With the structural health monitoring technique, several medium- and small-span bridges possessing the same or similar structural characteristics located in a local road network are simultaneously monitored within one cluster. Nevertheless, insufficient research has been performed on detecting the damage of all the bridges monitored within one cluster under time-varying environmental temperatures. To address this issue, a method is proposed to assess the damage in all the bridges within one cluster utilizing the residual between the cumulative distribution functions of strain monitoring data. First, an algorithm for reconstructing the strain monitoring dataset is introduced, effectively improving the computational efficiency at removing as much measured noise from a large amount of monitoring data as possible. Second, a cluster analysis algorithm considering the similarity among the strain monitoring data at different measured points is proposed to classify the strain monitoring data for all the bridges monitored within one cluster. Different classes of strain monitoring data are established by identifying similar probability distribution patterns. Third, for each class, a damage detection index is proposed based on the residual between the cumulative distribution functions of strain monitoring data. All the bridges monitored within one cluster are acted upon by similar environmental temperatures during the same monitoring period; hence, the effects of the environmental temperature on the proposed index are mitigated indirectly during the same monitoring period. Thus, the proposed index is implemented to effectively detect the damage in all the bridges belonging to each class. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation and with strain monitoring data obtained from actual bridges.
OuJLiH. Structural health monitoring in mainland China: review and future trends. Struct Health Monit2010; 9(3): 219–231.
2.
AnnamdasVGMBhallaSSohCK. Applications of structural health monitoring technology in Asia. Struct Health Monit2017; 16(3): 324–346.
3.
VagnoliMRemenyte-PrescottRAndrewsJ. Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges. Struct Health Monit2018; 17(4): 971–1007.
4.
KooKYBrownjohnJMWListDI, et al. Structural health monitoring of the Tamar suspension bridge. Struct Control Hlth Monit2013; 20(4): 609–625.
5.
CuryACremonaC. Assignment of structural behaviours in long-term monitoring: application to a strengthened railway bridge. Struct Health Monit2012; 11(4): 422–441.
6.
LiHLiSOuJ, et al. Modal identification of bridges under varying environmental conditions: temperature and wind effects. Struct Control Hlth Monit2010; 17(5): 495–512.
7.
LiuYZhangS. Probabilistic baseline of finite element model of bridges under environmental temperature changes. Comput-Aided Civ Inf2017; 32(7): 581–598.
8.
SohnHDzwonczykMStraserEG, et al. An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge. Earthq Eng Struct D1999; 28(8): 879–897.
9.
CaoYYimJZhaoY, et al. Temperature effects on cable stayed bridge using health monitoring system: a case study. Struct Health Monit2011; 10(5): 523–537.
10.
XiaoFHulseyJLBalasubramanianR. Fiber optic health monitoring and temperature behavior of bridge in cold region. Struct Control Hlth Monit2017; 24(11): e2020.
11.
CatbasFNSusoyMFrangopolDM. Structural health monitoring and reliability estimation: long span truss bridge application with environmental monitoring data. Eng Struct2008; 30(9): 2347–2359.
12.
XuYLChenBNgCL, et al. Monitoring temperature effect on a long suspension bridge. Struct Control Hlth Monit2010; 17(6): 632–653.
13.
PeetersBDe RoeckG. One-year monitoring of the Z24-bridge: environmental effects versus damage events. Earthq Eng Struct D2001; 30(2): 149–171.
14.
MaeckJPeetersBDe RoeckG. Damage identification on the Z24-bridge using vibration monitoring. Smart Mater Struct2001; 10(3): 512–517.
15.
MeruaneVHeylenW. Structural damage assessment under varying temperature conditions. Struct Health Monit2012; 11(3): 345–357.
16.
GuJGulMWuX. Damage detection under varying temperature using artificial neural networks. Struct Control Hlth Monit2017; 24(11): e1998.
17.
ReyndersEWurstenGDe RoeckG. Output-only structural health monitoring in changing environmental conditions by means of nonlinear system identification. Struct Health Monit2014; 13(1): 82–93.
18.
XiaYChenBZhouXQ, et al. Field monitoring and numerical analysis of Tsing Ma Suspension Bridge temperature behavior. Struct Control Hlth Monit2013; 20(4): 560–575.
19.
KoJMChakKKWangJY, et al. Formulation of an uncertainty model relating modal parameters and environmental factors by using long-term monitoring data. In: Proceedings of smart structures and materials 2003: smart systems and nondestructive evaluation for civil infrastructures, San Diego, CA, 18 August 2003, pp. 298–307. Bellingham, WA: SPIE.
20.
OhCKSohnH. Damage diagnosis under environmental and operational variations using unsupervised support vector machine. J Sound Vib2009; 325(1): 224–239.
21.
YanAMKerschenGDe BoeP, et al. Structural damage diagnosis under varying environmental conditions – part I: a linear analysis. Mech Syst Signal Pr2005; 19(4): 847–864.
22.
YanAMKerschenGDe BoeP, et al. Structural damage diagnosis under varying environmental conditions – part II: local PCA for non-linear cases. Mech Syst Signal Pr2005; 19(4): 865–880.
23.
KullaaJ. Elimination of environmental influences from damage-sensitive features in a structural health monitoring system. In: Proceedings of the first European workshop on structural health monitoring, Granada, 5–7 July 2006, pp. 742–749. Lancaster, PA: DESTech Publications.
24.
DeraemaekerAReyndersEDe RoeckG, et al. Vibration-based structural health monitoring using output-only measurements under changing environment. Mech Syst Signal Pr2008; 22(1): 34–56.
25.
KanungoTMountDMNetanyahuNS, et al. An efficient k-means clustering algorithm: analysis and implementation. IEEE T Pattern Anal2002; 24(7): 881–892.
26.
FallahianMKhoshnoudianFMeruaneV. Ensemble classification method for structural damage assessment under varying temperature. Struct Health Monit2018; 17(4): 747–762.
27.
WangHYajimaALiangRY, et al. Bayesian modeling of external corrosion in underground pipelines based on the integration of Markov chain Monte Carlo techniques and clustered inspection data. Comput-Aided Civ Inf2015; 30(4): 300–316.
28.
JohnstoneIMLuAY. On consistency and sparsity for principal components analysis in high dimensions. J Am Stat Assoc2009; 104: 682–693.
29.
JolliffeITCadimaJ. Principal component analysis: a review and recent developments. Philos T R Soc-A2016; 374(2065): 20150202.
30.
PanSHanYWeiS, et al. A model based on Gauss Distribution for predicting window behavior in building. Build Environ2019; 149: 210–219.
31.
YegnanAWilliamsonDGGraettingerAJ. Uncertainty analysis in air dispersion modeling. Environ Modell Softw2002; 17(7): 639–649.
32.
AmatoUCuomoVFontanaF, et al. Statistical predictability and parametric models of daily ambient temperature and solar irradiance: an analysis in the Italian climate. J Appl Meteorol Clim1989; 28(8): 711–721.
33.
NiYQZhangPYeXW, et al. Modeling of temperature distribution in a reinforced concrete supertall structure based on structural health monitoring data. Comput Concrete2011; 8(3): 293–309.
34.
WangYTaoWYanZ, et al. Uncertainty analysis of dynamic thermal rating based on environmental parameter estimation. EURASIP J Wirel Comm2018; 2018(1): 167.
35.
BrodyDCSyrokaJZervosM. Dynamical pricing of weather derivatives. Quant Financ2002; 2(3): 189–198.
36.
LivadaILykoudisSSantamourisM, et al. High ambient air temperature frequency distribution at Hellenic islands. Energ Buildings1998; 28(2): 119–126.
37.
ComanducciGMagalhãesFUbertiniF, et al. On vibration-based damage detection by multivariate statistical techniques: application to a long-span arch bridge. Struct Health Monit2016; 15(5): 505–524.
38.
HarmelRDRichardsonCWHansonCL, et al. Evaluating the adequacy of simulating maximum and minimum daily air temperature with the normal distribution. J Appl Meteorol Clim2002; 41(7): 744–753.
39.
KoJMNiYQ. Technology developments in structural health monitoring of large-scale bridges. Eng Struct2005; 27(12): 1715–1725.
40.
PianosiFWagenerT. A simple and efficient method for global sensitivity analysis based on cumulative distribution functions. Environ Modell Softw2015; 67: 1–11.
41.
ValverdeGSaricATTerzijaV. Stochastic monitoring of distribution networks including correlated input variables. IEEE T Power Syst2012; 28(1): 246–255.
42.
ClarkDVoBN. Convergence analysis of the Gaussian mixture PHD filter. IEEE T Signal Proces2007; 55(4): 1204–1212.
43.
ValverdeGSaricATTerzijaV. Probabilistic load flow with non-Gaussian correlated random variables using Gaussian mixture models. IET Gener Transm Dis2012; 6(7): 701–709.
44.
LoJ. Finite-dimensional sensor orbits and optimal nonlinear filtering. IEEE T Inform Theory1972; 18(5): 583–588.
45.
JiaoYLiuHWangX, et al. Temperature effect on mechanical properties and damage identification of concrete structure. Adv Mater Sci Eng2014; 2014: 191360.
