Review of anomaly detection in large span bridges: available methods, recent advancements and future trends

Ziyuan Fan, Xiaoli Tang, Yang Chen, Yuan Ren, Chao Deng, Zihang Wang, Ying Peng, Chenghong Shi, Qiao Huang

Advances in Bridge Engineering ›› 2024, Vol. 5 ›› Issue (1) : 0. DOI: 10.1186/s43251-024-00113-9
Review

Review of anomaly detection in large span bridges: available methods, recent advancements and future trends

Author information +
History +

Abstract

During the life-cycle service of the constructed large span bridges, they face various threats every day due to the sophisticated operational environments. To ensure the structural safety, it is necessary to detect potential anomaly. Based on different inspection, monitoring and analysis technique, huge amounts of data that direct or indirect reflect structural characteristics can be obtained, and hence the anomaly detection methods developed. In order to provide a summary of relevant information needed by researchers to realize what is concerned about and how current practices deal with these issues, then further promote the application, this paper reviews understanding of anomaly detection in large span bridges. It starts with an analysis of concerned parameters, including dynamic and static structural parameters of a bridge. The various data sources are then commented. Next, existing anomaly detection methods are reviewed and classified. Finally, this paper concisely provides recent progress and discusses future research trends based on the identified knowledge gaps. We hope that this review will help development in this field.

Keywords

Anomaly detection / Large span bridge / Time series / Structural health monitoring / Intelligent algorithm

Cite this article

Download citation ▾
Ziyuan Fan, Xiaoli Tang, Yang Chen, Yuan Ren, Chao Deng, Zihang Wang, Ying Peng, Chenghong Shi, Qiao Huang. Review of anomaly detection in large span bridges: available methods, recent advancements and future trends. Advances in Bridge Engineering, 2024, 5(1): 0 https://doi.org/10.1186/s43251-024-00113-9

References

[]
An YH, Chatzi E, Sim SH, Simon L. Recent progress and future trends on damage identification methods for bridge structures. Struct Control Health Monit, 2019, 26: e2416,
CrossRef Google scholar
[]
Adewuyi AP, Wu ZS, Serker K. Assessment of vibration-based damage identification methods using displacement and distributed strain measurements. Struct Health Monit, 2009, 8: 443-461,
CrossRef Google scholar
[]
Aktan AE, Catbas FN, Grimmelsman KA, Tsikos CJ. Issues in infrastructure health monitoring for management. J Eng Mech, 2000, 126(7): 711-724,
CrossRef Google scholar
[]
Alkayem NF, Cao MS, Zhang YF, Bayat M, Su ZQ. Structural damage detection using finite element model updating with evolutionary algorithms: a survey. Neural Comput Appl, 2018, 30: 389-411,
CrossRef Google scholar
[]
AASHTO. . AASHTO LRFD bridge design specifications, 2017 Washington, DC AASHTO
[]
Azimi M, Eslamlou AD, Pekcan G. Data-driven structural health monitoring and damage detection through deep learning: State-of- the-art review. Sensors, 2020, 20: 2778,
CrossRef Google scholar
[]
Bellino A, Fasana A, Garibaldi L, Marchesiello S. PCA-based detection of damage in time-varying systems. Mech Syst Signal Process, 2010, 24(7): 2250-2260,
CrossRef Google scholar
[]
Brockwell PJ, Davis RA. . Introduction to Time Series and Forecasting, 2002 New York Taylor & Francis,
CrossRef Google scholar
[]
Brownjohn JMW, Koo KY, Scullion A, List D. Operational deformations in long-span bridges. Struct Infrastruct Eng, 2015, 11(4): 556-574,
CrossRef Google scholar
[]
Carden E, Brownjohn JM. ARMA modelled time-series classification for structural health monitoring of civil infrastructure. Mech Syst Signal Process, 2008, 22(2): 295-314,
CrossRef Google scholar
[]
Comanducci G, Magalhaes F, Ubertini F, Cunha A. On vibration-based damage detection by multivariate statistical techniques: Application to a long-span arch bridge. Struct Health Monit, 2016, 15(5): 515-534,
CrossRef Google scholar
[]
Cross EJ, Worden K. Cointegration and why it works for SHM. J Phys: Conf Ser, 2012, 382: 012046
[]
Cui C, Xu YL, Zhang QH, Wang FY. Vehicle-induced fatigue damage prognosis of orthotropic steel decks of cable-stayed bridges. Eng Struct, 2020, 212: 110509,
CrossRef Google scholar
[]
Ding YL, Li AQ, Sun J, Deng Y. Research on seasonal correlation of wavelet packet energy spectrum and temperature of Runyang Suspension Bridge. Sci China Series E, 2009, 52(6): 1776-1785,
CrossRef Google scholar
[]
Doebling SW, Farrar CR, Prime MB. A summary review of vibration-based damage identification methods. Shock Vibr Digest, 1998, 30(2): 91-105,
CrossRef Google scholar
[]
Fan ZY, Huang Q, Ren Y, Xu X, Zhu ZY. Real-time dynamic warning on deflection abnormity of cable-stayed bridges considering operational environment variations. J Perform Constr Facil, 2021, 35(1): 04020123,
CrossRef Google scholar
[]
Fan ZY, Huang Q, Ren Y, Ye QW, Chang WJ, Wang YC. Cointegration based modeling and anomaly detection approaches using monitoring data of a suspension bridge. Smart Struct Syst, 2023, 31(2): 183-197
[]
Fan ZY, Huang Q, Ren Y, Zhu ZY, Xu X. A cointegration approach for cable anomaly warning based on structural health monitoring data: An application to cable-stayed bridges. Adv Struct Eng, 2020, 23(13): 2789-2802,
CrossRef Google scholar
[]
Fan ZY, Ye QW, Xu X, Ren Y, Huang Q, Li WZ. Fatigue reliability-based replacement strategy for bridge stay cables: A case study in China. Structures, 2022, 39: 1176-1188,
CrossRef Google scholar
[]
Flah M, Nunez I, Ben Chaabene W, Nehdi ML. Machine learning algorithms in civil structural health monitoring: a systematic review. Arch Comput Methods Eng, 2021, 28: 2621-2643,
CrossRef Google scholar
[]
Gravitz SI. An analytical procedure for orthogonalization of experimentally measured modes. J Aerospace Sci, 2015, 25(11): 721-722,
CrossRef Google scholar
[]
Greco F, Leonetti L, Lonetti P, Blasi PN. Crack propagation analysis in composite materials by using moving mesh and multiscale techniques. Comput Struct, 2015, 153: 201-216,
CrossRef Google scholar
[]
Gul M, Catbas FN. Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering. J Sound Vib, 2011, 330(6): 1196-1210,
CrossRef Google scholar
[]
Hakim SJS, Razak HA. Adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for structural damage identification. Struct Eng Mech, 2013, 45(6): 779-802,
CrossRef Google scholar
[]
Han NJ, Zhang B, Zhao WG, Zhang H. Truss bridge anomaly detection using quasi-static rotation response. J Civ Struct Heal Monit, 2022, 12(3): 579-591,
CrossRef Google scholar
[]
Hao S. I-35W bridge collapse. J Bridg Eng, 2010, 15(5): 608-614,
CrossRef Google scholar
[]
Hou RR, Wang XY, Xia Q, Xia Y. Sparse Bayesian learning for structural damage detection under varying temperature conditions. Mech Syst Signal Process, 2020, 145: 106965,
CrossRef Google scholar
[]
Hou ST, Dong B, Wang HC, Wu G. Inspection of surface defects on stay cables using a robot and transfer learning. Autom Constr, 2020, 119: 103382,
CrossRef Google scholar
[]
Huang HB, Yi TH, Li HN, Liu H. Strain-based performance warning method for bridge main girders under variable operating conditions. J Bridg Eng, 2020, 25(4): 04020013,
CrossRef Google scholar
[]
Huang W, Pei MS, Liu XD, Wei Y. Design and construction of super-long span bridges in China: Review and future perspectives. Front Struct Civ Eng, 2020, 14(4): 803-838,
CrossRef Google scholar
[]
Invernizzi S, Montagnoli F, Carpinteri A. Very high cycle corrosion fatigue study of the collapsed Polcevera Bridge, Italy. J Bridge Eng, 2022, 27(1): 04021102,
CrossRef Google scholar
[]
Jayawardhana M, Zhu XQ, Liyanapathirana R, Gunawardana U. Statistical damage sensitive feature for structural damage detection using AR model coefficients. Adv Struct Eng, 2015, 18(10): 1551-1562,
CrossRef Google scholar
[]
Jones D, Snider C, Nassehi A, Yon J, Hicks B. Characterising the digital twin: a systematic literature review. CIRP J Manuf Sci Technol, 2020, 29: 36-52,
CrossRef Google scholar
[]
Karimi S, Mirza O. Damage identification in bridge structures: review of available methods and case studies. Aust J Struct Eng, 2023, 24(2): 89-119,
CrossRef Google scholar
[]
Kromanis R, Kripakaran P. SHM of bridges: characterising thermal response and detecting anomaly events using a temperature-based measurement interpretation approach. J Civ Struct Heal Monit, 2016, 6: 237-254,
CrossRef Google scholar
[]
Kromanis R, Kripakaran P. Performance of signal processing techniques for anomaly detection using a temperature-based measurement interpretation approach. J Civ Struct Heal Monit, 2021, 11(1): 15-34,
CrossRef Google scholar
[]
Kullaa J. Distinguishing between sensor fault, structural damage, and environmental or operational effects in structural health monitoring. Mech Syst Signal Process, 2011, 25(8): 2976-2989,
CrossRef Google scholar
[]
Li J, Huang Y, Asadollahi P. Sparse Bayesian learning with model reduction for probabilistic structural damage detection with limited measurements. Eng Struct, 2021, 247: 113183,
CrossRef Google scholar
[]
Li SW, Laima SJ, Li H. Data-driven modeling of vortex-induced vibration of a long-span suspension bridge using decision tree learning and support vector regression. J Wind Eng Ind Aerodyn, 2018, 172: 196-211,
CrossRef Google scholar
[]
Liang YB, Li DS, Song GB, Feng Q. Frequency co-integration-based damage detection for bridges under the influence of environmental temperature variation. Measurement, 2018, 125: 163-175,
CrossRef Google scholar
[]
Lin KQ, Xu YL, Lu XZ, Guan ZG. Collapse prognosis of a long-span cable-stayed bridge based on shake table test and nonlinear model updating. Earthquake Eng Struct Dynam, 2021, 50(2): 455-474,
CrossRef Google scholar
[]
Liu ZX, Guo T, Chai S. Probabilistic fatigue life prediction of bridge cables based on multiscaling and mesoscopic fracture mechanics. Appl Sci Basel, 2016, 6(4): 99,
CrossRef Google scholar
[]
Liu ZX, Guo T, Correia J, Wang LB. Reliability-based maintenance strategy for gusset plate connections in steel bridges based on life-cost optimization. J Perform Constr Facil, 2020, 34(5): 04020088,
CrossRef Google scholar
[]
Makhoul N. Review of data quality indicators and metrics, and suggestions for indicators and metrics for structural health monitoring. Adv Bridge Eng, 2022, 3: 17,
CrossRef Google scholar
[]
Mehrabi AB. Performance of cable-stayed bridges: Evaluation methods, observations, and a rehabilitation case. J Perform Constr Facil, 2016, 30(1): C4014007,
CrossRef Google scholar
[]
Mosavi A, Dickey D, Seracino R, Rizkalla S. Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distance. Mech Syst Signal Process, 2012, 26(1): 254-267,
CrossRef Google scholar
[]
Neves AC, Gonzalez I, Leander J, Karoumi R. Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. J Civ Struct Heal Monit, 2017, 7(5): 689-702,
CrossRef Google scholar
[]
Ni YQ, Xia HW, Wong KY, Ko JM. In-service condition assessment of bridge deck using long-term monitoring data of strain response. J Bridg Eng, 2012, 17(6): 876-885,
CrossRef Google scholar
[]
Ni YQ, Wang YW, Zhang C. A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data. Eng Struct, 2020, 212: 110520,
CrossRef Google scholar
[]
Niu J, Zong ZH, Chu FP. Damage identification method of girder bridges based on finite element model updating and modal strain energy. Sci China Technol Sci, 2015, 58(4): 701-711,
CrossRef Google scholar
[]
Niu J. . Probabilistic damage identification of bridge structure based on finite element model validation, 2018 Nanjing Southeast University
[]
Pan QY, Bao YQ, Li H. Transfer learning-based data anomaly detection for structural health monitoring. Struct Health Monit, 2023, 22(5): 3077-3091,
CrossRef Google scholar
[]
Pandey AK, Biswas M, Samman MM. Damage detection from changes in curvature mode shapes. J Sound Vib, 1991, 145(2): 321-332,
CrossRef Google scholar
[]
Posenato D, Lanata F, Inaudi D, Smith IFC. Model-free data interpretation for continuous monitoring of complex structures. Adv Eng Inform, 2008, 22: 135-144,
CrossRef Google scholar
[]
Ren P, Zhou Z. Two-step approach to processing raw strain monitoring data for damage detection of structures under operational conditions. Sensors, 2021, 21: 6887,
CrossRef Google scholar
[]
Ren Y, Ye QW, Xu X, Huang Q, Fan ZY, Li C, Chang WJ. An anomaly pattern detection for bridge structural response considering time-varying temperature coefficients. Structures, 2022, 46: 285-298,
CrossRef Google scholar
[]
Rios AJ, Plevris V, Nogal M. Bridge management through digital twin-based anomaly detection systems: a systematic review. Fronti Built Environ, 2023, 9: 1176621,
CrossRef Google scholar
[]
Rodrigues C, Felix C, Figueiras J. Fiber-optic-based displacement transducer to measure bridge deflections. Struct Health Monit, 2011, 10(2): 147-156,
CrossRef Google scholar
[]
Santos ICE, de Brito JLV, Caetano ED. Uncertainty quantification: data assimilation, propagation and validation of the numerical model of the Arade river cable-stayed bridge. Struct Infrastruct Eng, 2022, 18(10–11): 1410-1427,
CrossRef Google scholar
[]
Silva SD, Júnior MD, Junior VL. Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition. J Braz Soc Mech Sci Eng, 2007, 29(2): 174-184,
CrossRef Google scholar
[]
Singh P, Mittal S, Sadhu A. Recent advancements and future trends in indirect bridge health monitoring. Pract Period Struct Des Constr, 2023, 28(1): 03122008,
CrossRef Google scholar
[]
Sohn H, Czarnecki JA, Farrar CR. Structural health monitoring using statistical process control. J Struct Eng, 2000, 126(11): 1356-1363,
CrossRef Google scholar
[]
Sohn H. Effects of environmental and operational variability on structural health monitoring. Phil Trans R Soc A, 2007, 365: 539-560,
CrossRef Google scholar
[]
Soleimani-Babakamali MH, Soleimani-Babakamali R, Sarlo R, Farghally MF, Lourentzou I. On the effectiveness of dimensionality reduction for unsupervised structural health monitoring anomaly detection. Mech Syst Signal Process, 2023, 187: 109910,
CrossRef Google scholar
[]
Sun LM, Shang ZQ, Xia Y, Bhowmick S, Nagarajaiah S. Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection. J Struct Eng, 2020, 146(5): 04020073,
CrossRef Google scholar
[]
Sun Z, Siringoringo DM, Chen SZ, Lu J. Cumulative displacement-based detection of damper malfunction in bridges using data-driven isolation forest algorithm. Eng Fail Anal, 2023, 143: 106849,
CrossRef Google scholar
[]
Tang R, Zhu J, Ren Y, Ding Y, Wu J, Guo Y, et al.. A knowledge-guided fusion visualisation method of digital twin scenes for mountain highways. ISPRS Int J Geo-Inf, 2023, 12(10): 424,
CrossRef Google scholar
[]
Thai H-T. Machine learning for structural engineering: a state-of-the-art review. Structures, 2022, 38: 448-491,
CrossRef Google scholar
[]
Tomé ES, Pimentel M, Figueiras J. Damage detection under environmental and operational effects using cointegration analysis - Application to experimental data from a cable-stayed bridge. Mech Syst Signal Process, 2020, 135: 106386,
CrossRef Google scholar
[]
Wang WZ, Dan DH, Gao JQ. Study on damage identification of high-speed railway truss bridge based on statistical steady-state strain characteristic function. Eng Struct, 2023, 294: 116723,
CrossRef Google scholar
[]
Wang X, Niederleithinger E, Hindersmann I. The installation of embedded ultrasonic transducers inside a bridge to monitor temperature and load influence using coda wave interferometry technique. Struct Health Monit, 2022, 21: 913-927,
CrossRef Google scholar
[]
Wang Z, Yang DH, Yi TH, Zhang GH, Han JG. Eliminating environmental and operational effects on structural modal frequency: a comprehensive review. Struct Control Health Monit, 2022, 29(11): e3073,
CrossRef Google scholar
[]
Wickramasinghe WR, Thambiratnam DP, Chan THT. Damage detection in a suspension bridge using modal flexibility method. Eng Fail Anal, 2020, 107(I): 104194,
CrossRef Google scholar
[]
Worden K, Manson G, Fieller NRJ. Damage detection using outlier analysis. J Sound Vib, 2000, 229(3): 647-667,
CrossRef Google scholar
[]
Wu GM, Yi TH, Yang DH, Li HN, Liu H. Early Warning method for bearing displacement of long-span bridges using a proposed time-varying temperature–displacement model. J Bridg Eng, 2021, 26(9): 04021068,
CrossRef Google scholar
[]
Xin HH, Cheng L, Diender R, Veljkovic M. Fracture acoustic emission signals identification of stay cables in bridge engineering application using deep transfer learning and wavelet analysis. Adv Bridge Eng, 2020, 1: 6,
CrossRef Google scholar
[]
Xu X, Huang Q, Ren Y, Zhao D, Yang J, Zhang D. Modeling and separation of thermal effects from cable-stayed bridge response. J Bridg Eng, 2019, 24(5): 04019028,
CrossRef Google scholar
[]
Xu X, Ren Y, Huang Q, Fan ZY, Tong ZJ, Chang WJ, Liu B. Anomaly detection for large span bridges during operational phase using structural health monitoring data. Smart Mater Struct, 2020, 29(4): 045029,
CrossRef Google scholar
[]
Xu X, Ren Y, Huang Q, Zhao DY, Tong ZJ, Chang WJ. Thermal response separation for bridge long-term monitoring systems using multi-resolution wavelet-based methodologies. J Civ Struct Heal Monit, 2020, 10: 527-541,
CrossRef Google scholar
[]
Xu X, Forde MC, Ren Y, Huang Q. A Bayesian approach for site-specific extreme load prediction of large scale bridges. Struct Infrastruct Eng, 2021, 19(9): 1249-1262,
CrossRef Google scholar
[]
Xu X, Qian ZD, Huang Q, Ren Y, Liu B. Probabilistic anomaly trend detection for cable-supported bridges using confidence interval estimation. Adv Struct Eng, 2022, 25(5): 966-978,
CrossRef Google scholar
[]
Xu X, Forde MC, Ren Y, Huang Q, Liu B. Multi-index probabilistic anomaly detection for large span bridges using Bayesian estimation and evidential reasoning. Struct Health Monit, 2023, 22(2): 948-965,
CrossRef Google scholar
[]
Xu YL. Making good use of structural health monitoring systems of long-span cable-supported bridges. J Civ Struct Heal Monit, 2018, 8: 477-497,
CrossRef Google scholar
[]
Yang DH, Yi TH, Li HN, Zhang YF. Correlation-based estimation method for cable-stayed bridge girder deflection variability under thermal action. J Perform Constr Facil, 2018, 32(5): 04018070,
CrossRef Google scholar
[]
Yang DY, Frangopol DM. Risk-based inspection planning of deteriorating structures. Struct Infrastruct Eng, 2022, 18(1): 109-128,
CrossRef Google scholar
[]
Yin SH, Tang CY. Identifying cable tension loss and deck damage in a cable-stayed bridge using a moving vehicle. J Vib Acoust, 2011, 133(2): 021007,
CrossRef Google scholar
[]
Yu EB, Wei H, Han Y, Hu P, Xu GJ. Application of time series prediction techniques for coastal bridge engineering. Adv Bridge Eng, 2021, 2: 6,
CrossRef Google scholar
[]
Zeng YP, Yan YY, Weng S, Sun YH, Tian W, Yu H. Fuzzy clustering of time-series model to damage identification of structures. Adv Struct Eng, 2019, 22(4): 868-881,
CrossRef Google scholar
[]
Zhang GQ, Wang B, Li J, Xu YL. The application of deep learning in bridge health monitoring: a literature review. Adv Bridge Eng, 2022, 3: 22,
CrossRef Google scholar
[]
Zhang JL, Zhang J, Wu ZS. Long-short term memory network-based monitoring data anomaly detection of a long-span suspension bridge. Sensors, 2022, 22: 6045,
CrossRef Google scholar
[]
Zhang YM, Wang H, Bai Y, Mao JX, Chang XY, Wang LB. Switching Bayesian dynamic linear model for condition assessment of bridge expansion joints using structural health monitoring data. Mech Syst Signal Process, 2021, 160: 107879,
CrossRef Google scholar
[]
Zhao H, Tan CJ, OBrien EJ, Zhang B, Uddin N, Guo HJ. Developing digital twins to characterize bridge behavior using measurements taken under random traffic. J Bridge Eng, 2022, 27(1): 04021101,
CrossRef Google scholar
[]
Zheng HT, Mita A. Localized damage detection of structures subject to multiple ambient excitations using two distance measures for autoregressive models. Struct Health Monit, 2009, 8(3): 207-216,
CrossRef Google scholar
[]
Zhong RM, Zhong ZH, Liu QQ, Zhou HF. A multiscale finite element model validation method of composite cable-stayed bridge based on structural health monitoring system. Shock Vib, 2015, 2015: 817281
[]
Zhong RM, Zong ZH, Niu J, Liu QQ, Zheng PJ. A multiscale finite element model validation method of composite cable-stayed bridge based on Probability Box theory. J Sound Vib, 2016, 370: 111-131,
CrossRef Google scholar
[]
Zong ZH, Zhong RM, Zheng PJ, Qin ZY, Liu QQ. Damage and safety prognosis of bridge structures based on structural health monitoring: progress and challenges. China J Highw Transp, 2014, 27(14): 46-57
[]
Zhu YJ, Ni YQ, Jin H, Inaudi D, Laory I. A temperature-driven MPCA method for structural anomaly detection. Eng Struct, 2019, 190: 447-458,
CrossRef Google scholar
Funding
Science Foundation of Zhejiang Sci-Tech University(22052331-Y); General Science Research Project of Department of Education of Zhejiang Province(Y202354008); National Key Research and Development Program of China(2022YFB3706704); Academician Special Science Research Project of CCCC(YSZX-03-2022-01-B)

Accesses

Citations

Detail

Sections
Recommended

/