A Research on the Association of Pavement Surface Damages Using Data Mining
The association of pavement surface damages used to rely on the judgments of the experts. However, with the accumulation of data in the pavement surface maintenance database and the improvement of Data Mining, there are more and more methods available to explore the association of pavement surface damages. This research adopts Apriori algorithm to conduct association analysis on pavement surface damages. From the experience of experts, it has been believed that the association of road damages is complicated. However, through case studies, it has been found that pavement surface damages are caused among longitudinal cracking, alligator cracking and pen-holes, and they are unidirectional influence. In addition, with the help of association rules, it has been learned that, in pavement surface preventative maintenance, the top priority should be the repair of longitudinal cracking and alligator cracking, which can greatly reduce the occurrence of pen-holes and the risk of state compensations.
KeywordsData Mining Association Rule Longitudinal Crack Pavement Surface Apriori Algorithm
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