Browse > Article
http://dx.doi.org/10.13161/kibim.2016.6.2.029

Developing an Estimation Model for Safety Rating of Road Bridges Using Rule-based Classification Method  

Chung, Sehwan (서울대학교 건설환경공학부)
Lim, Soram (서울대학교 건설환경공학부)
Chi, Seokho (서울대학교 건설환경공학부, 서울대학교 건설환경종합연구소)
Publication Information
Journal of KIBIM / v.6, no.2, 2016 , pp. 29-38 More about this Journal
Abstract
Road bridges are deteriorating gradually, and it is forecasted that the number of road bridges aging over 30 years will increase by more than 3 times of the current number. To maintain road bridges in a safe condition, current safety conditions of the bridges must be estimated for repair or reinforcement. However, budget and professional manpower required to perform in-depth inspections of road bridges are limited. This study proposes an estimation model for safety rating of road bridges by analyzing the data from Facility Management System (FMS) and Yearbook of Road Bridges and Tunnel. These data include basic specifications, year of completion, traffic, safety rating, and others. The distribution of safety rating was imbalanced, indicating 91% of road bridges have safety ratings of A or B. To improve classification performance, five safety ratings were integrated into two classes of G (good, A and B) and P (poor ratings under C). This rearrangement was set because facilities with ratings under C are required to be repaired or reinforced to recover their original functionality. 70% of the original data were used as training data, while the other 30% were used for validation. Data of class P in the training data were oversampled by 3 times, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was used to develop the estimation model. The results of estimation model showed overall accuracy of 84.8%, true positive rate of 67.3%, and 29 classification rule. Year of completion was identified as the most critical factor on affecting lower safety ratings of bridges.
Keywords
Road bridges; Safety rating; Rule-based classification; Facility Management System;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Bektas, B. A., Carriquiry, A., Smadi, O. (2013). Using Classification Trees for Predicting National Bridge Inventory Condition Ratings, Journal of Infrastructure Systems, 19(4), pp. 425-433.   DOI
2 Cattan, J., Mohammadi, J. (1997). Analysis of Bridge Condition Rating Data Using Neural Networks, Microcomputers in Civil Engineering, 12(6), pp. 419-429.   DOI
3 Cohen, W. W. (1995). Fast Effective Rule Induction, Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, USA.
4 Han. J., Kamber, M., Pei. J. (2012). Data Mining : Concepts Journal 38 of KIBIM Vol.6, No.2 (2016). and Techniques, 3rd ed., Morgan Kaufmann.
5 Huang, R. Y., Chen, P. F. (2012). Analysis of Influential Factors and Association Rules for Bridge Deck Deterioration with Utilization of National Bridge Inventory, Journal of Marine Science and Technology, 20(3), pp. 336-344.
6 Hua). Artificial Neural Network Model of Bridge Deterioration, Journal of Performance of Constructed Facilities, 24(6), pp. 597-602.   DOI
7 Kim, Y. J., Yoo, D. W. (2002). Lessons and Analysis of Event in Domestic Bridge Failures, Journal of Korean Society of Civil Engineers, 50(8), pp. 34-40.
8 Ko, S. S., Park, H. K., Lee, H. C., Yeo. S. K., Shin, K. S., Choi, D. Y. (2011). A Study on Survey and Improvement Plan of Facility Safety Management.
9 Korea Infrastructure Safety and Technology Corporation. (2015). Facility Management System, http://www.fms.or.kr (Nov. 18. 2015).
10 Korea Infrastructure Safety and Technology Corporation (2010). Detailed Guideline of Safety Inspection and Precise Safety Diagnosis.
11 Korea Institute of Civil Engineering and Building Technology (2015). Yearbook of Road Bridges and Tunnels (2015).
12 Lin, T. K., Lin, C. C. J., Chang, K. C. (2002). A Neural Network Based Methodology for Estimating Bridge Damage after Major Earthquakes, Journal of the Chinese Institute of Engineers, 25(4), pp. 415-424.   DOI
13 Lee, J., Sanmugarasa, K., Blumenstein, M., Loo, Y. C. (2008). Improving the Reliability of a Bridge Management System (BMS) Using an ANN-based Backward Prediction Model (BPM), Automation in Construction, 17(6), pp. 758-772.   DOI
14 Ministry of Land, Infrastructure and Transport of Korea (2015.8.11.). Special Act on the Safety Control of Public Structures.
15 Ministry of Land, Infrastructure, and Transport of Korea (2007). The Second Basic Plan for Safety Management and Maintenance of Public Structures.
16 Oh, B. H., Lee, M. K., Jeong, B. S., Lee, W. P., Kim, W. S., Choi, Y. C., Jang, S. Y., Hong, K. O., Kim, S. I., Kim, M. S., Lee, H. H., Seok, J. S., Lee, J. H., Lim, S. N., Kim, K. H., Lim, H. T., Shin, D. H. (2002). A Study on Developing an Expert System for Predicting Remaining Life of Concrete Decks of Road Bridges.
17 Park, S. H. (2014). Large Unbalanced Data Classification Based on Hadoop for Prediction of Traffic Accidents, Masters Thesis, Konkuk University.
18 Park, S. H. (2004). Survey on Road Bridges Safety Management and Improvement Plan, Korea Infrastructure Safety and Technology Corporation, Journal of Facility Safety, 13, pp. 124-130.
19 Wang. S., Yao, X. (2012). Multiclass Imbalance Problems:Analysis and Potential Solutions, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 42(4), pp. 1119-1130.   DOI