A considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures such as beams or plates. In the application on a complex structure, such as steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates and then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple-damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Abdalla M, Grigoriadis K, Zimmerman D (2003) An optimal hybrid expansion-reduction damage detection method. J Vib Control 9(8):983–995. doi:10.1177/10775463030098005
Abdo MAB, Hori M (2002) A numerical study of structural damage detection using changes in the rotation of mode shapes. J Sound Vib 251(2):227–239. doi:10.1006/jsvi.2001.3989
Bernal D (2002) Load vectors for damage localization. J Eng Mech 128(1):7–14. doi:10.1061/(ASCE)0733-9399(2002)128:1(7)
Carrasco CJ, Osegueda RA, Ferregut CM, Grygier M (1997) Damage localization in a space truss model using modal strain energy. In: Proceedings of the 1997 15th international modal analysis conference, IMAC. Part 2 (of 2). Orlando
Cawley P, Adams RD (1979) The location of defects in structures from measurements of natural frequencies. J Strain Anal Eng Design 14(2):49–57. doi:10.1243/03093247V142049
Chou JH, Ghaboussi J (2001) Genetic algorithm in structural damage detection. Comput Struct 79(14):1335–1353. doi:10.1016/S0045-7949(01)00027-X
Friswell MI, Penny JET, Garvey SD (1998) A combined genetic and eigensensitivity algorithm for the location of damage in structures. Comput Struct 69(5):547–556. doi:10.1016/S0045-7949(98)00125-4
Gao Y, Spencer BF (2006) Online damage diagnosis for civil infrastructure employing a flexibility-based approach. Smart Mater Struct 15(1):9–19. doi:10.1088/0964-1726/15/1/030
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston, pp 1–432
Guo HY, Li ZL (2009) A two-stage method to identify structural damage sites and extents by using evidence theory and micro-search genetic algorithm. Mech Syst Signal Process 23(3):769–782. doi:10.1016/j.ymssp.2008.07.008
Hassiotis S (2000) Identification of damage using natural frequencies and Markov parameters. Comput Struct 74(3):365–373. doi:10.1016/S0045-7949(99)00034-6
Hassiotis S, Jeong G (1993) Assessment of structural damage from natural frequency measurements. Comput Struct 49(4):679–691. doi:10.1016/0045-7949(93)90071-K
Hassiotis S, Jeong GD (1995) Identification of stiffness reductions using natural frequencies. J Eng Mech 121(10):1106–1113. doi:10.1061/(ASCE)0733-9399(1995)121:10(1106
Luber W (1997) Structural damage localization using optimization method. In: Anon (ed) Proceedings of the international modal analysis conference IMAC. SEM, Bethel, pp 1088–1095
Maeck J, De Roeck G (1999) Dynamic bending and torsion stiffness derivation from modal curvatures and torsion rates. J Sound Vib 225(1):153–170. doi:10.1006/jsvi.1999.2228
Messina A, Jones IA, Williams EJ (1996) Damage detection and localisation using natural frequency changes. In: Conference on identification in engineering systems. Swansea, pp 67–76
Messina A, Williams EJ, Contursi T (1998) Structural damage detection by a sensitivity and statistical-based method. J Sound Vib 216(5):791–808. doi:10.1006/jsvi.1998.1728
Park S, Stubbs N, Bolton R, Choi S, Sikorsky C (2001) Field verification of the damage index method in a concrete box-girder bridge via visual inspection. Comput Aided Civ Infrastruct Eng 16(1):58–70
Perera R, Ruiz A, Manzano C (2009) Performance assessment of multicriteria damage identification genetic algorithms. Comput Struct 87(1–2):120–127. doi:10.1016/j.compstruc.2008.07.003
Pothisiri T, Hjelmstad KD (2003) Structural damage detection and assessment from modal response. J Eng Mech 129(2):135–145. doi:10.1061/(ASCE)0733-9399(2003)129:2(135
Sazonov ES, Klinkhachorn P, GangaRao HVS, Halabe UB (2003) Non-baseline damage detection from changes in strain energy mode shapes experiments on armored vehicle launched bridge. In: Proceedings of 29th annual review of progress in quantitative nondestructive evaluation (QNDE). Bellingham, pp 1415–1422. doi:10.1063/1.1570297
Shi ZY, Law SS, Zhang LM (2000) Damage localization by directly using incomplete mode shapes. J Eng Mech 126(6):656–660. doi:10.1061/(ASCE)0733-9399(2000)126:6(656)
Wang JY, Ko JM, Ni YQ (2000) Modal sensitivity analysis of Tsing Ma Bridge for structural damage detection. In: Proceedings of SPIE—the international society for optical engineering: nondestructive evaluation of highways, utilities, and pipelines IV, vol 3995. Society of Photo-Optical Instrumentation Engineers, Newport Beach, pp 300–311
Wang L (2012) Innovative damage assessment of steel truss bridges using modal strain energy correlation. Queensland University of Technology
Wang L, Chan THT, Thambiratnam DP, Tan ACC (2010a) Damage detection for truss bridge structures using correlation-based structural modal strain energy. In: Bartlett FM (ed) Proceedings of the 8th international conference on short and medium span bridges. CSCE, Niagara Falls, Ontario, pp 143–151
Wang L, Chan THT, Thambiratnam DP, Tan ACC (2010) Improved correlation-based modal strain energy method for global damage detection of truss bridge structures. In: Zhang JR, Cai CS (eds) Proceedings of the international symposium on life-cycle performance of bridge and structures. Science Press (China), Changsha, pp 145–154
Zhang L, Wang T, Tamura Y (2010) A frequency-spatial domain decomposition (FSDD) method for operational modal analysis. Mec Syst Signal Process 24(5):1227–1239. doi:10.1016/j.ymssp.2009.10.024
The authors acknowledge the financial support from China Scholarship Council (CSC), Queensland University of Technology and the ARC Linkage Project (No. LP0882162).
About this article
Cite this article
Wang, F.L., Chan, T.H.T., Thambiratnam, D.P. et al. Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm. J Civil Struct Health Monit 3, 117–127 (2013). https://doi.org/10.1007/s13349-013-0041-8
- Structural vibration
- Damage detection
- Truss bridges
- Genetic algorithm
- Model strain energy
- Optimization-based methods