Skip to main content

Advertisement

SpringerLink
Log in

Finite element model updating of a multispan bridge with a hybrid metaheuristic search algorithm using experimental data from wireless triaxial sensors

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The Guadalquivir bridge is a large-scale twin steel truss bridge located in Spain that opened to traffic in 1929. Since the bridge has come into operation for a long time, structural health monitoring (SHM) is strictly necessary to guarantee safety and avoid serious incidents. This paper proposes a novel approach to model updating for the Guadalquivir bridge based on the vibration measurements combined with a hybrid metaheuristic search algorithm. Cuckoo Search (CS) is an evolutionary algorithm derived from global search techniques to look for the best solution. Nevertheless, CS contains some fundamental defects that may reduce its effectiveness in dealing with optimization issues. A main drawback of CS arises in the low convergence level because CS applies fixed values for parameters when looking for the optimal solution. In addition, CS relies a lot on the quality of original populations and does not have the capability to enhance the quality of the next generations. If the position of the original particles is far from the optimal places, it may be challenging to look for the best solution. To remedy the shortcomings of CS, we propose a hybrid metaheuristic algorithm (HGAICS) employing the advantages of both Genetic Algorithm (GA) and Improved Cuckoo Search (ICS) to solve optimization problems. HGAICS contains two outstanding characteristics as follows: (1) GA is employed to create original particles with the best quality based on the capacity of crossover and mutation operators and (2) those particles are then applied to look for the global best derived from the flexible and global search ability of ICS. This paper also presents the application of wireless triaxial sensors (WTSs) taking the place of classical wired systems (CWSs) to the measurements. The use of WTSs increases dramatically the freedom in setting up experimental measurements. The results show that the performance of the proposed hybrid algorithm not only determines uncertain parameters of the Guadalquivir bridge properly, but also is more accurate than GA, CS, and improved CS (ICS). A MATLAB package of the proposed method (HGAICS) is available via GitHub: https://github.com/HoatranCH/HGAICS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Li X, Wen Z, Su H (2019) An approach using random forest intelligent algorithm to construct a monitoring model for dam safety. Eng Comput 2019:1–18

    Google Scholar 

  2. Li M, Si W, Ren Q, Song L, Liu H (2020) An integrated method for evaluating and predicting long-term operation safety of concrete dams considering lag effect. Eng Comput 2020:1–15

    Google Scholar 

  3. Hien TD (2020) A static analysis of nonuniform column by stochastic finite-element method using weighted integration approach. Transp Commun Sci J 2020:70

    Google Scholar 

  4. Gillich GR, Ntakpe JL, Wahab MA, Praisach ZI, Mimis MC (2017) Damage detection in multi-span beams based on the analysis of frequency changes. J Phys Conf Ser 2017:842

    Google Scholar 

  5. Gillich GR, Praisach ZI (2013) Detection and quantitative assessment of damages in beam structures using frequency and stiffness changes. Key Eng Mater 569:1013–1020

    Article  Google Scholar 

  6. Thein CK, Liu JS (2017) Numerical modeling of shape and topology optimisation of a piezoelectric cantilever beam in an energy-harvesting sensor. Eng Comput 33(1):137–148

    Article  Google Scholar 

  7. Sun Z, Wei M, Zhang Z, Qu G (2019) Secure routing protocol based on multi-objective ant-colony-optimization for wireless sensor networks. Appl Soft Comput 77:366–375

    Article  Google Scholar 

  8. Hoa TN, Khatir S, De Roeck G, Long NN, Thanh BT, Wahab MA (2020) An efficient approach for model updating of a large-scale cable-stayed bridge using ambient vibration measurements combined with a hybrid metaheuristic search algorithm. Smart Struct Syst 25(4):487–499

    Google Scholar 

  9. Dang HV, Tran-Ngoc H, Nguyen TV, Bui-Tien T, De Roeck G, Nguyen HX (2020) Data-driven structural health monitoring using feature fusion and hybrid deep learning. IEEE Trans Autom Sci Eng 2020:5

    Google Scholar 

  10. Tran-Ngoc H, Khatir S, Le-Xuan T, De Roeck G, Bui-Tien T, Wahab MA (2020) A novel machine-learning based on the global search techniques using vectorized data for damage detection in structures. Int J Eng Sci 157:103376

    Article  MathSciNet  Google Scholar 

  11. Mai BTT, Cuong NH, Quang ND, Tai DH (2020) Experimental study on flexural and shear behaviour of sandwich panels using glass textile reinforced concrete and autoclaved aerated concrete

  12. Cuong NH, Quang ND (2020) Experimental study on flexural behavior of prestressed and non-prestressed textile reinforced concrete plates

  13. Kao CY, Hung SL (2003) Detection of structural damage via free vibration responses generated by approximating artificial neural networks. Comput Struct 81(28–29):2631–2644

    Article  Google Scholar 

  14. Ashebo DB, Chan TH, Yu L (2007) Evaluation of dynamic loads on a skew box girder continuous bridge. Part I: field test and modal analysis. Eng Struct 29:1052–1063

    Article  Google Scholar 

  15. Jin SS, Cho S, Jung HJ (2015) Adaptive reference updating for vibration-based structural health monitoring under varying environmental conditions. Comput Struct 158:211–224

    Article  Google Scholar 

  16. Wu B, Lu H, Chen B, Gao Z (2017) Study on finite element model updating in highway bridge static loading test using spatially-distributed optical fiber sensors. Sensors 17:1657

    Article  Google Scholar 

  17. Minshui H, Hongping Z (2008) Finite element model updating of bridge structures based on sensitivity analysis and optimization algorithm. Wuhan Univ J Natural Sci 13:87–92

    Article  Google Scholar 

  18. Tran-Ngoc H, Khatir S, De Roeck G, Bui-Tien T, Nguyen-Ngoc L, Abdel-Wahab M (2018) Model updating for Nam O Bridge using particle swarm optimization algorithm and genetic algorithm. Sensors. 18:4131

    Article  Google Scholar 

  19. Deng L, Cai C (2009) Bridge model updating using response surface method and genetic algorithm. J Bridge Eng 15:553–564

    Article  Google Scholar 

  20. Rao ARM, Lakshmi K, Venkatachalam D (2012) Damage diagnostic technique for structural healthmonitoring using POD and self adaptive differential evolution algorithm. Comput Struct 106:228–244

    Google Scholar 

  21. Feng D, Feng MQ (2015) Model updating of railway bridge using in situ dynamic displacement measurement under trainloads. J Bridge Eng 20:04015019

    Article  Google Scholar 

  22. El-Borgi S, Smaoui H, Cherif F, Bahlous S, Ghrairi A (2004) Modal identification and finite element model updating of a reinforced concrete bridge. Emirates J Eng Res 9:29–34

    Google Scholar 

  23. Marchand B, Chamoin L, Rey C (2019) Parameter identification and model updating in the context of nonlinear mechanical behaviors using a unified formulation of the modified Constitutive Relation Error concept. Comput Methods Appl Mech Eng 345:1094–1113

    Article  MathSciNet  Google Scholar 

  24. Goller B, Pradlwarter HJ, Schueller GI (2009) Robust model updating with insufficient data. Comput Methods Appl Mech Eng 198(37–40):3096–3104

    Article  Google Scholar 

  25. Bayraktar A, Altunisik AC, Sevim B, Turker T (2010) Finite element model updating of Kömürhan highway bridge based on experimental measurements. Smart Struct Syst 6:373–388

    Article  Google Scholar 

  26. Rao RV, Keesari HS, Oclon P, Taler J (2020) An adaptive multi-team perturbation-guiding Jaya algorithm for optimization and its applications. Eng Comput 36(1):391–419

    Article  Google Scholar 

  27. Rao RV, Saroj A (2017) A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm Evol Comput 37:1–26

    Article  Google Scholar 

  28. Kiran MS (2015) TSA: Tree-seed algorithm for continuous optimization. Expert Syst Appl 42(19):6686–6698

    Article  Google Scholar 

  29. Khatir S, Boutchicha D, Le Thanh C, Tran-Ngoc H, Nguyen TN, Abdel-Wahab M (2020) Improved ANN technique combined with Jaya algorithm for crack identification in plates using XIGA and experimental analysis. Theor Appl Fract Mech 2020:102554

    Article  Google Scholar 

  30. Barshandeh S, Haghzadeh M (2020) A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Eng Comput 2020:1–44

    Google Scholar 

  31. Khatir S, Khatir T, Boutchicha D, Le Thanh C, Tran-Ngoc H, Bui TQ, Capozucca R, Abdel-Wahab M (2020) An efficient hybrid TLBO-PSO-ANN for fast damage identification in steel beam structures using IGA. Smart Struct Syst 25(5):605–617

    Google Scholar 

  32. Tran-Ngoc H, He L, Reynders E, Khatir S, Le-Xuan T, De Roeck G, Bui-Tien T, Wahab MA (2020) An efficient approach to model updating for a multispan railway bridge using orthogonal diagonalization combined with improved particle swarm optimization. J Sound Vibr 2020:115315

    Article  Google Scholar 

  33. Tran-Ngoc H, Khatir S, De Roeck G, Bui-Tien T, Wahab MA (2020) Damage assessment in beam-like structures using Cuckoo Search Algorithm and experimentally measured data. In: Proceedings of the 13th international conference on damage assessment of structures (pp. 380–385). Springer, Singapore

  34. Khatir S, Wahab MA, Boutchicha D, Capozucca R, Khatir T (2018) Optimization of IGA parameters based on beam structure using Cuckoo Search algorithm. In: Numerical modelling in engineering (pp 380–389). Springer, Singapore

  35. Yildiz AR (2013) Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int J Adv Manuf Technol 64:55–61

    Article  Google Scholar 

  36. Xu H, Liu J, Lu Z (2016) Structural damage identification based on cuckoo search algorithm. Adv Struct Eng 19:849–859

    Article  Google Scholar 

  37. Tran-Ngoc H, Khatir S, De Roeck G, Bui-Tien T, Wahab MA (2019) An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm. Eng Struct 199:109637

    Article  Google Scholar 

  38. Tran-Ngoc H, Khatir S, Ho-Khac H, De Roeck G, Bui-Tien T, Wahab MA (2020) Efficient Artificial neural networks based on a hybrid metaheuristic optimization algorithm for damage detection in laminated composite structures. Compos Struct 2020:113339

    Google Scholar 

  39. Marichelvam MK, Prabaharan T, Yang XS (2014) Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Appl Soft Comput 19:93–101

    Article  Google Scholar 

  40. Mohapatra P, Chakravarty S, Dash PK (2015) An improved cuckoo search based extreme learning machine for medical data classification. Swarm Evol Comput 24:25–49

    Article  Google Scholar 

  41. Zhou Y, Zheng H, Luo Q, Wu J (2013) An improved cuckoo search algorithm for solving planar graph coloring problem. Appl Math Inf Sci 7(2):785

    Article  MathSciNet  Google Scholar 

  42. Aggestam E, Nielsen JC (2019) Multi-objective optimisation of transition zones between slab track and ballasted track using a genetic algorithm. J Sound Vib 446:91–112

    Article  Google Scholar 

  43. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210–214), IEEE

  44. Valian E, Tavakoli S, Mohanna S, Haghi A (2013) Improved cuckoo search for reliability optimization problems. Comput Ind Eng 64:459–468

    Article  Google Scholar 

  45. Bui T, He L, De Roeck G (2012) Ambient vibration test of the Guadalquivir railway Bridge. Smart Struct 2012:221–236

    Google Scholar 

  46. Reynders E, Schevenels M, Roeck G (2014) A MATLAB toolbox for experimental and operational modal analysis. In: MACEC

Download references

Acknowledgements

The authors acknowledge the financial support of VLIR-UOS TEAM Project, VN2018TEA479A103, ‘Damage assessment tools for Structural Health Monitoring of Vietnamese infrastructures’ funded by the Flemish Government. The authors also acknowledge the assistance of colleagues from the Department of Civil Engineering, KU Leuven, Belgium in carrying out the measurement campaign of the Guadalquivir bridge. Moreover, the first author needs to acknowledge the financial supports from Ministry of Education and Training (MOET) under the project research “B2020-GHA-02” and Bijzonder Onderzoeksfonds (BOF) of Ghent University.

Author information

Authors and Affiliations

  1. Department of Electrical Energy, Metals, Mechanical Constructions and Systems, Faculty of Engineering and Architecture, Ghent University, 9000, Ghent, Belgium

    H. Tran-Ngoc

  2. Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam

    H. Tran-Ngoc, T. Le-Xuan & T. Bui-Tien

  3. Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam

    S. Khatir

  4. Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, 3001, Leuven, Belgium

    G. De Roeck

  5. Division of Computational Mechanics, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    M. Abdel Wahab

  6. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    M. Abdel Wahab

Authors
  1. H. Tran-Ngoc
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Khatir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. Le-Xuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. De Roeck
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. T. Bui-Tien
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. Abdel Wahab
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Abdel Wahab.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tran-Ngoc, H., Khatir, S., Le-Xuan, T. et al. Finite element model updating of a multispan bridge with a hybrid metaheuristic search algorithm using experimental data from wireless triaxial sensors. Engineering with Computers 38 (Suppl 3), 1865–1883 (2022). https://doi.org/10.1007/s00366-021-01307-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-021-01307-9

Keywords

Search

Navigation