Using rough set theory to induce pavement maintenance and rehabilitation strategy

  • Authors:

  • Jia-Ruey Chang;Ching-Tsung Hung;Gwo-Hshiung Tzeng;Shih-Chung Kang

  • Affiliations:

  • Department of Civil Engineering, MingHsin University of Science & Technology, Hsin-Chu, Taiwan;Institute of Civil Engineering, National Central University, ChungLi, Taoyuan, Taiwan;Kainan University, Luchu, Taoyuan County, Taiwan;Department of Civil Engineering, National Taiwan University, Taipei, Taiwan

  • Venue:
  • RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
  • Year:
  • 2007

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Abstract

Rough Set Theory (RST) is an induction based decision-making technique, which can extract useful information from attribute-value (decision) table. This study introduces RST into pavement management system (PMS) for maintenance and rehabilitation (M&R) strategy induction. An empirical study is conducted by using the pavement distress data collected from 7 county roads by experienced pavement engineers of Taiwan Highway Bureau (THB). For each road section, the severity and coverage of existing distresses and required M&R treatment were separately recorded. The analytical database consisting of 2,348 records (2,000 records for rule induction, and 348 records for rule testing) are established to induce M&R strategies. On the basis of the testing results, total accuracy and total coverage for the induced strategies are as high as 88.7% and 84.2% respectively, which illustrates that RST certainly can reduce distress types and remove redundant records to induce the proper M&R strategies.