TMYHJGCXB土木与环境工程学报Journal of Civil and Environmental Engineering2096-6717土木与环境工程学报编辑部中国重庆tmyhjgcxb-42-5-1152096-6717(2020)05-0115-1110.11835/j.issn.2096-6717.2020.109U446.2R桥梁健康监测2019年度研究进展State-of-the-art review of the bridge health monitoring in 2019单德山ShanDeshan
The bridge health monitoring system (BHMS)continuously measures and records the structural responses by using a variety of sensors and communication devices in the bridge operation process. The automatic analysis of signal data can be done effectively in the BHMS to fulfill the timely danger warning and safety assessment.So as to protect the normal operation of the structure, prolong the service life of the structure, and guide the management and maintenance decision of the bridge structure.As a new branch of bridge engineering, bridge health monitoring technology has gradually become a hot research field. For the sake of the more efficient application of health monitoring system in bridge engineering, this paper summarizes the current states of several representative BHMS techniques: the decision-making analysis for the BHMS implementation, sensor signal preprocessing, signal de-noising processing, modal parameter identification, finite element model updating, damage identification, condition prediction and assessment. Then, the related researches and applications of these key techniques during 2019 are summarized and discussed.Consequently, it is found that the pattern recognition technology and the machine learning method are more and more widely used in the current research of bridge health monitoring.
桥梁健康监测系统信号预处理模态参数识别有限元模型损伤识别bridge health monitoring system(BHMS)signal preprocessingmodal parameter identificationfinite element modeldamage identification国家自然科学基金51978577国家自然科学基金51678489国家重点研发计划2016YFC0802202国家重点基础研究发展计划2013CB0363中电建路桥集团资助科研项目SCMQ-201728-ZB国家自然科学基金(51978577、51678489);国家重点研发计划(2016YFC0802202);国家重点基础研究发展计划(2013CB0363);中电建路桥集团资助科研项目(SCMQ-201728-ZB)National Natural Science Foundation of China51978577National Natural Science Foundation of China51678489National Key R & D Program of China2016YFC0802202National Key Basic Research Program of China2013CB0363Science and Technology Project of Power ChinaSCMQ-201728-ZBNational Natural Science Foundation of China (No. 51978577, 51678489); National Key R & D Program of China (No. 2016YFC0802202); National Key Basic Research Program of China (No. 2013CB0363); Science and Technology Project of Power China (No. SCMQ-201728-ZB)
SUNL MSHANGZ QXIAYDevelopment and prospect of bridge structural health monitoring in the context of big data20193211120
SUN L M, SHANG Z Q, XIA Y. Development and prospect of bridge structural health monitoring in the context of big data[J]. China Journal of Highway and Transport, 2019, 32(11):1-20. (in Chinese)
GATTIMStructural health monitoring of an operational bridge:A case study201919520020910.1016/j.engstruct.2019.05.102
GATTI M. Structural health monitoring of an operational bridge:A case study[J]. Engineering Structures, 2019, 195:200-209.
NEVESA CLEANDERJGONZÁLEZIAn approach to decision-making analysis for implementation of structural health monitoring in bridges2019266e235210.1002/stc.2352
NEVES A C, LEANDER J, GONZÁLEZ I, et al. An approach to decision-making analysis for implementation of structural health monitoring in bridges[J]. Structural Control and Health Monitoring, 2019, 26(6):e2352.
CHENHZHANGB ZArchitecture design and implementation suggestions of health monitoring system for Xiahe Bridge2019152172175
CHEN H, ZHANG B Z. Architecture design and implementation suggestions of health monitoring system for Xiahe Bridge[J]. Journd of Highway and Transportation Research and Development, 2019, 15(2):172-175.
GUJ SGeneral design of health monitoring system for the Yellow River bridge of Shijiazhuang-Jinan passenger dedicated line20193645459
GU J S. General design of health monitoring system for the Yellow River bridge of Shijiazhuang-Jinan passenger dedicated line[J]. Journal of Railway Engineering Society, 2019, 36(4):54-59.(in Chinese)
FUY GPENGCGOMEZFSensor fault management techniques for wireless smart sensor networks in structural health monitoring2019267e236210.1002/stc.2362
FU Y G, PENG C, GOMEZ F, et al. Sensor fault management techniques for wireless smart sensor networks in structural health monitoring[J]. Structural Control and Health Monitoring, 2019, 26(7):e2362.
XIANGYDUJBridge health monitoring system based on big data technology20202914448, 54
XIANG Y, DU J. Bridge health monitoring system based on big data technology[J]. Railway Computer Application, 2020, 29(1):44-48, 54. (in Chinese)
NIF TZHANGJNOORIM NDeep learning for data anomaly detection and data compression of a long-span suspension bridge202035768570010.1111/mice.12528
NI F T, ZHANG J, NOORI M N. Deep learning for data anomaly detection and data compression of a long-span suspension bridge[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(7)685-700
YANPStudy on EMD Wavelet correlation de-noising of bridge health monitoring sampling signals2019393204209
YAN P. Study on EMD Wavelet correlation de-noising of bridge health monitoring sampling signals[J]. Noise and Vibration Control, 2019, 39(3):204-209.(in Chinese)
CHENY GZHONGZ YModal decomposition of response signals for a bridge structure based on the improved EEMD201938102330
CHEN Y G, ZHONG Z Y. Modal decomposition of response signals for a bridge structure based on the improved EEMD[J]. Journal of Vibration and Shock, 2019, 38(10):23-30. (in Chinese)
LIUX YSHAND STANK XNear-fault pulse-like earthquake signals denoising algorithm based on complementary ensemble empirical mode decomposition20195955963
LIU X Y, SHAN D S, TAN K X. Near-fault pulse-like earthquake signals denoising algorithm based on complementary ensemble empirical mode decomposition[J]. Railway Engineering, 2019, 59(5):59-63. (in Chinese)
HUANGS XWANGX PLIC FData decomposition method combining permutation entropy and spectral substitution with ensemble empirical mode decomposition201913943845310.1016/j.measurement.2019.01.026
HUANG S X, WANG X P, LI C F, et al. Data decomposition method combining permutation entropy and spectral substitution with ensemble empirical mode decomposition[J]. Measurement, 2019, 139:438-453.
ZHANGE HSHAND SLIQNonlinear and non-stationary detection for measured dynamic signal from bridge structure based on adaptive decomposition and multiscale recurrence analysis201997130210.3390/app9071302
ZHANG E H, SHAN D S, LI Q. Nonlinear and non-stationary detection for measured dynamic signal from bridge structure based on adaptive decomposition and multiscale recurrence analysis[J]. Applied Sciences, 2019, 9(7):1302.
张二华.桥梁结构时变系统的张量子空间识别研究[D].成都: 西南交通大学, 2019.
ZHANG E H. Research on tensor subspace identification of time-varying system for bridge structures[D]. Chengdu: Southwest Jiaotong University, 2019. (in Chinese)
ALTUNISIKA CKALKANEOKURF YNon-destructive modal parameter identification of historical timber bridges using ambient vibration tests after restoration201914641142410.1016/j.measurement.2019.06.051
ALTUNISIK A C, KALKAN E, OKUR F Y, et al. Non-destructive modal parameter identification of historical timber bridges using ambient vibration tests after restoration[J]. Measurement, 2019, 146:411-424.
HUANGZSHAND SLIQComparison of operational modal analysis methods for long span cable-stayed bridge2019271144155
HUANG Z, SHAN D S, LI Q. Comparison of operational modal analysis methods for long span cable-stayed bridge[J]. Journal of Basic Science and Engineering, 2019, 27(1):144-155. (in Chinese)
ZHUY CAUS KBROWNJOHNJ M WBayesian operational modal analysis with buried modes201912124626310.1016/j.ymssp.2018.11.022
ZHU Y C, AU S K, BROWNJOHN J M W. Bayesian operational modal analysis with buried modes[J]. Mechanical Systems and Signal Processing, 2019, 121:246-263.
LIUJ LZHENGJ YWEIX JA combined method for instantaneous frequency identification in low frequency structures201919437038310.1016/j.engstruct.2019.05.057
LIU J L, ZHENG J Y, WEI X J, et al. A combined method for instantaneous frequency identification in low frequency structures[J]. Engineering Structures, 2019, 194:370-383.
MAOJ XWANGHFUY GAutomated modal identification using principal component and cluster analysis:Application to a long-span cable-stayed bridge20192610e2430
MAO J X, WANG H, FU Y G, et al. Automated modal identification using principal component and cluster analysis:Application to a long-span cable-stayed bridge[J]. Structural Control and Health Monitoring, 2019, 26(10):e2430.
CHENY GZHONGZ YIntelligent identification of the real mode for bridge structures2019323471479
CHEN Y G, ZHONG Z Y. Intelligent identification of the real mode for bridge structures[J]. Journal of Vibration Engineering, 2019, 32(3):471-479. (in Chinese)
HEMLIANGPLIL GAutomatic modal parameter identification based on improved two-stage FCM algorithm2019495940948
HE M, LIANG P, LI L G, et al. Automatic modal parameter identification based on improved two-stage FCM algorithm[J]. Journal of Southeast University (Natural Science Edition), 2019, 49(5):940-948. (in Chinese)
ZHOUX HSHAND STANK XComparison of deterministic subspace identification methods for model bridge shaking table test20193947381
ZHOU X H, SHAN D S, TAN K X, et al. Comparison of deterministic subspace identification methods for model bridge shaking table test[J]. Journal of Chang'an University (Natural Science Edition), 2019, 39(4):73-81.(in Chinese)
XUYBROWNJOHNJ M WHESTERDEnhanced sparse component analysis for operational modal identification of real-life bridge structures201911658560510.1016/j.ymssp.2018.07.026
XU Y, BROWNJOHN J M W, HESTER D. Enhanced sparse component analysis for operational modal identification of real-life bridge structures[J]. Mechanical Systems and Signal Processing, 2019, 116:585-605.
FERRARIRFROIODRIZZIEModel updating of a historic concrete bridge by sensitivity- and global optimization-based Latin Hypercube Sampling201917913916010.1016/j.engstruct.2018.08.004
FERRARI R, FROIO D, RIZZI E, et al. Model updating of a historic concrete bridge by sensitivity- and global optimization-based Latin Hypercube Sampling[J]. Engineering Structures, 2019, 179:139-160.
BAUTISTA-DECASTROÁSÁNCHEZ-APARICIOL JCARRASCO-GARCÍAPA multidisciplinary approach to calibrating advanced numerical simulations of masonry arch bridges201912933736510.1016/j.ymssp.2019.04.043
BAUTISTA-DE CASTROÁ, SÁNCHEZ-APARICIO L J, CARRASCO-GARCÍA P, et al. A multidisciplinary approach to calibrating advanced numerical simulations of masonry arch bridges[J]. Mechanical Systems and Signal Processing, 2019, 129:337-365.
BARTILSOND TJANGJSMYTHA WFinite element model updating using objective-consistent sensitivity-based parameter clustering and Bayesian regularization201911432834510.1016/j.ymssp.2018.05.024
BARTILSON D T, JANG J, SMYTH A W. Finite element model updating using objective-consistent sensitivity-based parameter clustering and Bayesian regularization[J]. Mechanical Systems and Signal Processing, 2019, 114:328-345.
HESTERDKOOKXUYBoundary condition focused finite element model updating for bridges201919810951410.1016/j.engstruct.2019.109514
HESTER D, KOO K, XU Y, et al. Boundary condition focused finite element model updating for bridges[J]. Engineering Structures, 2019, 198:109514.
YINTZHUH PAn efficient algorithm for architecture design of Bayesian neural network in structural model updating202035435437210.1111/mice.12492
YIN T, ZHU H P. An efficient algorithm for architecture design of Bayesian neural network in structural model updating[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(4)354-372
MAY PLIUY JLIUJMulti-scale finite element model updating of CFST composite truss bridge based on response surface method201932115161
MA Y P, LIU Y J, LIU J. Multi-scale finite element model updating of CFST composite truss bridge based on response surface method[J]. China Journal of Highway and Transport, 2019, 32(11):51-61. (in Chinese)
SHAND SGUX YLIZ HAffine-interval uncertainty updating of finite element model for cable-stayed bridge20193226776
SHAN D S, GU X Y, LI Z H, et al. Affine-interval uncertainty updating of finite element model for cable-stayed bridge[J]. China Journal of Highway and Transport, 2019, 32(2):67-76.(in Chinese)
SHAND SCHAIY HZHOUX HTension identification of suspenders with supplemental dampers for through and half-through arch bridges under construction201914530401826510.1061/(ASCE)ST.1943-541X.0002255
SHAN D S, CHAI Y H, ZHOU X H, et al. Tension identification of suspenders with supplemental dampers for through and half-through arch bridges under construction[J]. Journal of Structural Engineering, 2019, 145(3):04018265.
NGANJ WCAPRANIC CBAIYFull-field finite element model updating using Zernike moment descriptors for structures exhibiting localized mode shapes201912137338810.1016/j.ymssp.2018.11.027
NGAN J W, CAPRANI C C, BAI Y. Full-field finite element model updating using Zernike moment descriptors for structures exhibiting localized mode shapes[J]. Mechanical Systems and Signal Processing, 2019, 121:373-388.
金梦茹.基于结构响应向量与机器学习的损伤识别方法研究[D].广州: 华南理工大学, 2019.
JIN M R. Damage identification methods based on structural response vectors and machine learning algorithms[D]. Guangzhou: South China University of Technology, 2019. (in Chinese)
SENDERAZOKZHANGWOn the effectiveness of principal component analysis for decoupling structural damage and environmental effects in bridge structures201945728029810.1016/j.jsv.2019.06.003
SEN D, ERAZO K, ZHANG W, et al. On the effectiveness of principal component analysis for decoupling structural damage and environmental effects in bridge structures[J]. Journal of Sound and Vibration, 2019, 457:280-298.
DE ALMEIDA CARDOSORCURYABARBOSAFAutomated real-time damage detection strategy using raw dynamic measurements201919610936410.1016/j.engstruct.2019.109364
DE ALMEIDA CARDOSO R, CURY A, BARBOSA F. Automated real-time damage detection strategy using raw dynamic measurements[J]. Engineering Structures, 2019, 196:109364.
TRAN-NGOCHKHATIRSDE ROECKGAn efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm201919910963710.1016/j.engstruct.2019.109637
TRAN-NGOC H, KHATIR S, DE ROECK G, et al. An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm[J]. Engineering Structures, 2019, 199:109637.
MEIQ PGÜLMBOAYMIndirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis201911952354610.1016/j.ymssp.2018.10.006
MEI Q P, GÜL M, BOAY M. Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis[J]. Mechanical Systems and Signal Processing, 2019, 119:523-546.
LIUJ XCHENS HBERGÉSMDiagnosis algorithms for indirect structural health monitoring of a bridge model via dimensionality reduction202013610645410.1016/j.ymssp.2019.106454
LIU J X, CHEN S H, BERGÉS M, et al. Diagnosis algorithms for indirect structural health monitoring of a bridge model via dimensionality reduction[J]. Mechanical Systems and Signal Processing, 2020, 136:106454.
LOCKEWSYBRANDTJREDMONDLUsing drive-by health monitoring to detect bridge damage considering environmental and operational effects202046811508810.1016/j.jsv.2019.115088
LOCKE W, SYBRANDT J, REDMOND L, et al. Using drive-by health monitoring to detect bridge damage considering environmental and operational effects[J]. Journal of Sound and Vibration, 2020, 468:115088.
OSKOUIE ATAYLORTANSARIFMethod and monitoring approach for distributed detection of damage in multi-span continuous bridges201918938539510.1016/j.engstruct.2019.02.037
OSKOUI E A, TAYLOR T, ANSARI F. Method and monitoring approach for distributed detection of damage in multi-span continuous bridges[J]. Engineering Structures, 2019, 189:385-395.
RUBIOJ JKASHIWATLAITEERAPONGTMulti-class structural damage segmentation using fully convolutional networks201911210312110.1016/j.compind.2019.08.002
RUBIO J J, KASHIWA T, LAITEERAPONG T, et al. Multi-class structural damage segmentation using fully convolutional networks[J]. Computers in Industry, 2019, 112:103121.
DUNGC VSEKIYAHHIRANOSA vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks201910221722910.1016/j.autcon.2019.02.013
DUNG C V, SEKIYA H, HIRANO S, et al. A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks[J]. Automation in Construction, 2019, 102:217-229.
ZHANGX YWANGF ZYUHDamage detection method of bridge model based on curvature variation of bridge deck201938103340
ZHANG X Y, WANG F Z, YU H, et al. Damage detection method of bridge model based on curvature variation of bridge deck[J]. Journal of Chongqing Jiaotong University (Natural Science), 2019, 38(10):33-40.(in Chinese)
LI C L, LIN Z H, WANG F Y, et al. Accurate damage identification of three-span varying depth continuous girder bridge[J]. Journal of the China Railway Society, 2019, 41(12):96-105. (in Chinese)
JIANZ KLIUX JZHANGS XDamage identification in simply supported beam bridge based on strain index and D-S evidence theory2019345791799
JIAN Z K, LIU X J, ZHANG S X. Damage identification in simply supported beam bridge based on strain index and D-S evidence theory[J]. Journal of Experimental Mechanics, 2019, 34(5):791-799.(in Chinese)
WANGYTUMBEVAM DTHRALLA PPressure-activated adhesive tape pattern for monitoring the structural condition of steel bridges via digital image correlation2019268e2382
WANG Y, TUMBEVA M D, THRALL A P, et al. Pressure-activated adhesive tape pattern for monitoring the structural condition of steel bridges via digital image correlation[J]. Structural Control and Health Monitoring, 2019, 26(8):e2382.
ZHOUC YWUY TCUIG JComprehensive measurement techniques and multi-index correlative evaluation approach for structural health monitoring of highway bridges202015210736010.1016/j.measurement.2019.107360
ZHOU C Y, WU Y T, CUI G J, et al. Comprehensive measurement techniques and multi-index correlative evaluation approach for structural health monitoring of highway bridges[J]. Measurement, 2020, 152:107360.
SCOZZESEFRAGNILTUBALDIEModal properties variation and collapse assessment of masonry arch bridges under scour action201919910966510.1016/j.engstruct.2019.109665
SCOZZESE F, RAGNI L, TUBALDI E, et al. Modal properties variation and collapse assessment of masonry arch bridges under scour action[J]. Engineering Structures, 2019, 199:109665.
MANGALATHUSHWANGS HCHOIERapid seismic damage evaluation of bridge portfolios using machine learning techniques201920110978510.1016/j.engstruct.2019.109785
MANGALATHU S, HWANG S H, CHOI E, et al. Rapid seismic damage evaluation of bridge portfolios using machine learning techniques[J]. Engineering Structures, 2019, 201:109785.
WANGG XDINGY LLIUHSafety evaluation on wear condition of bearings for high speed railway bridge based on dynamic displacement monitoring20194013946
WANG G X, DING Y L, LIU H, et al. Safety evaluation on wear condition of bearings for high speed railway bridge based on dynamic displacement monitoring[J]. China Railway Science, 2019, 40(1):39-46.(in Chinese)
NIY QWANGY WZHANGCA Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data202021211052010.1016/j.engstruct.2020.110520
NI Y Q, WANG Y W, ZHANG C. A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data[J]. Engineering Structures, 2020, 212:110520.