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Research on Reference Indicators for Sustainable Pavement Maintenance Cost Control through Data Mining

1
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China
2
State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510640, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(3), 877; https://doi.org/10.3390/su11030877
Received: 23 January 2019 / Accepted: 30 January 2019 / Published: 8 February 2019
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Abstract

Maintenance management has become increasingly important in the development of highways and government investment, but the shortage of funds is still a serious problem. When the administrative department reviews expense, the existing evaluation methodology cannot be applied to the current national condition and its calculation process is too complicated. Therefore, in order to improve this situation, this paper analyses various factors affecting maintenance costs, and obtains the quantitative relationship between the six main influencing factors such as traffic volume, using time, location, the number of lanes, overlays, and major rehabilitation. Based on regression analysis, an accuracy-based and cost-oriented control methodology is proposed, which can be dynamically updated according to the market conditions. This method is built on the data of 18 typical highways in Guangdong Province, China. The control reference indicators consist of a set of models and confidence intervals, and the actual cost needs to meet the corresponding requirements. In addition, the expenditure characteristics of rehabilitation and reconstruction in China are summarized. Experiments showed that this methodology can be used to guide cost planning and capital allocation in sustainable maintenance and achieved good results in application, making it worthwhile to promote them in other areas. View Full-Text
Keywords: pavement maintenance management; routine maintenance; cost control; factors analysis; data mining pavement maintenance management; routine maintenance; cost control; factors analysis; data mining
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Yang, Y.; Huang, L.; Wang, J.; Xia, Y. Research on Reference Indicators for Sustainable Pavement Maintenance Cost Control through Data Mining. Sustainability 2019, 11, 877.

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