University (VGTU) Press Technika
Deposited On: 16 Apr 2014 01:13
Last Modified: 18 May 2015 02:52

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page