Browse > Article
http://dx.doi.org/10.12989/sem.2002.13.2.187

Evaluation of existing bridges using neural networks  

Molina, Augusto V. (Parson Transportation Group)
Chou, Karen C. (Department of Mechanical & Civil Engineering, Minnesota State University)
Publication Information
Structural Engineering and Mechanics / v.13, no.2, 2002 , pp. 187-209 More about this Journal
Abstract
The infrastructure system in the United States has been aging faster than the resource available to restore them. Therefore decision for allocating the resources is based in part on the condition of the structural system. This paper proposes to use neural network to predict the overall rating of the structural system because of the successful applications of neural network to other fields which require a "symptom-diagnostic" type relationship. The goal of this paper is to illustrate the potential of using neural network in civil engineering applications and, particularly, in bridge evaluations. Data collected by the Tennessee Department of Transportation were used as "test bed" for the study. Multi-layer feed forward networks were developed using the Levenberg-Marquardt training algorithm. All the neural networks consisted of at least one hidden layer of neurons. Hyperbolic tangent transfer functions were used in the first hidden layer and log-sigmoid transfer functions were used in the subsequent hidden and output layers. The best performing neural network consisted of three hidden layers. This network contained three neurons in the first hidden layer, two neurons in the second hidden layer and one neuron in the third hidden layer. The neural network performed well based on a target error of 10%. The results of this study indicate that the potential for using neural networks for the evaluation of infrastructure systems is very good.
Keywords
infrastructure; systems; evaluation; bridges; ratings; neural networks;
Citations & Related Records

Times Cited By Web Of Science : 3  (Related Records In Web of Science)
Times Cited By SCOPUS : 4
연도 인용수 순위
1 Li, Z., Mu, B., and Peng, J. (2000), "Alkali-silica reaction of concrete with admixtures", J. Eng. Mech., ASCE, 126(3), March, 243-249.   DOI   ScienceOn
2 Haykin, S. (1994), Neural Networks, Macmillan College Publishing Company, Inc., Englewood Cliffs, NJ.
3 Cabral, S.V., and Katafygiotis, L.S. (2001), "Neural network based response surface method and adaptive importance sampling for reliability analysis of large structural systems", Proc. of 8th ICOSSAR'01, Newport Beach, CA, USA, (in print).
4 Watanabe, K., Matsuura, I., and Abe, M. (1989), "Incipient fault diagnosis of chemical processes via artificial neural networks", AIChE J., 35, November, 1803-1812.   DOI
5 Hect-Nielsen, R. (1990), Neurocomputing, Addison-Wesley Publishing Co., Reading, MA.
6 Cheon, S.W., and Chang, S.H. (1993), "Application of neural networks to a connectionist expert system for transient identification in nuclear power plants", Nuclear Technology, 102, May, 177-191.
7 Wong, F.S., Chou, K.C., and Yao, J.T.P. (1999), "Civil engineering including earthquake engineering", Chapter 6 of Practical Applications of Fuzzy Technologies, H-J Zimmermann editor, 7 of The Handbooks of Fuzzy Sets Series, Kluwer Academic Publisher, 207-246.
8 Sankarasubramanian, G., and Rajasekaran, S. (1996), "Constitutive modeling of concrete using a new failure criterion", Comp. and Struct., 58(5), March, 1003-1014.   DOI   ScienceOn
9 Hornik, K., Stinchcombe, M., and White, H. (1989), "Multilayer feedforward network are universal approximators" Neural Networks, 2, 359-366.   DOI   ScienceOn
10 Eberhart, R.C., and Dobbins, R.W. (1990), Neural Network PC Tools: A Practical Guide, Academic Press, San Diego, CA.
11 Accarain, P., and Desbrandes, R. (1993), "Neuro-computing helps pore pressure determination", Petroleum Engineer Int., 65, February, 39-42.
12 Beale, M., and Demuth, H. (1992), Neural Network Toolbox, The MathWorks, Inc., Natick, MA.
13 Boger, Z. (1992), "Application of neural networks to water and wastewater treatment plant operation", ISA Transactions, 31(1), 25-33.   DOI   ScienceOn
14 Fwa, T.F., and Chan, W.T. (1993), "Priority rating of highway maintenance needs by neural networks", J. Transp. Eng., ASCE, 119(3), May/June, 419-432.   DOI   ScienceOn
15 Venugopal, K.P., Sudhakar, R., and Pandya, A.S. (1992), "On-line learning control of autonomous underwater vehicles using feedforward neural networks", IEEE J. of Oceanic Eng., 17, October, 308-319.   DOI   ScienceOn
16 Hung, S-L, Kao, C.Y., and Lee, J.C. (2000), "Active pulse structural control using artificial neural networks", J. Eng. Mech., ASCE, 126(8), Aug., 839-849.   DOI   ScienceOn
17 Liut, D.A., Matheu, E.E., Singh, M.P., and Mook, D.T. (1999), "Neural network control of building structures by a force-matching training scheme", Earthq. Eng. and Struct. Dyn., 28(12), 1601-1620.   DOI   ScienceOn
18 U.S. Department of Transportation/Federal Highway Administration (1979) Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation's Bridges, Design & Inspection Branch Bridge Division, Washington, D.C.
19 Molina, A.V. (1996), "Evaluation of infrastructure systems using neural networks", Master Thesis, The University of Tennessee, Knoxville, TN, 95.
20 Funahashi, K. (1989), "On the approximate realization of continuous mappings by neural network", Neural Networks, 2, 183-192.   DOI   ScienceOn
21 Masters, T. (1993), Practical Neural Network Recipes in C++, Academic Press, Inc., San Diego, CA.
22 Kim, J-T., Jung H-J., and Lee, I-W. (2000), "Optimal structural control using neural networks", J. Eng. Mech., ASCE, 126(2), Feb., 201-205.   DOI   ScienceOn
23 Levenberg, K. (1944), "A method for the solution of certain non-linear problems in least squares", Quart. J. Appl. Math., 2, 164-168.
24 Masri, S.F., Smyth, A.W., Chassiakos, A.K., Caughey, T.K. and Hunter, N.F. (2000) "Application of neural networks for detection of changes in nonlinear systems", J. Eng. Mech., ASCE, 126(7), July, 666-676.   DOI   ScienceOn
25 Moselhi, O., Hegazy, T., and Fazio, P. (1991) "Neural networks as tools in construction", J. Const. Eng. and Man., ASCE, 117(4), December, 606-624.   DOI
26 Silven, S. (1992), "A neural approach to the assignment algorithm for multiple-target tracking", IEEE J. Oceanic Eng., 17(4), October, 326-332.   DOI   ScienceOn
27 Vanluchene, R.D., and Sun, R. (1990), "Neural networks in structural engineering", Microcomputers in Civil Engineering, 9(3), Sept., 207-215.
28 Wassermann, P.D. (1989), Neural Computing: Theory and Practice, Van Nostrand Reinhold, NY.
29 Fan, J.Y., Nikolaou, M., and White, R.E. (1993), "An approach to fault diagnosis of chemical processes via neural networks", AIChE J., 39, January, 82-88.   DOI
30 Hoskin, J.C., Kaliyur, K.M., and Himmelblau, D.M. (1991), "Fault diagnosis in complex chemical plants using artificial neural networks", AIChE J., 37(1), January, 137-141.   DOI
31 ASCE (2001), "The 2001 Report Card for Americas Infrastructure", American Society of Civil Engineers, http:// www.asce.org/reportcard/index.cfm.
32 Fa-Long, L., Zheng, B., and Xiao-Peng, Z. (1992), "Real-time implementation of 'propagator' bearing estimation algorithm by use of neural network", IEEE J. Oceanic Eng., 17(4), October, 320-325.   DOI   ScienceOn
33 Loh, C-H., Lin, C-Y., and Huang, C-C. (2000), "Time domain identification of frames under earthquake loadings", J. Eng. Mech., ASCE, 126(7), July, 693-703.   DOI   ScienceOn
34 Marwala, T. (2000), "Damage identification using committee of neural networks", J. Eng. Mech., ASCE, 126(1), Jan., 43-50.   DOI   ScienceOn
35 Sasaki, T. (2001), "A neural network based response surface approach for computing failure probabilities", Proc. of 8th ICOSSAR'01, Newport Beach, CA, USA, (in print).
36 Feng, M.Q., and Bahng, E.Y. (1999), "Damage assessment of jacketed RC columns using vibration tests", J. Struct. Eng., ASCE, 125(3), March, 265-271.   DOI   ScienceOn
37 Cybenko, G. (1989), "Approximation by superposition of a sigmoidal function", Mathematics of Control, Signals, and Systems, 2, 303-314.   DOI   ScienceOn
38 Zhao, J., Ivan, J.N., and DeWolf, J.T. (1998), "Structural damage detection using artificial neural networks", J. Infrastructure Systems, 4(3), Sept., 93-101.   DOI   ScienceOn
39 Ohga, Y., and Seki, H. (1993), "Abnormal event identification in nuclear power plants using a neural network and knowledge processing", Nuclear Technology, 101, February, 159-167.   DOI
40 IEEE (1991), First IEEE Conference on Ocean Engineering (CNNOE), IEEE Press, NY.
41 Chou, K.C., and Yuan, J. (1993), "Fuzzy-Bayesian approach to reliability of existing structures", J. Struct. Eng., ASCE, 119(11), November, 3276-3290.   DOI   ScienceOn