A Research on the Association of Pavement Surface Damages Using Data Mining

  • Ching-Tsung Hung
  • Jia-Ray Chang
  • Jian-Da Chen
  • Chien-Cheng Chou
  • Shih-Huang Chen
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)


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.


Data Mining Association Rule Longitudinal Crack Pavement Surface Apriori Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amado, V.: Expanding the Use of Pavement Management Data. In: 2000 MTC Transportation Scholars Conference, Ames, Iowa (2000)Google Scholar
  2. 2.
    Sarimollaoglu, M., Dagtas, S., Iqbal, K., Bayrak, C.: A Text-Independent Speaker Identification System Using Probabilistic Neural Networks. In: Proceedings of the International Conference on Computing, Communication and Control Technologies CCCT 2004, Austin, Texas, USA, vol. 7, pp. 407–411 (2004)Google Scholar
  3. 3.
    Nassar, K.: Application of data-mining to state transportation agencies. IT con. 12, 139–149 (2007)Google Scholar
  4. 4.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufman, San Francisco (2000)Google Scholar
  5. 5.
    Agrawal, R., Imilienski, T., Swami, A.: Mining association rules between sets of items in large datasets. In: Buneman, P., Jajodia, S. (eds.) Proc. of the 1996 ACM SIGMOD Int’l Conf. on Management of Data, pp. 207–216. ACM Press, New York (1993)Google Scholar
  6. 6.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic Itemset Counting and Implication Rules for Market Basket Analysis. In: Proceeding of 1997 ACM-SIGMOD (SIGMOD 1997), Tucson, AZ, pp. 255–264 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ching-Tsung Hung
    • 1
  • Jia-Ray Chang
    • 2
  • Jian-Da Chen
    • 3
  • Chien-Cheng Chou
    • 4
  • Shih-Huang Chen
    • 5
  1. 1.Assistant Professor, Department of Transportation Technology and Supply Chain ManagementKainan University 
  2. 2.Associate Professor, Department of Civil EngineeringMinghsin University of Science and Technology 
  3. 3.Ph.D. Candidate, Department of Civil EngineeringNational Central University 
  4. 4.Assistant Professor, Department of Civil EngineeringNational Central University 
  5. 5.Assistant Professor, Department of Traffic Engineering and ManagementFeng Chia University 

Personalised recommendations