Pavement Condition Assessment Using Fuzzy Logic Theory and Analytic Hierarchy Process
Publication: Journal of Transportation Engineering
Volume 137, Issue 9
Abstract
This paper integrates the advantages of the analytic hierarchy process (AHP) and fuzzy logic theory and develops a new approach for pavement condition assessment and project prioritization. Roughness, deflection, surface deterioration, rutting, and skid resistance are identified as five performance indicators for evaluating pavement condition. A survey is conducted among experienced professional engineers for establishing fuzzy membership functions of each performance indicator with respect to a fuzzy linguistic evaluation set Very good, Good, Fair, Poor, and Very poor using statistical regression. AHP is applied to determine weight from a paired-comparison matrix. Eventually the fuzzy comprehensive evaluation is carried out using fuzzy relations, which combines a fuzzy evaluation of single performance indicators to the one simultaneously considering all five performance indicators. A maximum grade principle (MGP) and a defuzzified weighted cumulative index (DWCI) are proposed to assign a linguistic assessment result and a numerical assessment result, respectively, to the condition of a road segment. A case study is provided to rank eight road segments using MGP and DWCI. The proposed method offers a promising approach to the accuracy and reliability issues in the data collection and rating of pavement distress condition.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This study is sponsored in part by the National Science Foundation (NSF) under grant No. NSFCMMI-0408390 and NSF CAREER Award NSFCMMI-0644552, by the American Chemical Society Petroleum Research Foundation under Grant No. UNSPECIFIEDPRF-44468-G9, by the National Science Foundation of China under Grant No. NSFC51050110143, by Huoyingdong Educational Foundation under Grant No. UNSPECIFIED114024, by Jiangsu Natural Science Foundation under Grant No. NSFSBK200910046, by Jiangsu Postdoctoral Foundation under Grant No. UNSPECIFIED0901005C, by Shandong Department of Transportation (DOT), by Shanxi DOT, by Shanxi DOT under Grant No. UNSPECIFIED7921000015, and by Yunnan DOT under Grant No. UNSPECIFIED2007(A)1-03, for which the authors are very grateful.
References
Arliansyah, J., Maruyama, T., and Takahashi, O. (2003). “A development of fuzzy pavement condition assessment.” Proc. JSCE, 61(746), 275–285.
Baladi, G. Y. (1990). “Analysis of pavement distress data, pavement distress indicies and remaining service life.” Module A-1, Course note on advanced pavement management systems, Federal Highway Administration, Washington, DC.
Bhushan, N., and Rai, K. (2004). Strategic decision making: Applying the analytic hierarchy process, Springer-Verlag, London.
Bogus, S. M., Song, J., Waggerman, R., and Lenke, L. R. (2010). “Correlation method for evaluating manual pavement distress data variability.” J. Infrastruct. Syst., 16(1), 66–72.
Chouinard, L. E. (1996). “Ranking models used for condition assessment of civil infrastructure systems.” J. Infrastruct. Syst., 2(1), 23–29.
Deng, X., and Sun, L. (1996). “The Euclid norm weight model for and its application in pavement evaluation.” China J. Highw. Transp., 9(1), 21–29 (in Chinese).
Ellingwood, B. R. (2005). “Risk-informed condition assessment of civil infrastructure: State of practice and research issues.” Struct. Infrastruct. Eng., 1(1), 7–18.
ERES Consultants. (1986). “MTC pavement management system user’s guide.” Metropolitan Transportation Commission, Oakland, CA.
Feng, S., and Xu, L. D. (1999). “Decision support for fuzzy comprehensive evaluation of urban development.” Fuzzy Sets Syst., 105(1), 1–12.
Fernando, E. G., and Hudson, W. R. (1983). “Development of a prioritization procedure for the network level pavement management system.” Research Rep. 307-2, Center for Transportation Research, Univ. of Texas, Austin, TX.
Forman, E. H., and Gass, S. I. (2001). “The analytical hierarchy process—An exposition.” Oper. Res., 49(4), 469–487.
Fugler, M. D., Richard, R., and Alawady, M. (1995). “Systematic evaluation of structural deterioration in underwater bridge substructures.” Transportation Research Record 1476, Transportation Research Board, Washington, DC, 139–146.
Georgopoulos, A., Loizos, A., and Flouda, A. (1995). “Digital image processing as a tool for pavement distress evaluation.” ISPRS J. Photogramm. Remote Sens., 50(1), 23–33.
Greene, J., and Shahin, M. (2010). “Airfield pavement condition assessment.” U.S. Army Corps of Engineers, 〈http://www.cecer.army.mil/td/tips/index.cfm〉 (Sep. 2010).
Haas, R., Hudson, W. R., and Zaniewski, J. (1994). Modern pavement management, Krieger, Malabar, FL.
He, Z. (1984). Fuzzy set theory and its applications, Tianjing Science and Technology, Tianjing, China (in Chinese).
Hudson, W. R., Haas, R., and Uzan, J. (1997). Infrastructure management, McGill-Hill, New York.
Ismail, N., Ismail, A., and Atiq, R. (2009). “An overview of expert systems in pavement management.” Eur. J. Sci. Res., 30(1), 99–111.
Jung, Y., Freeman, T. J., and Zollinger, D. G. (2008). “Guideline for routine maintenance of concrete pavement.” Research Rep. 5821-1, Texas Transportation Institute, Texas A&M Univ., College Station, TX.
Kang, M., Kim, M., and Lee, J. H. (2010). “Analysis of rigid pavement distresses on interstate highway using decision tree algorithms.” KSCE J Civ. Eng., 14(2), 123–130.
Kaufmann, A., and Gupta, M. M. (1991). Introduction to fuzzy arithmetic theory and applications, Van Nostrand Reinhold, New York.
Kim, J. (2000). “Statistical analysis of video images for evaluating pavement distress.” KSCE J Civ. Eng., 4(4), 257–264.
Larson, C. D., and Forman, E. H. (2007). “Application of the analytic hierarchy process to select project scope for videologging and pavement condition data collection.” Proc., 86th Annual Meeting of the Transportation Research Board, Washington, DC.
Loizos, A., and Plati, C. (2007). “Ground penetrating radar as an engineering diagnostic tool for foamed asphalt treated pavement layers.” Int. J. Pavement Eng., 8(2), 147–155.
Lootsma, F. A. (1997). Fuzzy logic for planning and decision making, Kluwer Academic, Dordrecht, Netherlands.
Luo, Z., and Yin, H. (2008). “Probabilistic analysis of pavement distress ratings with the clusterwise regression method.” Proc., 87th Annual Meeting of the Transportation Research Board, Washington, DC.
Mahler, D. S., Kharoufa, Z. B., Wong, E. K., and Shaw, L. G. (1991). “Pavement distress analysis using image processing techniques.” Comput. Aided Civ. Infrastruct. Eng., 6, 1–14.
Maser, K. R. (1996). “Condition assessment of transportation infrastructure using ground-penetrating radar.” J. Infrastruct. Syst., 2(2), 94–101.
Maser, K. (2005). “Automated systems for infrastructure condition assessment.” J. Infrastruct. Syst., 11(3), 153–153.
Mishalani, R. G., and Koutsopoulos, H. N. (1995). “Role of spatial dimension in infrastructure condition assessment and deterioration modeling.” Transportation Research Record 1508, Transportation Research Board, Washington, DC, 45–52.
Mustaffar, M., Ling, T. C., and Puan, O. C. (2008). “Automated pavement imaging program (APIP) for pavement cracks classification and quantification—A photogrammetric approach.” Int. Arch. Photogram. Rem. Sens. Spatial Inform. Sci., 37(B4), 367–372.
Pedrycz, W., and Gomide, F. (1998). An introduction to fuzzy sets: Analysis and design, MIT Press, Cambridge, MA.
Saaty, T. L. (2001). Fundamentals of decision making and priority theory, RWS Publications, Pittsburgh.
Saaty, T. L. (2008). “Relative measurement and its generalization in decision making: Why pairwise comparisons are central in mathematics for the measurement of intangible factors—The analytic hierarchy/network process.” Rev. R. Spanish Acad. Sci., Ser. A, Math., 102(2), 251–318.
Shahin, M. Y., and Kohn, S. D. (1979). “Development of pavement condition rating procedures for roads, streets and parking lots. Volume I. Condition rating procedure.” Technical Rep. M-268, Construction Engineering Research Laboratory, U.S. Army Corps of Engineers, Champaign, IL.
Song, B., Hao, S., Murakami, S., and Sadohara, S. (1996). “Comprehensive evaluation method on earthquake damage using fuzzy theory.” J. Urban Plann. Dev., 122(1), 1–17.
Sun, L. (1993). “The determination of degree of safety in bridge design based on fuzzy comprehensive evaluation.” J. Southeast Univ., 23(4), 138–142 (in Chinese).
Sun, L., and Deng, X. (1996). “Mathematical model of weight in evaluation systems.” J. Southeast Univ., 12(2), 111–118 (in Chinese).
Sun, L., and Deng, X. (1997). “Weight analysis in evaluation system.” J. Syst. Sci. Syst. Eng., 6(2), 137–147.
Sun, L., Deng, X., and Gu, W. (1997). “Pattern recognition of standard AC pavement structures for the design of high level highways.” China Civ. Eng. J., 30(3), 55–62 (in Chinese).
Sun, L., and Greenberg, B. S. (2006). “Multicriteria group decision making: Optimal priority synthesis from pairwise comparisons.” J. Optim. Theory Appl., 130(2).
Sun, Y. (2009). “Automated pavement distress detection using advanced image processing techniques.” M.S. thesis, Univ. of Toledo, OH.
Triantaphyllou, E. (2000). Multi-criteria decision making methods: A comprehensive study, Kluwer Academic, Dordrecht, Netherlands.
Wang, K. C. P. (2004). “Automated pavement distress survey through stereovision.” Final Rep., Highway IDEA Project 88, Federal Highway Administration, Washington, DC.
Wang, Y. M., and Elhag, T. M. S. (2008). “Evidential reasoning approach for bridge condition assessment.” Expert Syst. Appl., 34(1), 689–699.
Yager, R. (1988). “On ordered weighted averaging aggregation operators in multi-criteria decision making.” IEEE Trans. Syst. Man Cybern., 18(1), 183–190.
Yager, R. R., and Kacprzyk, J. (1997). The ordered weighted averaging operators, Kluwer, Norwell, MA.
Yang, T., and Yang, X. (1998). “Fuzzy comprehensive assessment, fuzzy clustering analysis and its application for urban traffic environment quality evaluation.” Transp. Res. Part D, 3(1), 51–57.
Zhou, J., Huang, P., and Chiang, F. P. (2005). “Wavelet-based pavement distress classification.” Transportation Research Record 1940, Transportation Research Board, Washington, DC.
Zimmermann, H. J. (1991). Fuzzy set theory and its applications, Kluwer Academic, Boston.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 137 • Issue 9 • September 2011
Pages: 648 - 655
Copyright
© 2011 American Society of Civil Engineers.
History
Received: May 24, 2010
Accepted: Oct 8, 2010
Published online: Oct 26, 2010
Published in print: Sep 1, 2011
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.