Although many studies analyze data in structural health monitoring under noisy conditions, few focus on results in high noise levels, where detecting changes, whether clear damage or hidden deterioration, presents a significant challenge due to environmental noise. In this study, a methodology is presented that combines robust algorithms proven effective in noisy environments. This approach not only identifies changes but also localizes and assesses the severity of deterioration and damage while mitigating noise impact on the final results. The methodology employs autocorrelation as an alternative to time history responses, which serves inputs to a discrete wavelet transform. Several statistical indices are then embedded to create the features of the algorithm. Gray wolf optimization is applied as a novel feature selection method in damage detection problems, yielding outstanding results. Integrating these selected features and applying a moving maximum filter produces a superior pattern that significantly enhances the outcomes. Finally, a supervised Ensemble learner is used to classify the data. The effectiveness of the proposed method is demonstrated through three case studies: a benchmark study consisting of 17 diverse linear and nonlinear scenarios under forced vibrations, a numerical deterioration model with 5 different scenarios in response to ambient vibrations, and an experimental model with 10 predefined damage scenarios subjected to forced chirp signals. The results show high accuracy, exceeding 90% across all scenarios, including original data and data contaminated with Gaussian noise at signal to noise ratio levels of 10, 5, 1, and 0.5 decibels.
GharehbaghiVRNoroozinejad FarsangiENooriM, et al. A critical review on structural health monitoring: definitions, methods, and perspectives. Arch Comput Methods Eng2021; 29: 2209–2235.
3.
FarrarCRWordenK.Structural health monitoring: a machine learning perspective. Hoboken, New Jersey: John Wiley & Sons, 2012.
4.
MahmoudiHBitarafMSalkhordehM, et al. (eds). A rapid machine learning-based damage detection algorithm for identifying the extent of damage in concrete shear-wall buildings. Structures2023; 47: 482–499.
5.
KullaaJ. Damage detection and localization using autocorrelation functions with spatiotemporal correlation. In: WuZNagayamaTDangJ, et al. (eds) Experimental Vibration Analysis for Civil Engineering Structures: Select Proceedings of the EVACES 2021, 24 August 2022, pp. 83–95. Cham: Springer International Publishing, Springer Nature Switzerland.
6.
JiangYShaoLXiangJ (eds). Combination of wavelet transform and extreme learning machine for detecting damages in composite plates. Structures2023; 57: 105242.
7.
YangWZhangZChenX.Vibration-based looseness identification of bolted structures via quasi-analytic wavelet packet and optimized large margin distribution machine. Struct Health Monit2023; 23: 856–875.
8.
MousaviAAZhangCMasriSF, et al. Damage detection and localization of a steel truss bridge model subjected to impact and white noise excitations using empirical wavelet transform neural network approach. Measurement2021; 185: 110060.
9.
MollJKexelCKatholJ, et al. Guided waves for damage detection in complex composite structures: the influence of omega stringer and different reference damage size. Appl Sci2020; 10(9): 3068.
10.
SvendsenBTFrøsethGTRönnquistA.Damage detection applied to a full-scale steel bridge using temporal moments. Shock Vib2020; 2020: 1–16.
11.
JovićABrkićKBogunovićN (eds). A review of feature selection methods with applications. In: 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), Croatia, 2015.
GharehbaghiVRFarsangiENYangT, et al. (eds). Deterioration and damage identification in building structures using a novel feature selection method. Structures2021; 29: 458–470.
14.
RostamiMBerahmandKForouzandehS.A novel community detection based genetic algorithm for feature selection. J Big Data2021; 8(1): 1–27.
15.
Babajanian BishehHGhodrati AmiriGNekooeiM, et al. Damage detection of a cable-stayed bridge based on combining effective intrinsic mode functions of empirical mode decomposition using the feature selection technique. Inverse Probl Sci Eng2021; 29(6): 861–881.
16.
ChengLWangYLiuX, et al. (eds). Outlier detection ensemble with embedded feature selection. Proceedings of the AAAI conference on artificial intelligence, USA, 2020.
17.
GharehbaghiVRNguyenAFarsangiEN, et al. Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach. J Build Eng2020; 30: 101292.
18.
GharehbaghiVRKalbkhaniHNoroozinejad FarsangiE, et al. A data-driven approach for linear and nonlinear damage detection using variational mode decomposition and GARCH model. Eng Comput2023; 39(3): 2017–2034.
19.
HeXWangCZhengR, et al. GPR image noise removal using grey wolf optimisation in the NSST domain. Remote Sens2021; 13(21): 4416.
20.
AbdullahMMalikTNAhmedA, et al. A novel hybrid GWO-LS estimator for harmonic estimation problem in time varying noisy environment. Energies2021; 14(9): 2587.
21.
LuCGaoLPanQ, et al. A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution. Appl Soft Comput2019; 75: 728–749.
22.
OvanesovaASuarezLE.Applications of wavelet transforms to damage detection in frame structures. Eng Struct2004; 26(1): 39-49.
23.
DaubechiesI. Ten lectures on wavelets. University City, Philadelphia: SIAM, 1992.
24.
SilikANooriMAltabeyWA, et al. Selecting optimum levels of wavelet multi-resolution analysis for time-varying signals in structural health monitoring. Struct Control Health Monit2021; 28(8): e2762.
25.
MotlaghMMBaharABaharO (eds). Damage detection in a 3D wind turbine tower by using extensive multilevel 2D wavelet decomposition and heat map, including soil-structure interaction. Structures2021; 29: 458–470.
26.
SalehiMGonenSErduranE (eds). A critical look at the use of wavelets in damage detection. In: European workshop on structural health monitoring, Palermo, Italy, 2022.
27.
AkujuobiCM.Wavelets and wavelet transform systems and their applications. Berlin/Heidelberg, Germany: Springer, 2022.
28.
NielsenM.On the construction and frequency localization of finite orthogonal quadrature filters. J Approximation Theory2001; 108(1): 36–52.
29.
DuYZhouSJingX, et al. Damage detection techniques for wind turbine blades: a review. Mech Syst Signal Process2020; 141: 106445.
30.
LiuZArdabilianMZineA, et al. Crack damage identification of a thick composite sandwich structure based on Gaussian Processes classification. Compos Struct2021; 255: 112825.
31.
BhattacharyaSYadavNAhmadA, et al. Multiple damage detection in PZT sensor using dual point contact method. Sensors2022; 22(23): 9161.
32.
YinXHuangZLiuY (eds). Damage features extraction of prestressed near-surface mounted CFRP beams based on tunable Q-factor wavelet transform and improved variational modal decomposition. Structures, vol. 45, pp. 1949–1961. Elsevier, 2022.
33.
BurudNKishenJC.Damage detection using wavelet entropy of acoustic emission waveforms in concrete under flexure. Struct Health Monit2021; 20(5): 2461–2675.
34.
RazaviMHadidiA (eds). Structural damage identification through sensitivity-based finite element model updating and wavelet packet transform component energy. Structuresvol. 33, pp. 4857–4870. Elsevier, 2021.
35.
WuJEl NaggarMHWangK.Pile damage detection using machine learning with the multipoint traveling wave decomposition method. Sensors2023; 23(19): 8308.
36.
NigamRSinghSK (eds). Crack detection in a beam using wavelet transform and photographic measurements. Structures, vol. 25, pp. 436–447. Elsevier, 2020
37.
DiabANestorovićT.Numerical investigation of the time-of-flight and wave energy dependent hybrid method for structural damage detection. J Vib Eng Technol2023; 11(6): 2689–2707.
38.
MajidiNRiahiHTZandiSM (eds). Evaluating the performance of different mother wavelet functions for down-sampling of earthquake records. Structures, vol. 51, pp. 846–879. Elsevier, 2023.
39.
QuqaSLandiLDiotalleviP (eds). Real time damage detection through single low-cost smart sensor. In: Proceedings of the 7th international conference on computational methods in structural dynamics and earthquake engineering, Crete, Greece, 2019.
40.
QuqaSLandiL.Damage detection in nonlinear elastic structures using individual sensors. Buildings2023; 13(3): 639.
41.
SoleymaniAJahangirHRashidiM, et al. Damage identification in reinforced concrete beams using wavelet transform of modal excitation responses. Buildings2023; 13(8): 1955.
42.
AmirruddinMAdzmanMRHalimSA, et al. (eds). Evaluation of denoising performance for noisy arc fault signal based on mother wavelet selection. In: 2019 IEEE Asia-Pacific conference on applied electromagnetics (APACE), Malacca, Malaysia, 2019.
43.
YanNLiJDackermannU, et al. (eds). A numerical investigation on the damage identification of timber utility poles based on wavelet packet energy. In: Proceedings of the 23rd Australasian conference on the mechanics of structures and materials (ACMSM23), Byron Bay, NSW, Australia, 9–12 December 2014.
44.
MujicaLERuizMVillamizarR.Multidimensional data statistical processing of magnetic flow leakage signals from a Colombian gas pipeline. Struct Health Monit2021; 20(6): 2963–2992.
45.
CaoJHeHZhangY, et al. Crack detection in ultrahigh-performance concrete using robust principal component analysis and characteristic evaluation in the frequency domain. Struct Health Monit2024; 23: 1013–1024.
46.
LiYChengGMaS, et al. Bearing fault diagnosis method based on complete center frequency distribution feature. Struct Health Monit2023; 22(6): 4100–4116.
47.
henLChoyYSWangTG, et al. Fault detection of wheel in wheel/rail system using kurtosis beamforming method. Struct Health Monit2019; 19(2): 495–509.
48.
VogguSSasmalS.Dynamic nonlinearities for identification of the breathing crack type damage in reinforced concrete bridges. Struct Health Monit2020; 20(1): 339–359.
49.
MirjaliliSMirjaliliSMLewisA.Grey wolf optimizer. Adv Eng Software2014; 69: 46–61.
50.
Al-TashiQAbdulkadirSJRaisHM, et al. Binary multi-objective grey wolf optimizer for feature selection in classification. IEEE Access2020; 8: 106247–106263.
51.
GhannadiPKourehliSSNooriM, et al. Efficiency of grey wolf optimization algorithm for damage detection of skeletal structures via expanded mode shapes. Adv Struct Eng2020; 23(13): 2850–2865.
52.
FarisHAljarahIAl-BetarMA, et al. Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl2018; 30: 413–435.
53.
GuQLiXJiangS.Hybrid genetic grey wolf algorithm for large-scale global optimization. Complexity2019; 2019(1): 2653512.
54.
KouchakiMSalkhordehMMashayekhiM, et al. Damage detection in power transmission towers using machine learning algorithms. Structures2023; 56: 104980.
55.
EslamlouADKavehAAzimiM, et al. Structural health monitoring via a group-theoretic WSA for optimal feature selection and data fusion. Structures2023; 57: 105280.
56.
HamidiaMMansourdehghanSAsjodiAH, et al. Machine learning-based seismic damage assessment of non-ductile RC beam-column joints using visual damage indices of surface crack patterns. Structures2022; 45: 2038–2050.
57.
LiuLÖzsuMT.Encyclopedia of database systems. New York, NY: Springer, 2009.
58.
VeisiH.Introduction to SVM. In: RadJAParandKChakravertyS (eds) Learning with fractional orthogonal Kernel classifiers in support vector machines: theory, algorithms and applications. Singapore: Springer Nature Singapore, 2023, pp. 3–18.
59.
SuthaharanS.Machine learning models and algorithms for big data classification. Integr Ser Inf Syst2016; 36: 1–12.
GuoGWangHBellD, et al. (eds). KNN model-based approach in classification. In: On the move to meaningful internet systems 2003: CoopIS, DOA, and ODBASE, Catania, Sicily, Italy, 2003.
62.
YangY. Chapter 4—Ensemble learning. In: YangY (ed.). Temporal data mining via unsupervised ensemble learning. Amsterdam, The Netherlands: Elsevier, 2017, pp. 35–56.
63.
FigueiredoEParkGFarrarCR, et al. Machine learning algorithms for damage detection under operational and environmental variability. Struct Health Monit2010; 10(6): 559–572.
64.
Omori YanoMda SilvaSFigueiredoE, et al. Damage quantification using transfer component analysis combined with Gaussian process regression. Struct Health Monit2023; 22(2): 1290–1307.
65.
FigueiredoEParkGFigueirasJ, et al. Structural health monitoring algorithm comparisons using standard data sets. Los Alamos, NM: Los Alamos National Laboratory (LANL), 2009.
66.
FigueiredoEParkGFigueirasJ, et al. Structural health monitoring algorithm comparisons using standard data sets. Los Alamos, NM: Los Alamos National Lab, 2009.
67.
GhahremaniBBitarafMGhorbani-TanhaAK, et al., (eds). Structural damage identification based on fast S-transform and convolutional neural networks. Structures2021; 29: 1199–1209.
68.
HouYHuWWangX, et al. Damage identification of ancient timber structure based on autocorrelation function. Adv Civ Eng2021; 2021(1): 6683666.
69.
NairKKKiremidjianASLawKH. Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. J Sound Vib2006; 291(1–2): 349–368.
70.
RamsagarPKPardueSJ.Damage detection method using autocorrelation. In: InIMAC-XIX: a conference on structural dynamics, Hyatt Orlando, Kissimmee, Florida, 2001, pp. 1622–1627.
71.
HyndmanRJ.Forecasting: principles and practice. Melbourne, Australia: OTexts, 2018.
72.
TanjiAKJrde BritoMAAlvesMG, et al. Improved noise cancelling algorithm for electrocardiogram based on moving average adaptive filter. Electronics2021; 10(19): 2366.
73.
KimGInJHLeeY, et al. Mott neurons with dual thermal dynamics for spatiotemporal computing. Nat Mater2024; 23: 1237–1244.
GharehbaghiVRKalbkhaniHNoroozinejad FarsangiE, et al. A data-driven approach for linear and nonlinear damage detection using variational mode decomposition and GARCH model. Eng with comput2023; 39(3): 2017–2034.
