Intelligent decision model of road maintenance based on improved weight random forest algorithm

ABSTRACT

The routine maintenance and rehabilitation of road pavement is vital to keep up with the service level and bearing capacity. However, the current problem is that, with the continuous increase of the scale of maintenance data, the traditional decision-making cannot satisfy the requirements in terms of accuracy and efficiency. The objectives of this paper were to improve the accuracy and efficiency of the maintenance decisions, overcome the decision error caused by insufficient human experience, and develop the mapping process for decision plans. This paper presented a decision-making method for asphalt pavement maintenance using improved weight random forest algorithm (IWRF) based on the correlation analysis (CA) and the analytic hierarchy process (AHP). Firstly, appropriate features were selected through CA of road detection data, and then decision trees were constructed based on Bootstrapping. Finally, qualified decision trees were chosen and weighted by AHP to form a random forest. To examine the feasibility, the algorithm was applied in a maintenance decision of the 80 km highway in Jiangsu province. The results showed that IWRF had a decision-making accuracy of up to 90%. Comparing with the traditional random forest algorithm, the IWRF algorithm had a 4.35% higher accuracy and saved 75% computation time.

KEYWORDS:

• Pavement maintenance
• random forest
• correlation analysis
• analytic hierarchy process
• decision intelligence

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by the National Natural Science Foundation of China (grant number 51878164 and 51922030), Key Laboratory of Jiangsu Province for Long Term Service and Safety of Road Infrastructure, Southeast University ‘Zhongying Young Scholars’ Project ‘Zhongying Young Scholars' Project, Changsha University of Science & Technology via Open Fund of National Engineering Laboratory of Highway Maintenance Technology (grant numbers kfj160104), Department of Transportation of Shandong Province, grant number 2018B51, the Science and Technology Support Project of Jiangsu Highway Engineering Maintenance Technology Co., Ltd.