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A review on structural health monitoring: past to present

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Abstract

The field of structural health monitoring (SHM) has gained significant attention from academia and industry, particularly in the realm of damage detection. This approach allows continuous monitoring of the structural integrity of systems and structures throughout their operational lifespan, leading to reduced dependence on periodic inspections and lower maintenance costs. Importantly, this method enables the assessment of structural degradation without causing actual damage to the structure itself. Over the past four decades, various evaluation methods for SHM have been developed, with concrete buildings benefiting greatly from their implementation. Among these methods, vibration-based techniques provide a quick and efficient way to assess the overall health of a structure. Specifically, frequency-based techniques are commonly employed within the vibrations-based SHM (VBSHM) framework, although extracting these parameters through algorithm development can be a complex and time-consuming task. This research aims to review VBSHM with a focus on modal parameters, exploring both traditional and modern methodologies to enhance the accuracy of structural assessment. Additionally, the study highlights the current limitations of research in this field and identifies available studies for further exploration.

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Abbreviations

SHM:

Structural Health Monitoring

VBSHM:

Vibration-Based Structural Health Monitoring

NDE:

Non-Destructive Evaluation

VE:

Visual Examination

CM:

Conventional Methods

AM:

Advance Methods

RH:

Rebound Hammer

UPV:

Ultrasonic Pulse Velocity

TI:

Thermal Image

CT:

Carbonation Test

IoT:

Internet of Things

AI:

Artificial Intelligence

ML:

Machine Learning

DP:

Deep Learning

FEM:

Finite Element Model

FGM:

Functionally Graded Material

NDT:

Non-Destructive Testing

SVM:

Support Vector Machine

EI:

Effective Independence

TB:

Time-Based

FB:

Frequency-Based

MB:

Modal-Based

TM:

Traditional Method

MM:

Modern Method

NB:

Numerical-Based

AR:

Auto-Regressive model

ARMA:

Auto-Regressive Moving Average

EDM:

Empirical Mode Decomposition

MDODA:

Mahalanobis Distance Outlier Detection Algorithm

IRF:

Impulse Response Functions

SBL:

Sparse Bayesian Learning

E:

Experimental

N:

Numerical

FRF:

Frequency Response Function

DFT:

Discrete Fourier Transform

SFRF:

Strain Frequency Response Function

FDD:

Frequency Domain Decomposition

MUSIC:

Multiple Signal Classification

FD:

Fractal Dimension

WT:

Wavelet Transform

NULS:

Normalized Uniform Load Surface

MSC:

Mode Shape Curvature

RD:

Random Decrement

EBDE:

Energy-Based Damping Evaluation

MCM:

Modal Curvature Method

PSDT:

Power Spectral Density Transmissibility

TDI:

Transmissibility Damage Indicator

DRQ:

Damage and Relative damage Quantification Indicator

PDDR:

Probability Distribution of Decay Rate

RCC:

Reinforced Concrete Cement

ANI:

Artificial Narrow Intelligence

AGI:

Artificial General Intelligence

ASI:

Artificial Super Intelligence

DCNN:

Deep Convolutional Neural Network

ANN:

Artificial Neural Network

LR:

Linear Regression

PCA:

Principal Component Analysis

NDC:

Neural Dynamics Classification

OLR:

Ordinary Linear Regression

CNN:

Convolutional Neural Network

VNA:

Visio non-destructive testing

VVA:

Visio vibration analysis

NVA:

Non-destructive Vibration Analysis

RNN:

Recurrent Neural Network

EE:

Experience Engineer

IASC-ASCE:

International association for structural control, American society of civil engineers

CNN-ATT-biGUR:

CNN– Attention- Bidirectional Gated recurrent unit

References

  1. Farrar CR, Worden K (2007) An introduction to structural health monitoring. Philos Trans R Soc A: Math, Phys Eng Sci 365(1851):303–315

    Article  Google Scholar 

  2. Wardhana K, Hadipriono FC (2003) Analysis of recent bridge failures in the United States. J Perform Constr Facil 17(3):144–150

    Article  Google Scholar 

  3. Olanitori LM (2011) Causes of structural failures of a building: case study of a building at Oba-Ile, Akure. J Build Apprais 6:277–284. https://doi.org/10.1057/jba.2011.5

    Article  Google Scholar 

  4. Garg RK, Chandra S, Kumar A (2022) Analysis of bridge failures in India from 1977 to 2017. Struct Infrastruct Eng 18(3):295–312. https://doi.org/10.1080/15732479.2020.1832539

    Article  Google Scholar 

  5. Chatterjee P (2014) Urban building collapse: What are the health implications? BMJ. https://doi.org/10.1136/bmj.g5256

    Article  Google Scholar 

  6. Rucka M (2020) Non-destructive testing of structures. Materials 13(21):4996

    Article  Google Scholar 

  7. Lin YC, Lin Y, Chan CC (2016) Use of ultrasonic pulse velocity to estimate strength of concrete at various ages. Mag Concr Res 68(14):739–749. https://doi.org/10.1680/jmacr.15.00025

    Article  Google Scholar 

  8. Kumavat HR, Chandak NR, Patil IT (2021) Factors influencing the performance of rebound hammer used for non-destructive testing of concrete members: a review. Case Stud Constr Mater 14:e00491. https://doi.org/10.1016/j.cscm.2021.e00491

    Article  Google Scholar 

  9. Sohn H, Farrar CR, Hemez FM, Shunk DD, Stinemates DW, Nadler BR, Czarnecki JJ (2003) A review of structural health monitoring literature: 1996–2001. Los Alamos Natl Lab, USA 1:16

    Google Scholar 

  10. Kumar S, Mahto DG (2013) Recent trends in industrial and other engineering applications of non-destructive testing: a review. Int J Sci Eng Res 4(9):183–195

    Google Scholar 

  11. Hussein AB, Abdi M (2021) Review paper on non-destructive testing and their accuracies to measure the mechanical properties of concrete. Int J Eng Appl Sci Technol 5(11):1–9

    Google Scholar 

  12. Gupta N, Gupta A (2021) Condition assessment of the structural elements of a reinforced concrete structure using non-destructive techniques. In: IOP conference series: materials science and engineering (Vol 1116, No 1, pp 012164). IOP Publishing. https://doi.org/10.1088/1757-899X/1116/1/012164

  13. Hamidian M, Shariati A, Khanouki MA, Sinaei H, Toghroli A, Nouri K (2012) Application of Schmidt rebound hammer and ultrasonic pulse velocity techniques for structural health monitoring. Sci Res Essays 7(21):1997–2001. https://doi.org/10.5897/SRE11.1387

    Article  Google Scholar 

  14. Kencanawati NN, Akmaluddin A, Anshari B, Gazi A, Shigeishi M (2018) The study of ultrasonic pulse velocity on plain and reinforced damaged concrete. In MATEC Web of Conferences (Vol 195, No 2018, pp 1–8). EDP Sciences. http://eprints.unram.ac.id/id/eprint/15668

  15. Helal J, Sofi M, Mendis P (2015) Non-destructive testing of concrete: a review of methods. Electr J Struct Eng 14(1):97–105

    Article  Google Scholar 

  16. Qaidi S, Najm HM, Abed SM, Özkılıç YO, Al Dughaishi H, Alosta M, Milad A (2022) Concrete containing waste glass as an environmentally friendly aggregate: a review on fresh and mechanical characteristics. Materials 15(18):6222. https://doi.org/10.3390/ma15186222

    Article  Google Scholar 

  17. Almeshal I, Al-Tayeb MM, Qaidi SM, Bakar BA, Tayeh BA (2022) Mechanical properties of eco-friendly cements-based glass powder in aggressive medium. Mater Today: Proc 58:1582–1587. https://doi.org/10.1016/j.matpr.2022.03.613

    Article  Google Scholar 

  18. Ahmed HU, Mahmood LJ, Muhammad MA, Faraj RH, Qaidi SM, Sor NH, Mohammed AA (2022) Geopolymer concrete as a cleaner construction material: an overview on materials and structural performances. Clean Mater. https://doi.org/10.1016/j.clema.2022.100111

    Article  Google Scholar 

  19. Akeed MH, Qaidi S, Ahmed HU, Faraj RH, Mohammed AS, Emad W, Azevedo AR (2022) Ultra-high-performance fiber-reinforced concrete. Part IV: durability properties, cost assessment, applications, and challenges. Case Stud Constr Mater 17:e01271. https://doi.org/10.1016/j.cscm.2022.e01271

    Article  Google Scholar 

  20. Svendsen BT, Øiseth O, Frøseth GT, Rønnquist A (2023) A hybrid structural health monitoring approach for damage detection in steel bridges under simulated environmental conditions using numerical and experimental data. Struct Health Monit 22(1):540–561. https://doi.org/10.1177/14759217221098998

    Article  Google Scholar 

  21. Mao JJ, Wang YJ, Zhang W, Wu MQ, Liu YZ, Liu XH (2023) Vibration and wave propagation in functionally graded beams with inclined cracks. Appl Math Model 118:166–184. https://doi.org/10.1016/j.apm.2023.01.035

    Article  Google Scholar 

  22. Yelisetti S, Katam R, Kalapatapu P, Pasupuleti VDK (2022) Global Health assessment of structures using NDT and machine learning. European Workshop on Structural Health Monitoring: EWSHM 2022-Volume 3. Springer International Publishing, Cham, pp 359–370

    Google Scholar 

  23. Katam R, Kalapatapu P, Pasupuleti VDK (2022) A review on technological advancements in the field of data driven structural health monitoring. In European workshop on structural health monitoring: EWSHM 2022-Volume 3 (pp 371-380). Cham: Springer International Publishing

  24. Kong X, Cai CS, Hu J (2017) The state-of-the-art on framework of vibration-based structural damage identification for decision making. Appl Sci 7(5):497. https://doi.org/10.3390/app7050497

    Article  Google Scholar 

  25. Yang Y, Zhang Y, Tan X (2021) Review on vibration-based structural health monitoring techniques and technical codes. Symmetry 13(11):1998. https://doi.org/10.3390/sym13111998

    Article  Google Scholar 

  26. Cao MS, Sha GG, Gao YF, Ostachowicz W (2017) Structural damage identification using damping: a compendium of uses and features. Smart Mater Struct 26(4):043001. https://doi.org/10.1088/1361-665X/aa550a

    Article  Google Scholar 

  27. Hou R, Xia Y (2021) Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019. J Sound Vib 491:115741. https://doi.org/10.1016/j.jsv.2020.115741

    Article  Google Scholar 

  28. Das S, Saha P, Patro SK (2016) Vibration-based damage detection techniques used for health monitoring of structures: a review. J Civ Struct Heal Monit 6:477–507

    Article  Google Scholar 

  29. Huang Q, Gardoni P, Hurlebaus S (2012) A probabilistic damage detection approach using vibration-based nondestructive testing. Struct Saf 38:11–21. https://doi.org/10.1016/j.strusafe.2012.01.004

    Article  Google Scholar 

  30. Yan YJ, Cheng L, Wu ZY, Yam LH (2007) Development in vibration-based structural damage detection technique. Mech Syst Signal Process 21(5):2198–2211. https://doi.org/10.1016/j.ymssp.2006.10.002

    Article  Google Scholar 

  31. Farrar CR, Lieven NA (2007) Damage prognosis: the future of structural health monitoring. Philos Trans R Soc A: Math, Phys Eng Sci 365(1851):623–632. https://doi.org/10.1098/rsta.2006.1927

    Article  Google Scholar 

  32. Hung SL, Kao CY, Huang JW (2022) Constrained K-means and genetic algorithm-based approaches for optimal placement of wireless structural health monitoring sensors. Civ Eng J 8(12):2675–2692

    Article  Google Scholar 

  33. Niyirora R, Wei J, Masengesho E, Munyaneza J, Niyonyungu F, Nyirandayisabye R (2022) Intelligent damage diagnosis in bridges using vibration-based monitoring approaches and machine learning: a systematic review. Res Eng. https://doi.org/10.1016/j.rineng.2022.100761

    Article  Google Scholar 

  34. Yam LH, Yan YJ, Jiang JS (2003) Vibration-based damage detection for composite structures using wavelet transform and neural network identification. Compos Struct 60(4):403–412. https://doi.org/10.1016/S0263-8223(03)00023-0

    Article  Google Scholar 

  35. Yan YJ, Yam LH (2004) Detection of delamination damage in composite plates using energy spectrum of structural dynamic responses decomposed by wavelet analysis. Comput Struct 82(4–5):347–358. https://doi.org/10.1016/j.compstruc.2003.11.002

    Article  Google Scholar 

  36. Sung SH, Jung HJ, Jung HY (2013) Damage detection for beam-like structures using the normalized curvature of a uniform load surface. J Sound Vib 332(6):1501–1519. https://doi.org/10.1016/j.jsv.2012.11.016

    Article  Google Scholar 

  37. Zhao J, Zhang L (2012) Structural damage identification based on the modal data change. Int J Eng Manuf 4:59–66. https://doi.org/10.5815/ijem.2012.04.08

    Article  Google Scholar 

  38. Khiem NT, Tran HT (2014) A procedure for multiple crack identification in beam-like structures from natural vibration mode. J Vib Control 20(9):1417–1427. https://doi.org/10.1177/1077546312470478

    Article  Google Scholar 

  39. Khiem NT, Toan LK (2014) A novel method for crack detection in beam-like structures by measurements of natural frequencies. J Sound Vib 333(18):4084–4103. https://doi.org/10.1016/j.jsv.2014.04.031

    Article  Google Scholar 

  40. Li S, Li S, Laima S, Li H (2021) Data-driven modeling of bridge buffeting in the time domain using long short-term memory network based on structural health monitoring. Struct Control Health Monitor 28(8):e2772. https://doi.org/10.1002/stc.2772

    Article  Google Scholar 

  41. Loh CH, Hung TY, Chen SF, Hsu WT (2015) Damage detection in bridge structure using vibration data under random travelling vehicle loads. J Phys: Conf Series 628(1):012044. https://doi.org/10.1088/1742-6596/628/1/012044

    Article  Google Scholar 

  42. Mosavi AA, Dickey D, Seracino R, Rizkalla S (2012) Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distances. Mech Syst Signal Process 26:254–267. https://doi.org/10.1016/j.ymssp.2011.06.009

    Article  Google Scholar 

  43. Ruocci G, Quattrone A, De Stefano A (2011) Multi-domain feature selection aimed at the damage detection of historical bridges. J Phys: Conf Series 305(1):012106. https://doi.org/10.1088/1742-6596/305/1/012106

    Article  Google Scholar 

  44. Figueiredo E, Figueiras J, Park G, Farrar CR, Worden K (2011) Influence of the autoregressive model order on damage detection. Comput-Aided Civ Infrastruct Eng 26(3):225–238. https://doi.org/10.1111/j.1467-8667.2010.00685.x

    Article  Google Scholar 

  45. Carden EP, Brownjohn JM (2008) ARMA modelled time-series classification for structural health monitoring of civil infrastructure. Mech Syst Signal Process 22(2):295–314. https://doi.org/10.1016/j.ymssp.2007.07.003

    Article  Google Scholar 

  46. Zhang X, Li D, Song G (2018) Structure damage identification based on regularized ARMA time series model under environmental excitation. Vibration 1(1):138–156. https://doi.org/10.3390/vibration1010011

    Article  Google Scholar 

  47. Casas JR, Moughty JJ (2017) Bridge damage detection based on vibration data: past and new developments. Front Built Environ 3:4. https://doi.org/10.3389/fbuil.2017.00004

    Article  Google Scholar 

  48. Todorovska MI, Rahmani MT (2013) System identification of buildings by wave travel time analysis and layered shear beam models—Spatial resolution and accuracy. Struct Control Health Monit 20(5):686–702. https://doi.org/10.1002/stc.1484

    Article  Google Scholar 

  49. Todorovska MI, Trifunac MD (2008) Earthquake damage detection in the Imperial County Services Building III: analysis of wave travel times via impulse response functions. Soil Dyn Earthq Eng 28(5):387–404. https://doi.org/10.1016/j.soildyn.2007.07.001

    Article  Google Scholar 

  50. Xu YL, Chen J (2004) Structural damage detection using empirical mode decomposition: experimental investigation. J Eng Mech 130(11):1279–1288. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:11(1279)

    Article  Google Scholar 

  51. Bao C, Hao H, Li ZX (2013) Integrated ARMA model method for damage detection of subsea pipeline system. Eng Struct 48:176–192. https://doi.org/10.1016/j.engstruct.2012.09.033

    Article  Google Scholar 

  52. Vamvoudakis-Stefanou KJ, Sakellariou JS, Fassois SD (2018) Vibration-based damage detection for a population of nominally identical structures: unsupervised Multiple Model (MM) statistical time series type methods. Mech Syst Signal Process 111:149–171. https://doi.org/10.1016/j.ymssp.2018.03.054

    Article  Google Scholar 

  53. Gul M, Catbas FN (2009) Statistical pattern recognition for Structural Health Monitoring using time series modeling: theory and experimental verifications. Mech Syst Signal Process 23(7):2192–2204. https://doi.org/10.1016/j.ymssp.2009.02.013

    Article  Google Scholar 

  54. Nair KK, Kiremidjian AS, Law KH (2006) Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. J Sound Vib 291(1–2):349–368. https://doi.org/10.1016/j.jsv.2005.06.016

    Article  Google Scholar 

  55. Wang QA, Dai Y, Ma ZG, Ni YQ, Tang JQ, Xu XQ, Wu ZY (2022) Towards probabilistic data-driven damage detection in SHM using sparse Bayesian learning scheme. Struct Control Health Monitor 29(11):e3070. https://doi.org/10.1002/stc.3070

    Article  Google Scholar 

  56. Krishnan Nair K, Kiremidjian AS (2007) Time series based structural damage detection algorithm using Gaussian mixtures modeling. J Dyn Syst Meas Control. https://doi.org/10.1115/1.2718241

    Article  Google Scholar 

  57. Stubbs N, Osegueda R (1990) Global damage detection in solids- experimental verification. Int J Anal Exp Modal Anal 5:81–97

    Google Scholar 

  58. Khiem NT, Hai TT, Huong LQ (2022) Crack identification of functionally graded beam using distributed piezoelectric sensor. J Vib Control. https://doi.org/10.1177/10775463221095649

    Article  Google Scholar 

  59. Sarah J, Hejazi F, Rashid RS, Ostovar N (2019) A review of dynamic analysis in frequency domain for structural health monitoring. IOP Conf Series: Earth Environ Sci 357(1):012007. https://doi.org/10.1088/1755-1315/357/1/012007

    Article  Google Scholar 

  60. Doebling SW, Farrar CR, Prime MB (1998) A summary review of vibration-based damage identification methods. Shock Vibration Digest 30(2):91–105

    Article  Google Scholar 

  61. Morassi A (2001) Identification of a crack in a rod based on changes in a pair of natural frequencies. J Sound Vib 242(4):577–596. https://doi.org/10.1006/jsvi.2000.3380

    Article  Google Scholar 

  62. Salawu OS (1997) Detection of structural damage through changes in frequency: a review. Eng Struct 19(9):718–723. https://doi.org/10.1016/S0141-0296(96)00149-6

    Article  Google Scholar 

  63. Capecchi D, Ciambella J, Pau A, Vestroni F (2016) Damage identification in a parabolic arch by means of natural frequencies, modal shapes and curvatures. Meccanica 51(11):2847–2859

    Article  Google Scholar 

  64. Weng S, Zhu H, Xia Y, Li J, Tian W (2020) A review on dynamic substructuring methods for model updating and damage detection of large-scale structures. Adv Struct Eng 23(3):584–600. https://doi.org/10.1177/1369433219872429

    Article  Google Scholar 

  65. Zhang Y, Wang L, Xiang Z (2012) Damage detection by mode shape squares extracted from a passing vehicle. J Sound Vib 331(2):291–307. https://doi.org/10.1016/j.jsv.2011.09.004

    Article  Google Scholar 

  66. Chaudhari CC, Gaikwad JA, Bhanuse VR, Kulkarni JV (2014) Experimental investigation of crack detection in cantilever beam using vibration analysis. In: 2014 First International Conference on Networks & Soft Computing (ICNSC2014) (pp 130–134). IEEE. https://doi.org/10.1109/CNSC.2014.6906685

  67. Chinka SSB, Putti SR, Adavi BK (2021) Modal testing and evaluation of cracks on cantilever beam using mode shape curvatures and natural frequencies. Structures 32:1386–1397. https://doi.org/10.1016/j.istruc.2021.03.049

    Article  Google Scholar 

  68. Williams EJ, Messina A (1999) Applications of the multiple damage location assurance criterion. In: Key Engineering Materials (Vol 167, pp 256–264). Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/KEM.167-168.256

  69. Adams DE, Farrar CR (2002) Classifying linear and nonlinear structural damage us-ing frequency domain arx models. Struct Health Monit 1(2):185–201

    Article  Google Scholar 

  70. Liu W, Li C, Ma L, Du L (2023) A frequency-domain formulation for predicting ground-borne vibration induced by underground train on curved track. J Sound Vib 549:117578. https://doi.org/10.1016/j.jsv.2023.117578

    Article  Google Scholar 

  71. Aktan AE, Ciloglu SK, Grimmelsman KA, Pan Q, Catbas FN (2005) Opportunities and challenges in health monitoring of constructed systems by modal analysis. In: Proceedings of the international conference on experimental vibration analysis for civil engineering structures, vol 200, pp 11–34, October 2005

  72. Lynch JP, Wang Y, Loh KJ, Yi JH, Yun CB (2006) Performance monitoring of the Geumdang Bridge using a dense network of high-resolution wireless sensors. Smart Mater Struct 15(6):1561. https://doi.org/10.1088/0964-1726/15/6/008

    Article  Google Scholar 

  73. Comanducci G, Ubertini F, Materazzi AL (2015) Structural health monitoring of suspension bridges with features affected by changing wind speed. J Wind Eng Ind Aerodyn 141:12–26. https://doi.org/10.1016/j.jweia.2015.02.007

    Article  Google Scholar 

  74. Nagayama T, Reksowardojo AP, Su D, Mizutani T (2017) Bridge natural frequency estimation by extracting the common vibration component from the responses of two vehicles. Eng Struct 150:821–829. https://doi.org/10.1016/j.engstruct.2017.07.040

    Article  Google Scholar 

  75. Soh CK, Tseng KK, Bhalla S, Gupta A (2000) Performance of smart piezoceramic patches in health monitoring of a RC bridge. Smart Mater Struct 9(4):533. https://doi.org/10.1088/0964-1726/9/4/317

    Article  Google Scholar 

  76. Nikolakopoulos PG, Katsareas DE, Papadopoulos CA (1997) Crack identification in frame structures. Comput Struct 64(1–4):389–406. https://doi.org/10.1016/S0045-7949(96)00120-4

    Article  Google Scholar 

  77. Goulet JA, Michel C, Kiureghian AD (2015) Data-driven post-earthquake rapid structural safety assessment. Earthq Eng Struct Dynam 44(4):549–562. https://doi.org/10.1002/eqe.2541

    Article  Google Scholar 

  78. Rolek P, Bruni S, Carboni M (2016) Condition monitoring of railway axles based on low frequency vibrations. Int J Fatig 86:88–97. https://doi.org/10.1016/j.ijfatigue.2015.07.004

    Article  Google Scholar 

  79. Doebling SW, Farrar CR, Prime MB, Shevitz DW (1996) Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review. Technical report. https://doi.org/10.2172/249299. https://www.osti.gov/servlets/purl/249299

  80. Lin RM, Zhu J (2006) Model updating of damped structures using FRF data. Mech Syst Signal Process 20(8):2200–2218. https://doi.org/10.1016/j.ymssp.2006.05.008

    Article  Google Scholar 

  81. Cheraghi N, Zou GP, Taheri F (2005) Piezoelectric-based degradation assessment of a pipe using fourier and wavelet analyses. Comput-Aided Civ Infrastruct Eng 20(5):369–382. https://doi.org/10.1111/j.1467-8667.2005.00403.x

    Article  Google Scholar 

  82. Amezquita-Sanchez JP, Osornio-Rios RA, Romero-Troncoso RJ, Dominguez-Gonzalez A (2012) Hardware-software system for simulating and analyzing earthquakes applied to civil structures. Nat Hazards Earth Syst Sci 12(1):61–73. https://doi.org/10.5194/nhess-12-61-2012,2012

    Article  Google Scholar 

  83. Philibert M, Soutis C, Gresil M, Yao K (2018) Damage detection in a composite T-joint using guided lamb waves. Aerospace 5(2):40. https://doi.org/10.3390/aerospace5020040

    Article  Google Scholar 

  84. Zenzen R, Belaidi I, Khatir S, Wahab MA (2018) A damage identification technique for beam-like and truss structures based on FRF and Bat Algorithm. Comptes Rendus Mécanique 346(12):1253–1266. https://doi.org/10.1016/j.crme.2018.09.003

    Article  Google Scholar 

  85. Pu Q, Hong Y, Yang CL, S, Xu X, (2019) Model updating–based damage detection of a concrete beam utilizing experimental damped frequency response functions. Adv Struct Eng 22(4):935–947. https://doi.org/10.1177/1369433218789556

    Article  Google Scholar 

  86. Zhang J, Guo SL, Wu ZS, Zhang QQ (2015) Structural identification and damage detection through long-gauge strain measurements. Eng Struct 99:173–183. https://doi.org/10.1016/j.engstruct.2015.04.024

    Article  Google Scholar 

  87. Cheng L, Cigada A (2017) Experimental strain modal analysis for beam-like structure by using distributed fiber optics and its damage detection. Measur Sci Technol 28(7):074001. https://doi.org/10.1088/1361-6501/aa6c8c

    Article  Google Scholar 

  88. Lin JH, Loh CH (2017) Structural damage detection using high dimension data reduction and visualization techniques. In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2017 (Vol 10168, pp 804–817). SPIE

  89. Sipple JD, Sanayei M (2014) Finite element model updating of the UCF grid benchmark using measured frequency response functions. Mech Syst Signal Process 46(1):179–190. https://doi.org/10.1016/j.ymssp.2014.01.008

    Article  Google Scholar 

  90. Fallahian M, Khoshnoudian F, Talaei S (2018) Application of couple sparse coding ensemble on structural damage detection. Smart Struct Syst 21(1):001–014

    Google Scholar 

  91. Lynch JP, Sundararajan A, Law KH, Kiremidjian AS, Kenny T, Carryer E (2003) Embedment of structural monitoring algorithms in a wireless sensing unit. Struct Eng Mech 15(3):285–297

    Article  Google Scholar 

  92. Alsaadi A, Shi Y, Jia Y (2020) Delamination detection via reconstructed frequency response function of composite structures. In: Proceedings of the 13th international conference on damage assessment of structures (pp 837–843). Springer, Singapore

  93. Yang J, Lam HF (2013) Model updating based structural damage detection of transmission tower: experimental verification by a scaled-model. Austr J Multi-Discip Eng 10(2):129–144

    Article  Google Scholar 

  94. El-Shafie A, Noureldin A, McGaughey D, Hussain A (2012) Fast orthogonal search (FOS) versus fast Fourier transform (FFT) as spectral model estimations techniques applied for structural health monitoring (SHM). Struct Multidiscip Optim 45(4):503–513

    Article  Google Scholar 

  95. Lee ET, Eun HC (2014) Damage detection of beam structure using response data measured by strain gages. J Vibroeng 16(1):147–155

    Google Scholar 

  96. Wu J, Li H, Ye F, Ma K (2019) Damage identification of bridge structure based on frequency domain decomposition and strain mode. J Vibroeng 21(8):2096–2105

    Article  Google Scholar 

  97. Perez-Ramirez CA, Machorro-Lopez JM, Valtierra-Rodriguez M, Amezquita-Sanchez JP, Garcia-Perez A, Camarena-Martinez D, Romero-Troncoso RDJ (2020) Location of multiple damage types in a truss-type structure using multiple signal classification method and vibration signals. Mathematics 8(6):932. https://doi.org/10.3390/math8060932

    Article  Google Scholar 

  98. Gkoktsi K, Giaralis A (2020) A compressive MUSIC spectral approach for identification of closely-spaced structural natural frequencies and post-earthquake damage detection. Prob Eng Mech 60:103030

    Article  Google Scholar 

  99. Lee J, Kim S (2007) Structural damage detection in the frequency domain using neural networks. J Intell Mater Syst Struct 18(8):785–792. https://doi.org/10.1177/1045389X06073640

    Article  Google Scholar 

  100. Zhang FL, Yang YP, Xiong HB, Yang JH, Yu Z (2019) Structural health monitoring of a 250-m super-tall building and operational modal analysis using the fast Bayesian FFT method. Struct Control Health Monitor 26(8):e2383. https://doi.org/10.1002/stc.2383

    Article  Google Scholar 

  101. Shadan F, Khoshnoudian F, Esfandiari A (2018) Structural damage identification based on strain frequency response functions. Int J Struct Stab Dyn 18(12):1850159. https://doi.org/10.1142/S0219455418501596

    Article  Google Scholar 

  102. Diez A, Khoa NLD, Makki Alamdari M, Wang Y, Chen F, Runcie P (2016) A clustering approach for structural health monitoring on bridges. J Civ Struct Heal Monit 6:429–445. https://doi.org/10.1007/s13349-016-0160-0

    Article  Google Scholar 

  103. Jiang X, Adeli H (2007) Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings. Int J Numer Meth Eng 71(5):606–629. https://doi.org/10.1002/nme.1964

    Article  Google Scholar 

  104. Radzieński M, Krawczuk M, Palacz M (2011) Improvement of damage detection methods based on experimental modal parameters. Mech Syst Signal Process 25(6):2169–2190. https://doi.org/10.1016/j.ymssp.2011.01.007

    Article  Google Scholar 

  105. Frizzarin M, Feng MQ, Franchetti P, Soyoz S, Modena C (2010) Damage detection based on damping analysis of ambient vibration data. Struct Control Health Monitor: The Off J Int Assoc Struct Control Monit Eur Assoc Control Struct 17(4):368–385. https://doi.org/10.1002/stc.296

    Article  Google Scholar 

  106. Mustafa S, Matsumoto Y, Yamaguchi H (2018) Vibration-based health monitoring of an existing truss bridge using energy-based damping evaluation. J Bridg Eng 23(1):04017114. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001159

    Article  Google Scholar 

  107. Dilena M, Limongelli MP, Morassi A (2015) Damage localization in bridges via the FRF interpolation method. Mech Syst Signal Process 52:162–180. https://doi.org/10.1016/j.ymssp.2014.08.014

    Article  Google Scholar 

  108. Feng D, Feng MQ (2016) Output-only damage detection using vehicle-induced displacement response and mode shape curvature index. Struct Control Health Monit 23(8):1088–1107. https://doi.org/10.1002/stc.1829

    Article  Google Scholar 

  109. Li J, Hao H, Lo JV (2015) Structural damage identification with power spectral density transmissibility: numerical and experimental studies. Smart Struct Syst, An Int J 15(1):15–40

    Article  Google Scholar 

  110. Maia NM, Almeida RA, Urgueira AP, Sampaio RP (2011) Damage detection and quantification using transmissibility. Mech Syst Signal Process 25(7):2475–2483. https://doi.org/10.1016/j.ymssp.2011.04.002

    Article  Google Scholar 

  111. Ay AM, Khoo S, Wang Y (2019) Probability distribution of decay rate: a statistical time-domain damping parameter for structural damage identification. Struct Health Monit 18(1):66–86. https://doi.org/10.1177/1475921718817336

    Article  Google Scholar 

  112. Capecchi D, Ciambella J, Pau A, Vestroni F (2016) Damage identification in a parabolic arch by means of natural frequencies, modal shapes and curvatures. Meccanica 51:2847–2859. https://doi.org/10.1007/s11012-016-0510-3

    Article  Google Scholar 

  113. Whalen TM (2008) The behavior of higher order mode shape derivatives in damaged, beam-like structures. J Sound Vib 309(3–5):426–464. https://doi.org/10.1016/j.jsv.2007.07.054

    Article  Google Scholar 

  114. Gauthier JF, Whalen TM, Liu J (2008) Experimental validation of the higher-order derivative discontinuity method for damage identification. Struct Control Health Monitor: The Off J Int Assoc Struct Control Monitor Eur Assoc Control Struct 15(2):143–161. https://doi.org/10.1002/stc.210

    Article  Google Scholar 

  115. Ratcliffe CP (1997) Damage detection using a modified Laplacian operator on mode shape data. J Sound Vib 204(3):505–517. https://doi.org/10.1006/jsvi.1997.0961

    Article  Google Scholar 

  116. Chandrashekhar M, Ganguli R (2009) Damage assessment of structures with uncertainty by using mode-shape curvatures and fuzzy logic. J Sound Vib 326(3–5):939–957. https://doi.org/10.1016/j.jsv.2009.05.030

    Article  Google Scholar 

  117. Sazonov E, Klinkhachorn P (2005) Optimal spatial sampling interval for damage detection by curvature or strain energy mode shapes. J Sound Vib 285(4–5):783–801. https://doi.org/10.1016/j.jsv.2004.08.021

    Article  Google Scholar 

  118. Li H, Huang Y, Ou J, Bao Y (2011) Fractal dimension-based damage detection method for beams with a uniform cross-section. Comput-Aided Civ Infrastruct Eng 26(3):190–206. https://doi.org/10.1111/j.1467-8667.2010.00686.x

    Article  Google Scholar 

  119. Cao M, Cheng L, Su Z, Xu H (2012) A multi-scale pseudo-force model in wavelet domain for identification of damage in structural components. Mech Syst Signal Process 28:638–659. https://doi.org/10.1016/j.ymssp.2011.11.011

    Article  Google Scholar 

  120. Al-Ghalib A, Mohammad F (2018) The use of modal parameters in structural health monitoring. In: MATEC Web of Conferences (Vol 162, p 04020). EDP Sciences. https://doi.org/10.1051/matecconf/201816204020

  121. Zhou K, Li QS, Zhi LH, Han XL, Xu K (2023) Investigation of modal parameters of a 600-m-tall skyscraper based on two-year-long structural health monitoring data and five typhoons measurements. Eng Struct 274:115162. https://doi.org/10.1016/j.engstruct.2022.115162

    Article  Google Scholar 

  122. Camarena-Martinez D, Osornio-Rios R, Romero-Troncoso RJ, Garcia-Perez A (2015) Fused empirical mode decomposition and MUSIC algorithms for detecting multiple combined faults in induction motors. J Appl Res Technol 13(1):160–167

    Article  Google Scholar 

  123. Mottershead J, Mares C (2000) Selection and updating parameters for an aluminium space-frame model. Mech Syst Signal Process 14(6):923–944

    Article  Google Scholar 

  124. Rabi BRM, Nagaraj P (2015) Finite element model updating of a space vehicle first stage motor based on experimental test results. Aerosp Sci Technol 45:422–430. https://doi.org/10.1016/j.ast.2015.06.014

    Article  Google Scholar 

  125. Rosenzveig G, Loufa F, Champaney L (2016) A FE model updating method for the simulation of the assembly process of large and lightweight aeronautical structures. Finite Elem Anal Des 111:56–63. https://doi.org/10.1016/j.finel.2015.12.006

    Article  Google Scholar 

  126. Yang Y, Chen Y (2009) A new direct method for updating structural models based on measured modal data. Eng Struct 31:32–42. https://doi.org/10.1016/j.engstruct.2008.07.011

    Article  Google Scholar 

  127. Wang D, Tan Z, Li Y, Liu Y (2014) Review of the application of finite element model updating to civil structures. Key Eng Mater 574(107–115):10. https://doi.org/10.4028/www.scientific.net/KEM.574.107

    Article  Google Scholar 

  128. Berman A, Nagy E (1983) Improvement of large analytical model using test data. Am Inst Aeronaut Astronaut J 21(8):1168–1173. https://doi.org/10.2514/3.60140

    Article  Google Scholar 

  129. Baruch M, Bar-Itzhack I (1978) Optimal weighted orthogonalization of measured modes. Am Inst Aeronaut Astronaut J 16(4):346–351. https://doi.org/10.2514/3.60896

    Article  Google Scholar 

  130. Caesar B (1986) Update and identification of dynamic mathematical models. In: The 4th international modal analysis conference, Los Angeles, CA

  131. Wei F (1990) Analytical dynamic model improvement using vibration test data. Am Inst Aeronaut Astronaut J 28(1):175–177. https://doi.org/10.2514/3.10371

    Article  Google Scholar 

  132. Mottershead J, Friswell M (1993) Model updating in structural dynamics: a survey. J Sound Vib 167(2):347–375. https://doi.org/10.1006/jsvi.1993.1340

    Article  Google Scholar 

  133. Marwala T (2010) Finite-element-model updating using computional intelligence techniques: Applications to structural dynamics. https://doi.org/10.1007/978-1-84996-323-7

  134. Alkayem NF, Cao M, Zhang Y, Bayat M, Su Z (2018) Structural damage detection using finite element model updating with evolutionary algorithms: a survey. Neural Comput Appl 30(2):389–411

    Article  Google Scholar 

  135. Imregun M, Visser WJ (1991) A review of model updating techniques. The Shock Vib Digest 23(1):9–20. https://doi.org/10.1177/058310249102300102

    Article  Google Scholar 

  136. Fritzen C, Jennewein D, Kiefer T (1998) Damage detection based on model updating methods. Mech Syst Signal Process 12(1):163–186. https://doi.org/10.1006/mssp.1997.0139

    Article  Google Scholar 

  137. Jaishi B, Kim H, Kim MK, Ren W, Lee S (2007) Finite element model updating of concrete-filled steel tubular arch bridge under operational condition using modal flexibility. Mech Syst Signal Process 21(6):2406–2426. https://doi.org/10.1016/j.ymssp.2007.01.003

    Article  Google Scholar 

  138. Farhat C, Hemez F (1993) Updating finite element dynamic models using an element-by-element sensitivity methodology. Am Inst Aeronaut Astronaut J 31(9):1702–1711. https://doi.org/10.2514/3.11833

    Article  Google Scholar 

  139. Zheng Z, Lu Z, Chena W, Liu J (2015) Structural damage identification based on power spectral density sensitivity analysis of dynamic responses. Comput Struct 146:176–184. https://doi.org/10.1016/j.compstruc.2014.10.011

    Article  Google Scholar 

  140. Arau´jo dos Santos J, Soares C, Mota Soares C, Pina H, (2000) A damage identification numerical model based on the sensitivity of orthogonality conditions and least squares techniques. Comput Struct 78(1–3):283–291. https://doi.org/10.1016/S0045-7949(00)00084-5

    Article  Google Scholar 

  141. Bakir P, Reynders E, Roeck G (2007) Sensitivity-based finite element model updating using constrained optimization with a trust region algorithm. J Sound Vib 305:211–225. https://doi.org/10.1016/j.jsv.2007.03.044

    Article  Google Scholar 

  142. Mordini A, Savov K, Wenzel H (2015) Damage detection on stay cables using an open source-based framework for finite element model updating. Struct Health Monit 7(2):91–102. https://doi.org/10.1177/1475921708089550

    Article  Google Scholar 

  143. Jung D, Kim C (2013) Finite element model updating on small-scale bridge model using the hybrid genetic algorithm. Struct Infrastruct Eng 9(5):481–495. https://doi.org/10.1080/15732479.2011.564635

    Article  Google Scholar 

  144. Marwala T (2010) Finite element model updating using computational intelligence techniques. Springer, London

    Book  Google Scholar 

  145. Beck J, Katafygiotis L (1998) Updating models and their uncertainties. I: Bayesian statistical framework. J Eng Mech 124(4):455–461

    Article  Google Scholar 

  146. Katafygiotis L, Beck J (1998) Updating models and their uncertainties. II: model identifiability. J Eng Mech 124(4):463–467

    Article  Google Scholar 

  147. Beck J, Yuen K (2004) Model selection using response measurements: Bayesian probabilistic approach. J Eng Mech 130(2):192–203. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:2(192)

    Article  Google Scholar 

  148. Sun H, Liu Y (2011) An improved Taguchi method and its application in finite element model updating of bridges. Key Eng Mater 456:51–65. https://doi.org/10.4028/www.scientific.net/KEM.456.51

    Article  Google Scholar 

  149. Marwala T, Mdlazi L, Sibisi S (2005) Finite element model updating using Bayesian framework and modal properties. J Aircr 42(1):275–287. https://doi.org/10.2514/1.11841

    Article  Google Scholar 

  150. Sohn H, Law K (1997) A Bayesian probabilistic approach for structure damage detection. Earthq Eng Struct Dyn 26:1259–1281. https://doi.org/10.1002/(SICI)1096-9845(199712)26:12%3c1259::AID-EQE709%3e3.0.CO;2-3

    Article  Google Scholar 

  151. Behmanesh I, Moaveni B (2015) Probabilistic identification of simulated damage on the Dowling Hall footbridge through Bayesian FE model updating. Struct Control Health Monit 22(3):463–483. https://doi.org/10.1002/stc.1684

    Article  Google Scholar 

  152. Kurata M, Kim J, Lynch J (2010) A probabilistic model updating algorithm for fatigue damage detection in aluminum hull structures. In: The ASME 2010 conference on smart materials, adaptive structures and intelligent systems, Philadelphia, Pennsylvania. https://doi.org/10.1115/SMASIS2010-3838

  153. Jiang X, Mahadevan S (2008) Bayesian probabilistic inference for nonparametric damage detection of structures. J Eng Mech 130(10):820–831. https://doi.org/10.1061/(ASCE)0733-9399(2008)134:10(820)

    Article  Google Scholar 

  154. Lam H, Katafygiotis L, Mickleborough N (2004) Application of a statistical model updating approach on phase I of the IASCASCE structural health monitoring benchmark study. J Eng Mech 130(1):34–48. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(34)

    Article  Google Scholar 

  155. Yuen K, Beck J, Au S (2004) Structural damage detection and assessment by adaptive Markov chain Monte Carlo simulation. Struct Control Health Monit 11:327–347. https://doi.org/10.1002/stc.47

    Article  Google Scholar 

  156. Teughels A, Roeck G, Suykens J (2003) Global optimization by coupled local minimizers and its application to FE model updating. Comput Struct 81(24–25):2337–2351. https://doi.org/10.1016/S0045-7949(03)00313-4

    Article  Google Scholar 

  157. Dubey A, Denis V, Serra R (2022) Sensitivity and efficiency of the frequency shift coefficient based on the damage identification algorithm: modeling uncertainty on natural frequencies. Vibration 5(1):59–79. https://doi.org/10.3390/vibration5010003

    Article  Google Scholar 

  158. Zhao JH, Zhang L (2012) Structural damage localization using DS evidence theory. Appli Mech Mater 105:999–1003

    Google Scholar 

  159. Yarnold MT, Moon FL (2015) Temperature-based structural health monitoring baseline for long-span bridges. Eng Struct 86:157–167. https://doi.org/10.1016/j.engstruct.2014.12.042

    Article  Google Scholar 

  160. Ozer E, Özcebe AG, Negulescu C, Kharazian A, Borzi B, Bozzoni F, Tubaldi E (2022) Vibration-based and near real-time seismic damage assessment adaptive to building knowledge level. Buildings 12(4):416. https://doi.org/10.3390/buildings12040416

    Article  Google Scholar 

  161. Cuong DQ, Chinh VD (2022) Estimation of overall fatigue life of jack-up leg structure. Civ Eng J 8(3):488–504

    Article  Google Scholar 

  162. Singh V, Sangle K (2022) Analysis of vertically oriented coupled shear wall interconnected with coupling beams. HighTech Innov J 3(2):230–242

    Article  Google Scholar 

  163. Li S, Sun L (2020) Detectability of bridge-structural damage based on fiber-optic sensing through deep-convolutional neural networks. J Bridg Eng 25(4):04020012. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001531

    Article  Google Scholar 

  164. Pathirage CSN, Li J, Li L, Hao H, Liu W, Ni P (2018) Structural damage identification based on autoencoder neural networks and deep learning. Eng Struct 172:13–28. https://doi.org/10.1016/j.engstruct.2018.05.109

    Article  Google Scholar 

  165. Maes K, Van Meerbeeck L, Reynders EPB, Lombaert G (2022) Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51. Mech Syst Signal Process 165:108380

    Article  Google Scholar 

  166. Rao MVV, Chaparala A (2022) A novel feature-based SHM assessment and predication approach for robust evaluation of damage data diagnosis systems. Wirel Pers Commun 124(4):3387–3411

    Article  Google Scholar 

  167. Ghiasi R, Torkzadeh P, Noori M (2016) A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function. Struct Health Monit 15(3):302–316. https://doi.org/10.1177/1475921716639587

    Article  Google Scholar 

  168. Yu Y, Wang C, Gu X, Li J (2019) A novel deep learning-based method for damage identification of smart building structures. Struct Health Monit 18(1):143–163. https://doi.org/10.1177/1475921718804132

    Article  Google Scholar 

  169. Rafiei MH, Adeli H (2017) A novel machine learning-based algorithm to detect damage in high-rise building structures. The Struct Des Tall Spec Build 26(18):e1400. https://doi.org/10.1002/tal.1400

    Article  Google Scholar 

  170. Muin S, Mosalam KM (2021) Structural health monitoring using machine learning and cumulative absolute velocity features. Appl Sci 11(12):5727. https://doi.org/10.3390/app11125727

    Article  Google Scholar 

  171. Zhang Y, Miyamori Y, Mikami S, Saito T (2019) Vibration-based structural state identification by a 1-dimensional convolutional neural network. Comput-Aided Civ Infrastruct Eng 34(9):822–839. https://doi.org/10.1111/mice.12447

    Article  Google Scholar 

  172. Lin YZ, Nie ZH, Ma HW (2017) Structural damage detection with automatic feature-extraction through deep learning. Comput-Aided Civ Infrastruct Eng 32(12):1025–1046. https://doi.org/10.1111/mice.12313

    Article  Google Scholar 

  173. Luckey D, Fritz H, Legatiuk D, Peralta Abadía JJ, Walther C, Smarsly K (2022) Explainable artificial intelligence to advance structural health monitoring. Structural Health Monitoring Based on Data Science Techniques, pp 331–346

  174. Azimi M, Eslamlou AD, Pekcan G (2020) Data-driven structural health monitoring and damage detection through deep learning: state-of-the-art review. Sensors 20(10):2778. https://doi.org/10.3390/s20102778

    Article  Google Scholar 

  175. Favarelli E, Giorgetti A (2020) Machine learning for automatic processing of modal analysis in damage detection of bridges. IEEE Trans Instrum Meas 70:1–13. https://doi.org/10.1109/TIM.2020.3038288

    Article  Google Scholar 

  176. Zinno R, Haghshenas SS, Guido G, Rashvand K, Vitale A, Sarhadi A (2023) The state of the art of artificial intelligence approaches and new technologies in structural health monitoring of bridges. Appl Sci 13(1):97. https://doi.org/10.3390/app13010097

    Article  Google Scholar 

  177. Lai Z, Liu W, Jian X, Bacsa K, Sun L, Chatzi E (2022). Neural modal ODEs: integrating physics-based modeling with neural ODEs for modeling high dimensional monitored structures. arXiv preprint arXiv:2207.07883. https://doi.org/10.1017/dce.2022.35

  178. Gordan M, Razak HA, Ismail Z, Ghaedi K (2017) Recent developments in damage identification of structures using data mining. Latin Am J Solids Struct 14:2373–2401. https://doi.org/10.1590/1679-78254378

    Article  Google Scholar 

  179. Bao Y, Chen Z, Wei S, Xu Y, Tang Z, Li H (2019) The state of the art of data science and engineering in structural health monitoring. Engineering 5(2):234–242. https://doi.org/10.1016/j.eng.2018.11.027

    Article  Google Scholar 

  180. Rao M, Chaparala A (2022) A novel feature-based SHM assessment and predication approach for robust evaluation of damage data diagnosis systems. Wirel Pers Commun 124:1–25

    Article  Google Scholar 

  181. Sajedi SO, Liang X (2020) Vibration-based semantic damage segmentation for large-scale structural health monitoring. Comput-Aided Civ Infrastruct Eng 35(6):579–596. https://doi.org/10.1111/mice.12523

    Article  Google Scholar 

  182. Das S, Dhang N (2022) Damage identification of structures using incomplete mode shape and improved TLBO-PSO with self-controlled multi-stage strategy. Structures 35:1101–1124

    Article  Google Scholar 

  183. Ye XW, Jin T, Yun CB (2019) A review on deep learning-based structural health monitoring of civil infrastructures. Smart Struct Syst 24(5):567–585

    Google Scholar 

  184. Dey A, Miyani G, Debroy S, Sil A (2020) In-situ NDT investigation to estimate degraded quality of concrete on existing structure considering time-variant uncertainties. J Build Eng 27:101001. https://doi.org/10.1016/j.jobe.2019.101001

    Article  Google Scholar 

  185. Hassani S, Dackermann U (2023) A systematic review of advanced sensor technologies for non-destructive testing and structural health monitoring. Sensors 23(4):2204. https://doi.org/10.3390/s23042204

    Article  Google Scholar 

  186. Keshmiry A, Hassani S, Mousavi M, Dackermann U (2023) Effects of environmental and operational conditions on structural health monitoring and non-destructive testing: a systematic review. Buildings 13(4):918. https://doi.org/10.3390/buildings13040918

    Article  Google Scholar 

  187. Sharma S, Sen S (2023) Real-time structural damage assessment using LSTM networks: regression and classification approaches. Neural Comput Appl 35(1):557–572

    Article  Google Scholar 

  188. Luleci F, Catbas FN, Avci O (2023) Generative adversarial networks for labeled acceleration data augmentation for structural damage detection. J Civ Struct Heal Monit 13(1):181–198. https://doi.org/10.1007/s13349-022-00627-8

    Article  Google Scholar 

  189. Fathnejat H, Ahmadi-Nedushan B, Hosseininejad S, Noori M, Altabey WA (2023) A data-driven structural damage identification approach using deep convolutional-attention-recurrent neural architecture under temperature variations. Eng Struct 276:115311. https://doi.org/10.1016/j.engstruct.2022.115311

    Article  Google Scholar 

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  1. Department of Civil Engineering, Ecole Centrale School of Engineering, Mahindra University, Bahadurpally, Jeedimetla, Hyderabad, Telangana, 500043, India

    Rakesh Katam & Venkata Dilip Kumar Pasupuleti

  2. Department of Computer Science Engineering, Ecole Centrale School of Engineering, Mahindra University, Bahadurpally, Jeedimetla, Hyderabad, Telangana, 500043, India

    Prafulla Kalapatapu

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Katam, R., Pasupuleti, V.D.K. & Kalapatapu, P. A review on structural health monitoring: past to present. Innov. Infrastruct. Solut. 8, 248 (2023). https://doi.org/10.1007/s41062-023-01217-3

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