Soft Computing Applications in Infrastructure Management
Publication: Journal of Infrastructure Systems
Volume 10, Issue 4
Abstract
Infrastructure management decisions, such as condition assessment, performance prediction, needs analysis, prioritization, and optimization are often based on data that is uncertain, ambiguous, and incomplete and incorporate engineering judgment and expert opinion. Soft computing techniques are particularly appropriate to support these types of decisions because these techniques are very efficient at handling imprecise, uncertain, ambiguous, incomplete, and subjective data. This paper presents a review of the application of soft computing techniques in infrastructure management. The three most used soft computing constituents, artificial neural networks, fuzzy systems, and genetic algorithms, are reviewed, and the most promising techniques for the different infrastructure management functions are identified. Based on the applications reviewed, it can be concluded that soft computing techniques provide appealing alternatives for supporting many infrastructure management functions. Although the soft computing constituents have several advantages when used individually, the development of practical and efficient intelligent tools is expected to require a synergistic integration of complementary techniques into hybrid models.
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References
1.
American Association of State Highway and Transportation Officials (AASHTO). ( 2001). Pavement management guide, AASHTO, Washington, D.C.
2.
American Association of State Highway and Transportation Officials (AASHTO). ( 2002). Transportation asset management guide, AASHTO, Washington, D.C. 〈http://downloads.transportation.org/amguide.pdf〉 (March 2004).
3.
Abdelrahim, A. M., and George, K. P. ( 2000). “Artificial neural network for enhancing selection of pavement maintenance strategy.” Trans. Res. Rec. 1669, Transportation Research Board, Washington D.C., 16–22.
4.
Alsugair, A. M., and Al-Qudrah, A. A. (1998). “Artificial neural network approach for pavement maintenance.” J. Comput. Civ. Eng., 12(4), 249–255.
5.
ASCE. (2003). “Report card for america’s infrastructure, 2003 progress report.” 〈http://www.asce.org/reportcard/〉 (March 2004).
6.
Bandara, N., and Gunaratne, M. (2001). “Current and future pavement maintenance prioritization based on rapid visual condition evaluation.” J. Transp. Eng., 127(2), 116–123.
7.
Cattan, J., and Mohammadi, J. (1997). “Analysis of bridge condition rating data using neural networks.” Microcomput. Civ. Eng., 12(6), 419–429.
8.
Chae, M. J., and Abraham, D. M. (2001). “Neuro-fuzzy approaches sanitary sewer pipeline condition assessment.” J. Comput. Civ. Eng., 15(1), 4–14.
9.
Chan, W. T., Fwa, T. F., and Hoque, K. Z. (2001). “Constraint handling methods in pavement maintenance programming.” Transp. Res., Part C: Emerg. Technol., 9(3), 175–190.
10.
Cheng, H. D., Chen, J., Glazier, C., and Hu, Y. G. (1999). “Novel approach to pavement cracking detection based on fuzzy set theory.” J. Comput. Civ. Eng., 13(4), 270–280.
11.
Chiang, W., Liu, K. F., and Lee, J. (2000). “Bridge damage assessment through fuzzy petri net based expert system.” J. Comput. Civ. Eng., 14(2), 141–149.
12.
Chou, J., O’Neill, W. A., and Cheng, H. ( 1995). “Pavement distress evaluation using fuzzy logic and moment invariants.” Transp. Res. Rec. 1505, Transportation Research Board, Washington, D.C.,39–46.
13.
Eldin, N. N., and Senouci, A. B. (1995). “A pavement condition rating model using backpropagation neural network.” Microcomput. Civ. Eng., 10(6), 433–441.
14.
Elton, D. J., and Juang, C. H. ( 1988). “Asphalt pavement evaluation using fuzzy sets.” Transp. Res. Rec. 1196, Transportation Research Board, Washington, D.C.,1–6.
15.
Farias, M.M., Neto, S.A. D., and Souza, R.O. ( 2003). “Prediction of longitudinal roughness using neural network.” MAIREPAV03—3rd Int. Symp. on Maintenance and Rehabilitation of Pavements and Technological Control, 87–97.
16.
Felker, V., Hossain, M., Najjar, Y., and Barezinsky, R. ( 2003). “Modeling the roughness of Kansas PCC pavements: A dynamic ANN approach.” 82th Transportation Research Board Annual Meeting, (CD-ROM), Washington, D.C.
17.
Ferreira, A., Antunes, A., and Picado-Santos, L. (2002). “Probabilistic segment-linked pavement management optimization model.” J. Transp. Eng., 128(6), 568–577.
18.
Federal Highway Administration (FHWA). ( 1999). Asset management primer, Office of Asset Management, FHWA, Washington, D.C.
19.
Flintsch, G.W. ( 2002). “Soft computing applications in transportation infrastructure asset management.” Proc., Application of Advanced Technologies in Transportation, ASCE, Reston, Va., 449–456.
20.
Flintsch, G.W. ( 2003). “Pavement management enhancement using soft computing.” MAIREPAV03—3rd Int. Symp. on Maintenance and Rehabilitation of Pavements and Technological Control, 783–792.
21.
Flintsch, G. W., Zaniewski, J. P. and Delton, J. ( 1996). “An artificial neural network for selecting pavement rehabilitation projects.” Transp. Res. Rec. 1524, Transportation Research Board, Washington, D.C.,185–193.
22.
Fontul, S., Autunes, M., and Marcelino, J. ( 2003). “Structural evaluation of pavements using neural networks.” MAIREPAV03—3rd Int. Symp. on Maintenance and Rehabilitation of Pavements and Technological Control, 119–127.
23.
Fwa, T. F., and Chan, W. T. (1993). “Priority rating of highway needs by neural networks.” J. Transp. Eng., 119(3), 419–432.
24.
Fwa, T. F., Chan, W. T., and Tan, C. Y. (1996). “Genetic-algorithm programming of road maintenance and rehabilitation.” J. Transp. Eng., 122(3), 246–253.
25.
Fwa, T.F., and Shanmugam, R. ( 1998). “Fuzzy logic technique for pavement condition rating and maintenance-needs assessment.” Proc., 4th Int. Conf. on Managing Pavements, 465–476.
26.
General Accounting Office (GAO). ( 2000). U.S. infrastructure, funding trends and opportunities to improve investment decisions, Rep. to the Congress GAO/RCE/AIMD-00-35, U.S. GAO, Washington, D.C.
27.
Goldberg, D. ( 1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley, Reading, Mass.
28.
Grivas, D., and Shen, Y. C. ( 1995). “Use of fuzzy relations to manage decisions in preserving civil infrastructure.” Transp. Res. Rec., Transportation Research Board, Washington D.C., 10–18.
29.
Hajek, J. J., and Hurdal, B. ( 1993). “Comparison of rule-based and neural network solutions for a structured selection problem.” Transp. Res. Rec. 1399, Transportation Research Board, Washington, D.C.,1–7.
30.
Hedfi, A., and Stephanos, P. ( 2001). “Pavement performance modeling: An applied approach at the state of Maryland.” 5th Int. Conf. on Managing Pavements (CD-ROM).
31.
Holland, J. ( 1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, Mich.
32.
Huang, Y., and Moore, R. K. ( 1997). “Roughness level probability prediction using artificial neural networks.” Transp. Res. Rec. 1592, Transportation Research Board, Washington D.C., 89–97.
33.
Hudson, W.R., Haas, R.C., and Uddin, W. ( 1997). Infrastructure management, McGraw-Hill, New York.
34.
Kaseko, M. S., and Ritchie, S. G. (1993). “A neural network-based methodology for pavement crack detection and classification.” Transp. Res., Part C: Emerg. Technol., 1C, 275–291.
35.
Kosko, B. ( 1992). Neural networks and fuzzy systems: A dynamical systems approach to machine intelligence, Prentice-Hall, Englewood Cliffs, N.J.
36.
La Torre, F., Domenichini, L., and Darter, M.I. ( 1998). “Roughness prediction based on the artificial neural network approach.” Proc., 4th Int. Conf. on Managing Pavements, Vol. 2, 599–612.
37.
Lee, B. J., and Lee, H. (2004). “Position invariant neural network for digital pavement crack analysis.” Comput. Aided Civ. Infrastruct. Eng., 12(2), 105–108.
38.
Liang, M., Wu, J., and Liang, C. (2001). “Multiple layer fuzzy evaluation for existing reinforced concrete bridges.” J. Infrastruct. Syst., 7(4), 144–159.
39.
Lin, J., Yau, J., and Hsiao, L. ( 2003). “Correlation analysis between international roughness index (IRI) and pavement distress by neural network.” 82th Transportation Research Board Annual Meeting (CD-ROM), Washington, D.C.
40.
Liu, C., Hammad, A., and Itoh, Y. (1997). “Maintenance strategy optimization of bridge decks using genetic algorithm.” J. Transp. Eng., 123(2), 91–100.
41.
Lou, Z., Gunaratne, M., Lu, J. J., and Dietrich, B. (2001). “Application of a neural network model to forecast short-term pavement crack condition: Florida case study.” J. Infrastruct. Syst., 7(4), 166–171.
42.
Martinelli, D., and Shoukry, S. N. ( 2000). “Performance evaluation of neural network in concrete condition assessment.” Transp. Res. Rec. 1739, Transportation Research Board, Washington, D.C., 76–82.
43.
Martinelli, D., Shoukry, S. N., and Varadarajan, S. T. ( 1995). “Hybrid artificial intelligence approach to continuous bridge monitoring.” Transp. Res. Rec.1497, Transportation Research Board, Washington, D.C., 77–82.
44.
Miyamoto, A., Kawamura, K., and Nakamura, H. (2000). “Bridge management system and maintenance optimization for existing bridges.” Comput. Aided Civ. Infrastruct. Eng., 15(1), 45–55.
45.
Nauck, D., Klawonn, F., and Kruse, R. ( 1997). Foundations of neuro-fuzzy systems, Wiley, New York.
46.
Owusu-Ababia, S. (1998). “Effect of neural network topology on flexible pavement cracking prediction.” Comput. Aided Civ. Infrastruct. Eng., 13(5), 349–355.
47.
Pant, D. P., Zhou, X., Arudi, R. S., Bodocsi, A., and Aktan, A. E. ( 1993). “Neural-network-based procedure for condition assessment of utility cuts in flexible pavements.” Transp. Res. Rec. 1399, Transportation Research Board, Washington, D.C., 8–13.
48.
Pilson, C., Hudson, W. R., and Anderson, V. ( 1999). “Multiobjective optimization in pavement management by using genetic algorithms and efficient surfaces.” Transp. Res. Rec. 1655, Transportation Research Board, Washington, D.C., 42–48.
49.
Prechaverakul, S., and Hadipriono, F. C. ( 1995). “Using a knowledge-based expert system and fuzzy logic for minor rehabilitation projects in Ohio.” Transp. Res. Rec. 1497, Transportation Research Board, Washington, D.C., 19–26.
50.
Razaqpur, A. G., Abd El Halim, A. O., and Mohamed, H. A. (1996). “Bridge management by dynamic programming and neural networks.” Can. J. Civ. Eng., 5(23), 1064–1069.
51.
Ritchie, S. G., Kaseko, M., and Bavarian, B. ( 1991). “Development of an intelligent system for automated pavement evaluation.” Transp. Res. Rec. 1311, Transportation Research Board, Washington, D.C., 112–119.
52.
Sadek, A.W., Shoukry, S.N., and Riad, M. ( 2003). “Dynamic artificial neural networks for dowel bending moment prediction from temperature gradient profile in concrete slabs.” 82th Transportation Research Board Annual Meeting (CD-ROM), Washington D.C.
53.
Saitoh, M., and Fukuda, T. (2000). “Modelling an asphalt pavement repair system considering fuzziness of budget constraints.” Comput. Aided Civ. Infrastruct. Eng., 15(1), 39–44.
54.
Senglaub, M., and Bahill, A. T. (1995). “Application of fuzzy technology to risk-based design and decision problems. Risk and safety assessment: Where is the balance?” ASME J. Pressure Vessel Technol., 296, 191–197.
55.
Shekharan, A. R. ( 1998). “Effect of noisy data on pavement performance prediction by artificial neural networks.” Transp. Res. Rec. 1643, Transportation Research Board, Washington, D.C., 7–13.
56.
Shekharan, A. R. ( 2000). “Solution of pavement deterioration equations by genetic algorithms.” Transp. Res. Rec. 1669, Transportation Research Board, Washington, D.C., 101–106.
57.
Shoukry, S. N., Martinelli, D. R., and Reigle, J. A. ( 1997). “Universal pavement distress evaluator based on fuzzy sets.” Transp. Res. Rec. 1592, Transportation Research Board, Washington, D.C., 180–186.
58.
Taha, M. A., and Hanna, A. S. ( 1995). “Evolutionary neural network model for the selection of pavement maintenance strategy.” Transp. Res. Rec. 1497, Transportation Research Board, Washington, D.C., 70–76.
59.
Taylor, J.C. ( 1995). “Chapter 1: The promise of neural networks.” Neural Networks, Alfred Waller, Henley on Thames, U.K.
60.
Van der Gryp, A., Bredenhann, S.J., Henderson, M.G., and Rohde, G.T. ( 1998). “Determining the visual condition index of flexible pavements using artificial neural networks.” Proc., 4th Int. Conf. on Managing Pavements, 115–129.
61.
Wang, K. C. P., and Liu, F. (1997). “A fuzzy set-based and performance oriented pavement network optimization system.” J. Infrastruct. Syst., 3(4), 154–159.
62.
Wang, K.C. P., Nallamothu, S., and Elliot, R.P. ( 1998). “Classification of pavement surface distress with an embedded neural net chip.” Artificial neural networks for civil engineers: Advanced features and applications, ASCE, Reston, Va, 131–161.
63.
Yang, J., Lu, J.J., Gunaratne, M., and Xiang, Q. ( 2003). “Overall pavement condition forecasting using neural networks—An application to Florida highway network.” 82th Transportation Research Board Annual Meeting (CD-ROM), Washington, D.C.
64.
Zadeh, L. A. (1965). “Fuzzy sets.” Inf. Control, 8, 338–353.
65.
Zadeh, L. A. (1973). “Outline of a new approach to the analysis of complex systems and decision processes.” IEEE Trans. Syst. Man Cybern., SMC 3, 28–44.
66.
Zadeh, L.A. ( 1997). “The role of fuzzy logic and soft computing in the conception, design, and deployment of intelligent systems.” Software agents and soft computing, Springer, New York.
67.
Zadeh, L. A. (2001). “Applied soft computing—Foreword.” Appl. Soft Comput., 1(1), 1–2.
68.
Zhang, Z., Singh, N., and Hudson, W. R. ( 1993). “Comprehensive ranking index for flexible pavement using fuzzy sets model.” Transp. Res. Rec. 1397, Transportation Research Board, Washington, D.C., 96–102.
69.
Zimmerman, K. ( 1995). “Pavement management methodologies to select projects and recommend preservation treatments.” National cooperative highway research program synthesis of the highway practice 222, Transportation Research Board, Washington, D.C.
Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 10 • Issue 4 • December 2004
Pages: 157 - 166
Copyright
Copyright © 2004 ASCE.
History
Published online: Nov 15, 2004
Published in print: Dec 2004
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