The use of structural health monitoring (SHM) systems on a regular basis is critical to achieve early damage detection, avoid unpredicted failures, and perform cost-effective maintenance planning. The main objective of this work is to present a model-based Damage Detection Framework for truss structural systems that uses output-only vibration measurements. Model-based methods provide much more comprehensive information about the condition of the monitored system than the data-driven and also allow the prediction of the location and level of damage. The measured vibration response of a healthy structural system under operational vibrations is employed to tune a parameterized FE model using state-of-the-art FE model updating techniques to obtain an optimal numerical model of the structural system. Based on the optimal FE model, a set of damaged FE models is generated for selected damage scenarios. A damage approximation approach that represents local damage with uniform stiffness reduction is also presented. In the Damage Detection Framework, the vibration data records for both the “healthy” and the “damaged” structure and the results from multiple numerical analysis on the “healthy” and the “damaged” FE models are used. The transmittance functions for the “healthy” and “damaged” states of the structure and the FE models are derived to calculate the damage indicators. Using these indicators, potentially damaged structural members are identified, grouped, and compared to finally locate the specific damaged member. The proposed framework provides both accurate damage localization and damage quantification using a limited number of sensors for unknown input excitation. Herein, the case study used is a laboratory steel truss bridge.
CawleyP. Structural health monitoring: closing the gap between research and industrial deployment. Structural Health Monitoring2018; 17(5): 1225–1244. DOI: 10.1177/1475921717750047
2.
DoeblingSWFarrarCRPrimeMB, et al.Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review. US: Los Alamos National Lab, 1996.
3.
SohnHFarrarCRHemezF, et al. “A review of structural health monitoring literature 1996 – 2001.” In: Third World Conference on Structural Control, December 2002, Como, Italy, March 2003, pp. 1–7. LA-13976-MS.
4.
FanWQiaoPTanPW. Vibration-based damage identification methods: a review and comparative study. Structural Health Monitoring2011; 1(2): 83–111. DOI: 10.1088/0964-1726/1/2/002
5.
AvciOAbdeljaberOKiranyazS, et al.A review of vibration-based damage detection in civil structures: from traditional methods to machine learning and deep learning applications. Mechanical Systems and Signal Processing2021; 147: 107077. DOI: 10.1016/j.ymssp.2020.107077
6.
DoeblingSWFarrarCPrimeMB. A summary review of vibration-based damage identification methods. The Shock and Vibration Digest1998; 30: 91–105.
7.
LiangRYHuJChoyF. Theoretical study of crack‐induced eigenfrequency changes on beam structures. J Eng Mech1992; 118(2): 384–396.
8.
PatilDPMaitiSK. Detection of multiple cracks using frequency measurements. Eng Fracture Mech2003; 70(12): 1553–1572. DOI: 10.1016/S0013-7944(02)00121-2
9.
ReyndersEWurstenGDe RoeckG. Output-only structural health monitoring in changing environmental conditions by means of nonlinear system identification. Structural Health Monitoring2013; 13(1): 82–93. DOI: 10.1177/1475921713502836
10.
ChangK-CKimC-W. Modal-parameter identification and vibration-based damage detection of a damaged steel truss bridge. Engineering Structures2016; 122: 156–173. DOI: 10.1016/j.engstruct.2016.04.057
11.
NavabianNBozorgnasabMTaghipourR, et al.Damage identification in plate-like structure using mode shape derivatives. Archive of Applied Mechanics2016; 86(5): 819–830. DOI: 10.1007/s00419-015-1064-x
12.
AllemangRJ. The modal assurance criterion - Twenty years of use and abuse. Sound and Vibration2003; 37(8): 14–21.
13.
FarahaniRVPenumaduD. Damage identification of a full-scale five-girder bridge using time-series analysis of vibration data. Engineering Structures2016; 115: 129–139. DOI: 10.1016/j.engstruct.2016.02.008
14.
GeorgioudakisMPlevrisV. A combined modal correlation criterion for structural damage identification with noisy modal data manolis. Journal of Sound and Vibration2018; 2018: 3183067, https://www.hindawi.com/journals/ace/2018/3183067/
15.
KurataMLynchJPLawKH, et al.Bayesian model updating approach for systematic damage detection of plate-type structures. Top Model Validation Uncertainty Quantification, Volume 42012; 4: 85–94. DOI: 10.1007/978-1-4614-2431-4_9
16.
ArgyrisCPapadimitriouCPanetsosP, et al.Bayesian model-updating using features of modal data: application to the Metsovo bridge. J Sensor Actuator Networks2020; 9(2): 27. DOI: 10.3390/JSAN9020027
17.
Riasat AzimMGülM. Damage detection of steel-truss railway bridges using operational vibration data. J Struct Eng2020; 146(3): 04020008. DOI: 10.1061/(asce)st.1943-541x.0002547
18.
MaiaNMMSilvaJMMAlmasEAM, et al.Damage detection in structures: from mode shape to frequency response function methods. Mechanical Systems and Signal Processing2003; 17(3): 489–498. DOI: 10.1006/mssp.2002.1506
19.
TrendafilovaICartmellMPOstachowiczW. Vibration-based damage detection in an aircraft wing scaled model using principal component analysis and pattern recognition. Journal of Sound and Vibration2008; 313(3–5): 560–566. DOI: 10.1016/j.jsv.2007.12.008
20.
Viet HaNGolinvalJ-C. Damage localization in linear-form structures based on sensitivity investigation for principal component analysis. Journal of Sound and Vibration2010; 329(21): 4550–4566. DOI: 10.1016/j.jsv.2010.04.032
21.
SakarisCSSakellariouJSFassoisSD. Random-vibration-based damage detection and precise localization on a lab-scale aircraft stabilizer structure via the generalized functional model based method. Struct Health Monit Int J2017; 16(5): 594–610. DOI: 10.1177/1475921717707903
22.
SundaresanMJGhoshalALiJ, et al.Experimental damage detection on a wing panel using vibration deflection shapes. Struct Health Monit2003; 2(3): 243–256. DOI: 10.1177/1475921703036047
23.
XiangJ-WMatsumotoTLongJ-Q, et al.Identification of damage locations based on operating deflection shape. Nondestructive Test Eval2013; 28(2): 166–180. DOI: 10.1080/10589759.2012.716437
24.
BeskhyrounSWegnerLDSparlingBF. New methodology for the application of vibration-based damage detection techniques. Struct Control Health Monit2012; 19: 632–649. DOI: 10.1002/stc
25.
ZhangHSchulzMJNaserA, et al.Structural Health Monitoring using transmittance functions. Mech Syst Signal Process1999; 13(5): 765–787.
26.
ChesnéSDeraemaekerA. Damage localization using transmissibility functions: A critical review. Mech Syst Signal Process2013; 38(2): 569–584. DOI: 10.1016/j.ymssp.2013.01.020
27.
PanCYuL. Sparse regularization-based damage detection in a bridge subjected to unknown moving forces. J Civil Struct Health Monit2019; 9(3): 425–438. DOI: 10.1007/s13349-019-00343-w
28.
KopsaftopoulosFPFassoisSD. A vibration model residual-based sequential probability ratio test framework for structural health monitoring. Struct Health Monit2015; 14(4): 359–381. DOI: 10.1177/1475921715580499
29.
SakellariouJSFassoisSDSakarisCS. IWSHM 2017: vibration-based damage localization and estimation via the stochastic functional model-based method: an overview. Struct Health Monit2018; 17(6): 1335–1348. DOI: 10.1177/1475921718793577
30.
AravanisT-CSakellariouJFassoisS. On the functional model-based method for vibration-based robust damage detection: versions and experimental assessment. Struct Health Monit2020; 20: 456–474. DOI: 10.1177/1475921720930206
31.
PoulimenosAGSakellariouJS. A transmittance-based methodology for damage detection under uncertainty: an application to a set of composite beams with manufacturing variability subject to impact damage and varying operating conditions. Struct Health Monit2019; 18(1): 318–333. DOI: 10.1177/1475921718779190
32.
MattsonSGPanditSM. Statistical moments of autoregressive model residuals for damage localisation. Mech Syst Signal Process2006; 20(3): 627–645. DOI: 10.1016/j.ymssp.2004.08.005
33.
Peter CardenEBrownjohnJMW. ARMA modelled time-series classification for structural health monitoring of civil infrastructure. Mech Syst Signal Process2008; 22(2): 295–314. DOI: 10.1016/j.ymssp.2007.07.003
34.
GiagopoulosDArailopoulosADertimanisV, et al.Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating. Struct Health Monit2019; 18(4): 1189–1206. DOI: 10.1177/1475921718790188
35.
SeventekidisPGiagopoulosDArailopoulosA, et al.Structural Health Monitoring using deep learning with optimal finite element model generated data. Mech Syst Signal Process2020; 145: 106972. DOI: 10.1016/j.ymssp.2020.106972
36.
MiguelLFFMiguelLFFKaminskiJ, et al.Damage detection under ambient vibration by harmony search algorithm. Expert Syst Appl2012; 39(10): 9704–9714. DOI: 10.1016/j.eswa.2012.02.147
37.
GulMCatbasFN. Damage assessment with ambient vibration data using a novel time series analysis methodology. J Struct Eng2011; 137(12): 1518–1526. DOI: 10.1061/(asce)st.1943-541x.0000366
38.
GoiYKimC-W. Damage detection of a truss bridge utilizing a damage indicator from multivariate autoregressive model. J Civil Struct Health Monit2017; 7(2): 153–162. DOI: 10.1007/s13349-017-0222-y
39.
MousaviAAZhangCMasriSF, et al.Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study. Struct Health Monit2021; 21(0): 887–912. DOI: 10.1177/14759217211013535
40.
GiagopoulosDArailopoulosA. Computational framework for model updating of large scale linear and nonlinear finite element models using state of the art evolution strategy. Comput Structures2017; 192: 210–232. DOI: 10.1016/j.compstruc.2017.07.004
41.
GiagopoulosDArailopoulosA. Computational Framework for Model Updating of Large Scale Linear and Nonlinear Finite Element Models using State of the Art Evolution Strategy, Computers and Structures, 192: 210–232, 2017
42.
SampaioRPCMaiaNMMAlmeidaRAB, et al.A simple damage detection indicator using operational deflection shapes. Mech Syst Signal Process2016; 72-73(73): 629–641. DOI: 10.1016/j.ymssp.2015.10.023
43.
HGrafe. Review of Frequency Response Function Updating Methods. Technical Report No. 1.01, BRITE-URANUS BRE2-CT94-0946., (1995, May)
44.
HadjidoukasPEAngelikopoulosPPapadimitriouC, et al.Π4U: a high performance computing framework for Bayesian uncertainty quantification of complex models. J Comput Phys2015; 284: 1–21.
45.
HadjidoukasPELappasEDimakopoulosVV. A runtime library for platform-independent task parallelism. In: Proceedings - 20th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2012, Munich, Germany, 15–17 Febraury 2012: 229–236, no. grant 100230. DOI: 10.1109/PDP.2012.89
46.
WordenKBurrowsAP. Optimal sensor placement for fault detection. Eng Structures2001; 23(8): 885–901. DOI: 10.1016/S0141-0296(00)00118-8
