Please wait a minute...
Frontiers of Structural and Civil Engineering

ISSN 2095-2430

ISSN 2095-2449(Online)

CN 10-1023/X

邮发代号 80-968

2019 Impact Factor: 1.68

Frontiers of Structural and Civil Engineering  2022, Vol. 16 Issue (12): 1552-1564   https://doi.org/10.1007/s11709-022-0903-4
  本期目录
Damage detection in beam-like structures using static shear energy redistribution
Xi PENG1,2, Qiuwei YANG1,2()
1. School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China
2. Engineering Research Center of Industrial Construction in Civil Engineering of Zhejiang, Ningbo University of Technology, Ningbo 315211, China
 全文: PDF(3054 KB)   HTML
Abstract

In this study, a static shear energy algorithm is presented for the damage assessment of beam-like structures. According to the energy release principle, the strain energy of a damaged element suddenly changes when structural damage occurs. Therefore, the change in the static shear energy is employed to determine the damage locations in beam-like structures. The static shear energy is derived from the spectral factorization of the elementary stiffness matrix and structural deflection variation. The advantage of using shear energy as opposed to total energy is that only a few deflection data points of the beam structure are required during the process of damage identification. Another advantage of the proposed approach is that damage detection can be performed without establishing a structural finite-element model in advance. The proposed technique is first validated using a numerical example with single, multiple, and adjacent damage scenarios. A channel steel beam and rectangular concrete beam are employed as experimental cases to further verify the proposed approach. The results of the simulation and experiment examples indicate that the proposed algorithm provides a simple and effective method for defect localization in beam-like structures.

Key wordsdamage detection    beam structure    strain energy    static displacement variation    energy damage index
收稿日期: 2022-06-21      出版日期: 2023-01-16
Corresponding Author(s): Qiuwei YANG   
 引用本文:   
. [J]. Frontiers of Structural and Civil Engineering, 2022, 16(12): 1552-1564.
Xi PENG, Qiuwei YANG. Damage detection in beam-like structures using static shear energy redistribution. Front. Struct. Civ. Eng., 2022, 16(12): 1552-1564.
 链接本文:  
https://academic.hep.com.cn/fsce/CN/10.1007/s11709-022-0903-4
https://academic.hep.com.cn/fsce/CN/Y2022/V16/I12/1552
Fig.1  
Fig.2  
Fig.3  
damage case element number stiffness reduction
scenario 1 10 10%
scenario 2 10 20%
scenario 3 10 30%
scenario 4 10 40%
scenario 5 6, 15 10%, 20%
scenario 6 6, 15 30%, 40%
Tab.1  
element number case 1 case 2 case 3 case 4
1 0.1532 0.3446 0.5908 0.9190
2 0.1532 0.3446 0.5908 0.9190
3 0.1532 0.3446 0.5908 0.9190
4 0.1532 0.3446 0.5908 0.9190
5 0.1532 0.3446 0.5908 0.9190
6 0.1532 0.3446 0.5908 0.9190
7 0.1532 0.3446 0.5908 0.9190
8 0.1532 0.3446 0.5908 0.9190
9 0.1532 0.3446 0.5908 0.9190
10 0.0051 0.0115 0.0196 0.0305
11 −0.1384 −0.3113 −0.5337 −0.8301
12 −0.1384 −0.3113 −0.5337 −0.8301
13 −0.1384 −0.3113 −0.5337 −0.8301
14 −0.1384 −0.3113 −0.5337 −0.8301
15 −0.1384 −0.3113 −0.5337 −0.8301
16 −0.1384 −0.3113 −0.5337 −0.8301
17 −0.1384 −0.3113 −0.5337 −0.8301
18 −0.1384 −0.3113 −0.5337 −0.8301
19 −0.1384 −0.3113 −0.5337 −0.8301
20 −0.1384 −0.3113 −0.5337 −0.8301
Tab.2  
Fig.4  
Fig.5  
Fig.6  
Fig.7  
damage case mean of the damage indices for elements 1–9 ( Δ Ω¯1 9) standard deviation of the damage indices for elements 1–9 (σ1 9) ΔΩ10 ΔΩ¯ 19 3?σ1 9
scenario 1 0.1478 0.0423 0.0455 > 0
scenario 2 0.3468 0.0743 0.0451 > 0
scenario 3 0.5948 0.0862 0.3322 > 0
scenario 4 0.9288 0.0879 0.6326 > 0
Tab.3  
damage case mean of the damage indices for elements 11–20 ( Δ Ω¯11 20) standard deviation of the damage indices for elements 11–20 (σ11 20) ΔΩ10 ΔΩ¯ 11 20 3?σ11 20
scenario 1 −0.1305 0.0529 −0.053 < 0
scenario 2 −0.32 0.0783 0.164 > 0
scenario 3 −0.5355 0.0441 0.4072 > 0
scenario 4 −0.8392 0.0649 0.6771 > 0
Tab.4  
damage case mean of the damage indices for elements 1–5 ( Δ Ω¯1 5) standard deviation of the damage indices for elements 1–5 (σ1 5) ΔΩ6 ΔΩ¯ 15 3?σ1 5
case 5 0.3892 0.009 0.1512 > 0
case 6 1.3926 0.0478 0.7871 > 0
Tab.5  
damage case mean of the damage indices for elements 7–14 ( Δ Ω¯7 14) standard deviation of the damage indices for elements 7–14 (σ7 14) ΔΩ6 ΔΩ¯ 714 3?σ7 14
case 5 −0.0176 0.0558 0.0612 > 0
case 6 −0.1671 0.0867 0.3692 > 0
Tab.6  
damage case mean of the damage indices for elements 7–14 ( Δ Ω¯7 14) standard deviation of the damage indices for elements 7–14 (σ7 14) ΔΩ15 ΔΩ¯ 714 3?σ7 14
case 5 −0.0176 0.0558 0.0109 > 0
case 6 −0.1671 0.0867 0.2392 > 0
Tab.7  
Fig.8  
damage case mean of the damage indices for elements 16–20 ( Δ Ω¯16 20) standard deviation of the damage indices for elements 16–20 (σ16 20) ΔΩ15 ΔΩ¯ 16 20 3?σ16 20
case 5 −0.3641 0.165 0.1187 > 0
case 6 −1.0845 0.0233 0.3482 > 0
Tab.8  
Fig.9  
number mean of the damage indices for elements 1–11 ( Δ Ω¯1 11) standard deviation of the damage indices for elements 1–11 (σ1 11) ΔΩ12 ΔΩ¯ 111 3?σ1 11
element 12 0.3447 0.0408 0.0969 > 0
Tab.9  
number mean of the damage indices for elements 14−20 ( Δ Ω¯14 20) standard deviation of the damage indices for elements 14−20 (σ14 20) ΔΩ13 ΔΩ¯ 14 20 3?σ14 20
element 13 −0.5098 0.0250 0.1164 > 0
Tab.10  
segment number (left-to-right) damage index
1 0.0015
2 0.0014
3 −0.0002
4 −0.0014
5 −0.0013
Tab.11  
segment number damage index
1 0.6853
2 0.6973
3 0.7267
4 −0.2040
5 −0.1787
6 −0.5200
7 −0.5960
8 −0.6107
Tab.12  
number mean of the damage indices for elements 1−3 ( Δ Ω¯1 3) standard deviation of the damage indices for elements 1−3 (σ1 3) ΔΩ4 ΔΩ¯ 13 3?σ1 3
segment 4 7.0311 × 10−5 2.1264 × 10−6 8.4332 × 10−5 > 0
Tab.13  
number mean of the damage indices for elements 6–8 ( Δ Ω¯6 8) standard deviation of the damage indices for elements 6–8 (σ6 8) ΔΩ5 ΔΩ¯ 68 3?σ6 8
segment 5 −5.7556 × 10−5 4.8668 × 10−6 2.5088 × 10−5 > 0
Tab.14  
1 S W Doebling, C R Farrar, M B Prime. A summary review of vibration-based damage identification methods. Shock and Vibration Digest, 1998, 30(2): 91–105
https://doi.org/10.1177/058310249803000201
2 E P Carden, P Fanning. Vibration based condition monitoring: A review. Structural Health Monitoring, 2004, 3(4): 355–377
https://doi.org/10.1177/1475921704047500
3 A PrinarisS AlampalliM Ettouney. Review of Remote Sensing for Condition Assessment and Damage Identification after Extreme Loading Conditions. Vancouver: Structures Congress 2008, ASCE, 2008
4 G Liu, L Wang, J K Liu, Y M Chen, Z R Lu. Identification of an airfoil-store system with cubic nonlinearity via enhanced response sensitivity approach. AIAA Journal, 2018, 56(12): 4977–4987
https://doi.org/10.2514/1.J057195
5 G Liu, L Wang, W L Luo, J K Liu, Z R Lu. Parameter identification of fractional order system using enhanced response sensitivity approach. Communications in Nonlinear Science and Numerical Simulation, 2019, 67(2): 492–505
https://doi.org/10.1016/j.cnsns.2018.07.026
6 A Messina, J E Williams, T Contursi. Structural damage detection by a sensitivity and statistical-based method. Journal of Sound and Vibration, 1998, 216(5): 791–808
https://doi.org/10.1006/jsvi.1998.1728
7 C R Ashokkumar, N G R Iyengar. Partial eigenvalue assignment for structural damage mitigation. Journal of Sound and Vibration, 2011, 330(1): 9–16
https://doi.org/10.1016/j.jsv.2010.08.024
8 Z Yang, L Wang. Structural damage detection by changes in natural frequencies. Journal of Intelligent Material Systems and Structures, 2010, 21(3): 309–319
https://doi.org/10.1177/1045389X09350332
9 Z Y Shi, S S Law, L M Zhang. Damage localization by directly using incomplete mode shapes. Journal of Engineering Mechanics, 2000, 126(6): 656–660
https://doi.org/10.1061/(ASCE)0733-9399(2000)126:6(656
10 P Ghannadi, S S Kourehli. Investigation of the accuracy of different finite element model reduction techniques. Structural Monitoring and Maintenance, 2018, 5(3): 417–428
11 P Ghannadi, S S Kourehli. Data-driven method of damage detection using sparse sensors installation by SEREPa. Journal of Civil Structural Health Monitoring, 2019, 9(4): 459–475
https://doi.org/10.1007/s13349-019-00345-8
12 P Ghannadi, S S Kourehli. An effective method for damage assessment based on limited measured locations in skeletal structures. Advances in Structural Engineering, 2021, 24(1): 183–195
https://doi.org/10.1177/1369433220947193
13 P Ghannadi, S S Kourehli, M Noori, W A Altabey. Efficiency of grey wolf optimization algorithm for damage detection of skeletal structures via expanded mode shapes. Advances in Structural Engineering, 2020, 23(13): 2850–2865
https://doi.org/10.1177/1369433220921000
14 P Ghannadi, S S Kourehli. Prediction of unmeasured mode shapes and structural damage detection using least squares support vector machine. Structural Monitoring and Maintenance, 2018, 5(3): 379–390
15 H P Zhu, L Li, X Q He. Damage detection method for shear buildings using the changes in the first mode shape slopes. Computers & Structures, 2011, 89(9−10): 733–743
https://doi.org/10.1016/j.compstruc.2011.02.014
16 Y Zhang, L Wang, Z Xiang. Damage detection by mode shape squares extracted from a passing vehicle. Journal of Sound and Vibration, 2012, 331(2): 291–307
https://doi.org/10.1016/j.jsv.2011.09.004
17 D Wu, S S Law. Model error correction from truncated modal flexibility sensitivity and generic parameters. I: Simulation. Mechanical Systems and Signal Processing, 2004, 18(6): 1381–1399
https://doi.org/10.1016/S0888-3270(03)00094-3
18 W Di, S S Law. Eigen-parameter decomposition of element matrices for structural damage detection. Engineering Structures, 2007, 29(4): 519–528
https://doi.org/10.1016/j.engstruct.2006.05.019
19 Q W Yang, J K Liu. Damage identification by the eigenparameter decomposition of structural flexibility change. International Journal for Numerical Methods in Engineering, 2009, 78(4): 444–459
https://doi.org/10.1002/nme.2494
20 Q W Yang. A new damage identification method based on structural flexibility disassembly. Journal of Vibration and Control, 2011, 17(7): 1000–1008
https://doi.org/10.1177/1077546309360052
21 S H Sung, H J Jung, H Y Jung. Damage detection for beam-like structures using the normalized curvature of a uniform load surface. Journal of Sound and Vibration, 2013, 332(6): 1501–1519
https://doi.org/10.1016/j.jsv.2012.11.016
22 Z B Yang, M Radzienski, P Kudela, W Ostachowicz. Fourier spectral-based modal curvature analysis and its application to damage detection in beams. Mechanical Systems and Signal Processing, 2017, 84: 763–781
https://doi.org/10.1016/j.ymssp.2016.07.005
23 J Xiang, T Matsumoto, Y Wang, Z Jiang. Detect damages in conical shells using curvature mode shape and wavelet finite element method. International Journal of Mechanical Sciences, 2013, 66: 83–93
https://doi.org/10.1016/j.ijmecsci.2012.10.010
24 A Khorram, F Bakhtiari-Nejad, M Rezaeian. Comparison studies between two wavelet based crack detection methods of a beam subjected to a moving load. International Journal of Engineering Science, 2012, 51: 204–215
https://doi.org/10.1016/j.ijengsci.2011.10.001
25 N Roveri, A Carcaterra. Damage detection in structures under traveling loads by Hilbert–Huang transform. Mechanical Systems and Signal Processing, 2012, 28: 128–144
https://doi.org/10.1016/j.ymssp.2011.06.018
26 F Cavadas, I F C Smith, J Figueiras. Damage detection using data-driven methods applied to moving-load responses. Mechanical Systems and Signal Processing, 2013, 39(1−2): 409–425
https://doi.org/10.1016/j.ymssp.2013.02.019
27 Z D Xu, M Liu, Z Wu, X Zeng. Energy damage detection strategy based on strain responses for long-span bridge structures. Journal of Bridge Engineering, 2011, 16(5): 644–652
https://doi.org/10.1061/(ASCE)BE.1943-5592.0000195
28 T H Yi, H N Li, H M Sun. Multi-stage structural damage diagnosis method based on “energy-damage” theory. Smart Structures and Systems, 2013, 12(3−4): 345–361
https://doi.org/10.12989/sss.2013.12.3_4.345
29 Y J Cha, O Buyukozturk. Structural damage detection using modal strain energy and hybrid multiobjective optimization. Computer-Aided Civil and Infrastructure Engineering, 2015, 30(5): 347–358
https://doi.org/10.1111/mice.12122
30 P Ghannadi, S S Kourehli. Structural damage detection based on MAC flexibility and frequency using moth-flame algorithm. Structural Engineering and Mechanics, 2019, 70(6): 649–659
31 P Ghannadi, S S Kourehli. Model updating and damage detection in multi-story shear frames using Salp Swarm Algorithm. Earthquakes and Structures, 2019, 17(1): 63–73
32 P GhannadiS S Kourehli. Multiverse optimizer for structural damage detection: Numerical study and experimental validation. Structural Design of Tall and Special Buildings, 2020, 29(13), e1777: 1–27
33 P Ghannadi, S S Kourehli. Efficiency of the slime mold algorithm for damage detection oflarge-scale structures. Structural Design of Tall and Special Buildings, 2022, e1967: 1–36
34 X Wang, N Hu, H Fukunaga, Z H Yao. Structural damage identification using static test data and changes in frequencies. Engineering Structures, 2001, 23(6): 610–621
https://doi.org/10.1016/S0141-0296(00)00086-9
35 M Sanayei, O Onipede. Assessment of structures using static test data. AIAA Journal, 1991, 29(7): 1174–1179
https://doi.org/10.2514/3.10720
36 M R Banan, M R Banan, K D Hjelmstad. Parameter estimation of structures from static response, I: Computational aspects. Journal of Structural Engineering, 1994, 120(11): 3243–3258
https://doi.org/10.1061/(ASCE)0733-9445(1994)120:11(3243
37 M R Banan, M R Banna, K D Hjelmstad. Parameter estimation of structures from static response, II: Numerical simulation studies. Journal of Structural Engineering, 1994, 120(11): 3259–3283
https://doi.org/10.1061/(ASCE)0733-9445(1994)120:11(3259
38 K D Hjelmstad, S Shin. Damage detection and assessment of structures from static response. Journal of Engineering Mechanics, 1997, 123(6): 568–576
https://doi.org/10.1061/(ASCE)0733-9399(1997)123:6(568
39 J H Chou, J Ghaboussi. Genetic algorithm in structural damage detection. Computers & Structures, 2001, 79(14): 1335–1353
https://doi.org/10.1016/S0045-7949(01)00027-X
40 F Bakhtiari-Nejad, A Rahai, A Esfandiari. A structural damage detection method using static noisy data. Engineering Structures, 2005, 27(12): 1784–1793
https://doi.org/10.1016/j.engstruct.2005.04.019
41 X Chen, H Zhu, C Chen. Structural damage identification using test static data based on grey system theory. Journal of Zhejiang University. Science A, 2005, 6(8): 790–796
https://doi.org/10.1631/jzus.2005.A0790
42 B Kouchmeshky, W Aquino, J C Bongard, H Lipson. Co-evolutionary algorithm for structural damage identification using minimal physical testing. International Journal for Numerical Methods in Engineering, 2007, 69(5): 1085–1107
https://doi.org/10.1002/nme.1803
43 M A B Abdo. Parametric study of using only static response in structural damage detection. Engineering Structures, 2012, 34: 124–131
https://doi.org/10.1016/j.engstruct.2011.09.027
44 M A B Abdo. Corrigendum to “Parametric study of using only static response in structural damage detection” [Engineering Structures 34 (2012) 124–131]. Engineering Structures, 2012, 35: 322
45 N T Le, D P Thambiratnam, A Nguyen, T H T Chan. A new method for locating and quantifying damage in beams from static deflection changes. Engineering Structures, 2019, 180: 779–792
https://doi.org/10.1016/j.engstruct.2018.11.071
46 Y Lu. Random beam structure damage identification method based on static measurement data and L1 regularization. Thesis for the Master’s Degree. Wuhan: Wuhan University of Technology, 2019 (in Chinese)
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed