[48] Gordan M,

Razak HA, Ismail Z, Ghaedi K, Tan ZX, Ghayeb HH. A hybrid ANN-based imperial competitive algorithm methodology for structural damage identification of slab-on-girder bridge using data mining. Applied Soft Computing Journal. 2020;**88**:106013. DOI: 10.1016/j.asoc.2019.106013

[49] Mehrjoo M, Khaji N, Moharrami H, Bahreininejad A. Damage detection of truss bridge joints using artificial neural networks. Expert Systems with Applications. 2008;**35**:1122-1131. DOI: 10.1016/j.eswa.2007.08.008

[50] Jahan S, Mojtahedi A, Mohammadyzadeh S, Hokmabady H. A fuzzy Krill Herd approach for structural health monitoring of bridges using operational modal analysis, Iran. Iranian Journal of Science and Technology, Transactions of Civil Engineering. 2020;**45**(2):1139-1157. DOI: 10.1007/ s40996-020-00475-w

[51] Padil KH, Bakhary N, Abdulkareem M, Li J, Hao H. Nonprobabilistic method to consider uncertainties in frequency response function for vibration-based damage detection using artificial neural network. Journal of Sound and Vibration. 2020;**467**:115069. DOI: 10.1016/j. jsv.2019.115069

[52] Silva M, Santos A, Santos R, Figueiredo E, Sales C, Costa JCWA. Deep principal component analysis: An enhanced approach for structural damage identification. Structural Control and Health Monitoring. 2019;**18**:1444-1463. DOI: 10.1177/1475921718799070

[53] Yin T, Jiang Q, Yuen K. Vibrationbased damage detection for structural connections using incomplete modal data by Bayesian approach and model reduction technique. Engineering Structures. 2017;**132**:260-277

#### [54] Ni P, Han Q, Du X,

Cheng X. Bayesian model updating of civil structures with likelihood-free inference approach and response reconstruction technique. Mechanical Systems and Signal Processing. 2022;**164**:108204. DOI: 10.1016/j. ymssp.2021.108204

[55] Tran-Ngoc H, Khatir S, De Roeck G, Bui-Tien T, Nguyen-Ngoc L, Abdel Wahab M. Model updating for Nam O bridge using particle swarm optimization algorithm and genetic algorithm. Sensors (Switzerland). 2018;**18**:1431. DOI: 10.3390/s18124131

[56] Majumdar A, Kumar D, Maity D. Damage assessment of truss structures from changes in natural frequencies using ant colony optimization. Applied Mathematics and Computation. 2012;**218**:9759-9772

[57] Majumdar A, Nanda B. A comparative study on inverse vibration based damage assessment techniques in beam structure using ant Colony optimization and particle swarm optimization. Advanced Science, Engineering and Medicine. 2020;**12**:918-923

[58] Barman SK, Maiti DK, Maity D. Damage detection of truss employing swarm-based optimization techniques: A comparison. In: Advanced Engineering Optimization Through Intelligent Techniques. Singapore: Springer; 2020. pp. 21-37. DOI: 10.1007/978-981-13-8196-6

[59] Wah WSL, Chen YT, Owen JS. A regression-based damage detection method for structures subjected to changing environmental and operational conditions. Engineering Structures. 2021;**228**:111462. DOI: 10.1016/j. engstruct.2020.111462

[60] Comanducci G, Magalhães F, Ubertini F, Cunha Á. On vibrationbased damage detection by multivariate statistical techniques: Application to a long-span arch bridge. Structural Control and Health Monitoring. 2016;**15**:505-524

[61] Jian X, Zhong H, Xia Y, Sun L. Faulty data detection and classification for bridge structural health monitoring via statistical and deep-learning approach. Structural Control and Health Monitoring. 2021;**28**:e2824. DOI: 10.1002/stc.2824

[62] Wang F, Song G, Mo YL. Shear loading detection of through bolts in bridge structures using a percussionbased one-dimensional memoryaugmented convolutional neural network. Computer-Aided Civil and Infrastructure Engineering. 2021;**36**:289- 301. DOI: 10.1111/mice.12602

[63] Deng J, Lu Y, Lee VCS. Concrete crack detection with handwriting script interferences using faster regionbased convolutional neural network. Computer-Aided Civil and Infrastructure Engineering. 2020;**35**:373-388. DOI: 10.1111/mice.12497

[64] Ni FT, Zhang J, Noori MN. Deep learning for data anomaly detection and data compression of a long-span suspension bridge. Computer-Aided Civil and Infrastructure Engineering. 2020;**35**:685-700. DOI: 10.1111/mice.12528

[65] Xu J, Gui C, Han Q. Recognition of rust grade and rust ratio of steel structures based on ensembled convolutional neural network. Computer-Aided Civil and Infrastructure Engineering. 2020;**35**:1160-1174. DOI: 10.1111/mice.12563

[66] Bao Y, Tang Z, Li H, Zhang Y. Computer vision and deep learning– based data anomaly detection method for structural health monitoring. Structural Control and Health Monitoring. 2019;**18**:401-421. DOI: 10.1177/1475921718757405

[67] Jang K, An YK, Kim B, Cho S. Automated crack evaluation of a highrise bridge pier using a ring-type climbing robot. Computer-Aided Civil and Infrastructure Engineering. 2021;**36**:14-29. DOI: 10.1111/mice.12550

[68] Okazaki Y, Okazaki S, Asamoto S, Chun PJ. Applicability of machine learning to a crack model in concrete bridges. Computer-Aided Civil and Infrastructure Engineering. 2020;**35**:775-792. DOI: 10.1111/mice.12532

[69] Rageh A, Eftekhar Azam S, Linzell DG. Steel railway bridge fatigue damage detection using numerical models and machine learning: Mitigating influence of modeling uncertainty. International Journal of Fatigue. 2020;**134**:105458. DOI: 10.1016/j. ijfatigue.2019.105458

[70] Yin T, Zhu HP. Probabilistic damage detection of a steel truss bridge model by optimally designed bayesian neural network. Sensors (Switzerland). 2018;**18**:3371. DOI: 10.3390/s18103371

[71] Santos J, Crémona C, Calado L. Real-time damage detection based on *Introduction to Monitoring of Bridge Infrastructure Using Soft Computing Techniques DOI: http://dx.doi.org/10.5772/intechopen.104905*

pattern recognition. Structural Concrete. 2016;**17**:338-354

[72] Santos JP, Cremona C, Calado L, Silveira P, Orcesi AD. On-line unsupervised detection of early damage. Structural Control and Health Monitoring. 2016;**23**:1047-1069

[73] Jin C, Jang S, Sun X, Li J, Christenson R. Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network. Journal of Civil Structural Health Monitoring. 2016;**6**:545- 560. DOI: 10.1007/s13349-016-0173-8

[74] Chun P, Yamashita H, Furukawa S. Bridge damage severity quantification UsingMultipoint acceleration measurement and artificial neural networks. Shock and Vibration. 2015;**2015**:789384

[75] Zhou HF, Ni YQ, Ko JM. Eliminating temperature effect in vibration-based structural damage detection. Journal of Engineering Mechanics. 2011;**137**:785-797

[76] Hsu T, Loh C. Damage detection accommodating nonlinear environmental effects by nonlinear principal component analysis. Structural Control and Health Monitoring. 2010;**17**:338-354

[77] Kabir S, Rivard P, Ballivy G. Neuralnetwork-based damage classification of bridge infrastructure using texture analysis. Canadian Journal of Civil Engineering. 2008;**35**:258-267

[78] Meixedo A, Santos J, Ribeiro D, Calçada R, Todd MD. Online unsupervised detection of structural changes using train – Induced dynamic responses. Mechanical Systems and Signal Processing. 2022;**165**:108268. DOI: 10.1016/j.ymssp.2021.108268

[79] Maes K, Van Meerbeeck L, Reynders EPB, Lombaert G. Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51. Mechanical Systems and Signal Processing. 2022;**165**:108380. DOI: 10.1016/j.ymssp.2021.108380

[80] Zhang G, Tang L, Liu Z, Zhou L, Liu Y, Jiang Z, et al. Enhanced features in principal component analysis with spatial and temporal windows for damage identification. Inverse Problems in Science and Engineering. 2021:1-18. DOI: 10.1080/17415977.2021.1954921

[81] Nie Z, Guo E, Li J, Hao H, Ma H, Jiang H. Bridge condition monitoring using fixed moving principal component analysis. Structural Control and Health Monitoring. 2020;**27**:1-29. DOI: 10.1002/ stc.2535

[82] Azim MR, Gül M. Data-driven damage identification technique for steel truss railroad bridges utilizing principal component analysis of strain response. Structure and Infrastructure Engineering. 2021;**17**:1019-1035. DOI: 10.1080/15732479.2020.1785512

[83] Li L, Liu H, Zhou H, Zhang C. Missing data estimation method for time series data in structure health monitoring systems by probability principal component analysis. Advances in Engineering Software. 2020;**149**:102901. DOI: 10.1016/j.advengsoft.2020.102901
