Fibre-reinforced composite materials are extensively used for manufacturing critical engineering components in diverse applications, which demands intelligent and reliable structural health monitoring (SHM) schemes to prevent catastrophic failures associated with composite structures. Composite materials have complex failure mechanisms, and it is essential to employ reliable SHM methods with high accuracy to detect damages at the incipient stage. Although there are several SHM technologies available, no single strategy is impeccable for tackling all damage types due to the incredibly complex failure mechanisms of the composite materials. Machine learning (ML) methods are frequently integrated to compensate for the limitations of the traditional SHM methods. This paper presents the state-of-the-art sensory methods and deep learning (DL) techniques while emphasizing the future directions for the engineering and scientific community interested in developing novel SHM systems for fibre-reinforced polymer composite structures intended for civil, aerospace, automotive, marine, oil and gas exploration industries.
GüemesAFernandez-LopezAPozoAR, et al.Structural health monitoring for advanced composite structures: a review. J Compos Sci2020; 4(1): 13. DOI: 10.3390/jcs4010013.
2.
SonySLaventureSSadhuA. A literature review of next-generation smart sensing technology in structural health monitoring. Struct Control Heal Monit2019; 26(3): 1–22. DOI: 10.1002/stc.2321.
3.
YuanF-GZargarSAChenQ, et al.Machine learning for structural health monitoring: challenges and opportunities. Bellingham, DC: SPIE, 2020, p. 2. DOI: 10.1117/12.2561610.
4.
AzimiMEslamlouADPekcanG. Data-driven structural health monitoring damage detection through deep learning: state-of-the- art review. Sensor2020; 20(10): 2778.
5.
FlahMNunezIBen ChaabeneW, et al.Machine learning algorithms in civil structural health monitoring: a systematic review. Arch Comput Methods Eng2020; 28: 0123456789. DOI: 10.1007/s11831-020-09471-9.
6.
XuDLiuPFChenZP, et al.Delamination analysis of carbon fiber/epoxy composite laminates under different loading rates using acoustic emission. J Fail Anal Prev2019; 19(4): 1034–1042. DOI: 10.1007/s11668-019-00691-1.
7.
MitshealADDiogoMOpukuroD, et al.A review of structural health monitoring techniques as applied to composite structures. Struct Durab Heal Monit2017; 11(2): 91–147. DOI: 10.3970/sdhm.2017.011.091.
8.
BalajiRSasikumarM. Structural health monitoring (SHM) system for polymer composites: a review. Indian J Sci Technol2016; 9(41): 1–12, doi:10.17485/ijst/2016/v9i41/85832.
9.
RajakDKPagarDDKumarR, et al.Recent progress of reinforcement materials: a comprehensive overview of composite materials. J Mater Res Technol2019; 8(6): 6354–6374. DOI: 10.1016/j.jmrt.2019.09.068.
10.
AleksendrićDCarloneP. Introduction to composite materials, 2015.
11.
ZanganaSEpaarachchiJFerdousW, et al.A novel hybridised composite sandwich core with glass, kevlar and zylon fibres – investigation under low-velocity impact. Int J Impact Eng2019; 137: 103430. DOI: 10.1016/j.ijimpeng.2019.103430.
12.
ZanganaSEpaarachchiJFerdousW, et al.Behaviour of continuous fibre composite sandwich core under low-velocity impact. Thin Walled Struct2020; 158: 107157. DOI: 10.1016/j.tws.2020.107157.
13.
MontalvãoDRibeiroAMRDuarte-SilvaJAB. Experimental assessment of a modal-based multi-parameter method for locating damage in composite laminates. Exp Mech2011; 51(9): 1473–1488. DOI: 10.1007/s11340-011-9472-5.
14.
DuchenePChakiSAyadiA, et al.A review of non-destructive techniques used for mechanical damage assessment in polymer composites. J Mater Sci2018; 53(11): 7915–7938. DOI: 10.1007/s10853-018-2045-6.
15.
ManoachEWarminskiJKlodaL, et al.Numerical and experimental studies on vibration based methods for detection of damage in composite beams. Compos Struct2017; 170: 26–39. DOI: 10.1016/j.compstruct.2017.03.005.
16.
TohGParkJ. Review of vibration-based structural health monitoring using deep learning. Appl Sci2020; 10(5): 1680. DOI: 10.3390/app10051680.
17.
Di SanteR. Fibre optic sensors for structural health monitoring of aircraft composite structures: recent advances and applications. Sensors (Switzerland)2015; 15(8): 18666–18713. DOI: 10.3390/s150818666.
18.
KahandawaGCEpaarachchiJAWangH, et al.Use of fixed wavelength fibre-bragg grating (FBG) filters to capture time domain data from the distorted spectrum of an embedded FBG sensor to estimate strain with an artificial neural network. Sensors Actuat A Phys2013; 194: 1–7. DOI: 10.1016/j.sna.2012.12.028.
19.
KahandawaGCEpaarachchiJWangH, et al.Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network. Meas J Int Meas Confed2013; 46(10): 4045–4051. DOI: 10.1016/j.measurement.2013.07.029.
20.
HafiziZMEpaarachchiJLauKT. Impact location determination on thin laminated composite plates using an NIR-FBG sensor system. Meas J Int Meas Confed2015; 61: 51–57. DOI: 10.1016/j.measurement.2014.08.040.
21.
KakeiAAIslamMLengJ, et al.Use of an elasto-plastic model and strain measurements of embedded fibre bragg grating sensors to detect mode I delamination crack propagation in woven cloth (0/90) composite materials. Struct Heal Monit2018; 17(2): 363–378. DOI: 10.1177/1475921717694812.
22.
KakeiAEpaarachchiJAIslamM, et al.Evaluation of delamination crack tip in woven fibre glass reinforced polymer composite using FBG sensor spectra and thermo-elastic response. Meas J Int Meas Confed2018; 122: 178–185. DOI: 10.1016/j.measurement.2018.03.023.
23.
KahandawaGCEpaarachchiJWangH, et al.Use of FBG sensors for SHM in aerospace structures. Photo Sensors2012; 2(3): 203–214. DOI: 10.1007/s13320-012-0065-4.
LambertiALuyckxGVan PaepegemW, et al.Detection, localization and quantification of impact events on a stiffened composite panel with embedded fiber bragg grating sensor networks. Sensors (Switzerland)2017; 17(4): 1–13. DOI: 10.3390/s17040743.
26.
Esmailzadeh NoghaniFTofighiSPishbinN, et al.Fast and high spatial resolution distributed optical fiber sensor. Opt Laser Technol2018; 115: 277–288. DOI: 10.1016/j.optlastec.2019.02.033.
27.
LalamNRLuPVenketeswaranA, et al.Raman-assisted BOTDA performance improvement with the differential pulse-width pair technique and an artificial neural network based fitting algorithm. In: Proceedings of the Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2020, 27 April–8 May 2020, Online, DOI: 10.1117/12.2558717.
28.
ZhuHLeiYYufengZ, et al.Optimized support vector machine assisted botda for temperature extraction with accuracy enhancement. IEEE Photon J2020; 12(1): 1–14. DOI: 10.1109/JPHOT.2019.2957410.
29.
LiWChenLBaoX. Compensation of temperature and strain coefficients due to local birefringence using optical frequency domain reflectometry. Opt Commun2013; 311: 26–32. DOI: 10.1016/j.optcom.2013.08.022.
30.
LiuYMaLYangC, et al.Raman distributed temperature sensor with high spatial and temperature resolution using optimized graded-index few-mode fiber over 25 km-long distance. Opt Infobase Conf Pap2018; Part F124(16): 20562–20571. DOI: 10.1364/ofs.2018.tue80.
31.
ShengLLigongLJinpengL, et al.Research on the simultaneous distributed measurement of temperature and strain based on Brillouin scattering effect in communication optical fiber. Physics2021; 1160702: 4, DOI: 10.1117/12.2582681.
32.
BaDLiYYanJ, et al.Phase-coded Brillouin optical correlation domain analysis with 2 mm resolution based on phase-shift keying. Opt Express2019; 27(25): 36197. DOI: 10.1364/oe.27.036197.
33.
DongYChenLBaoX. Extending the sensing range of Brillouin optical time-domain analysis combining. J Light Technol2012; 30(8): 1161–1167.
34.
MeiHHaiderMFJosephR, et al.Recent advances in piezoelectric wafer active sensors for structural health monitoring applications. Sensors (Switzerland)2019; 19(2): 383. DOI: 10.3390/s19020383.
35.
BarretoLSMachadoMRSantosJC, et al.Damage indices evaluation for one-dimensional guided wave-based structural health monitoring. Lat Am J Sol Struct2021; 18(2): 1–17. DOI: 10.1590/1679-78256292.
36.
De CastroBABaptistaFGCiampaF. A comparison of signal processing techniques for impedance-based damage characterization in carbon fibers under noisy inspections. Mater Today Proc2019; 34: 275–279. DOI: 10.1016/j.matpr.2020.03.470.
37.
NaWSBaekJ. A review of the piezoelectric electromechanical impedance based structural health monitoring technique for engineering structures. Sensors (Switzerland)2018; 18: 1307. DOI: 10.3390/s18051307.
38.
MichalcováLBělskýPPetrusováL. Composite panel structural health monitoring and failure analysis under compression using acoustic emission. J Civ Struct Heal Monit2018; 8(4): 607–615. DOI: 10.1007/s13349-018-0302-7.
39.
LiHLiJYunX, et al.Non-destructive examination of interfacial debonding using acoustic emission. Dent Mater2011; 27(10): 964–971. DOI: 10.1016/j.dental.2011.06.002.
40.
CrivelliDMarioGMarkE, et al.Localisation and identification of fatigue matrix cracking and delamination in a carbon fibre panel by acoustic emission. Compos B Eng2015; 74: 1–12. DOI: 10.1016/j.compositesb.2014.12.032.
41.
ZargarSAYuanFG. Impact diagnosis in stiffened structural panels using a deep learning approach. Struct Heal Monit2021; 20(2): 681–691. DOI: 10.1177/1475921720925044.
42.
ZhaoRYanRChenZ, et al.Deep learning and its applications to machine health monitoring. Mech Syst Signal Process2019; 115: 213–237. DOI: 10.1016/j.ymssp.2018.05.050.
43.
PathirageCSNLiJLiL, et al.Structural damage identification based on autoencoder neural networks and deep learning. Eng Struct2018; 172: 13–28. DOI: 10.1016/j.engstruct.2018.05.109.
44.
PathirageCSNLiJLiL, et al.Development and application of a deep learning–based sparse autoencoder framework for structural damage identification. Struct Heal Monit2019; 18(1): 103–122. DOI: 10.1177/1475921718800363.
45.
TabianIFuHKhodaeiZS. A convolutional neural network for impact detection and characterization of complex composite structures. Sensors (Switzerland)2019; 19(22): 1–25. DOI: 10.3390/s19224933.
46.
AbdeljaberOAvciOKiranyazS, et al.Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J Sound Vib2017; 388: 154–170. DOI: 10.1016/j.jsv.2016.10.043.
47.
de RezendeSWFde MouraJDRVNetoRMF, et al.Convolutional neural network and impedance-based SHM applied to damage detection. Eng Res Express2020; 2(3): 35031. DOI: 10.1088/2631-8695/abb568.
48.
FoxMRSchultheiszCRReederJR, et al.Materials examination of the vertical stabilizer from American airlines flight 587. Mater Sci Technol2006; 2: 171.
49.
BykowskiMHudginsADeaconRM, et al.Failure analysis of the space shuttle Columbia RCC leading edge. J Fail Anal Prev2006; 6(1): 39–45. DOI: 10.1361/154770206X86509.
50.
MAIB. MAIB report no 08/2015 - Cheeki Rafiki - very serious marine casualty, 2015.
51.
RederMDGonzalezEMeleroJJ. Wind turbine failures - tackling current problems in failure data analysis. J Phys Conf Ser2016; 753(7): 072027. DOI: 10.1088/1742-6596/753/7/072027.
52.
GabrysJSchatzJKellyC, et al. The Use of LS-DYNA in the Columbia Accident Investigation and Return to Flight Activities. 8th International LS-DYNA Users Conference, Dearborn, USA, January 1,2004, PP. 1-10.
53.
Shankar VermaAZhiyuJZhengruR, et al. A probabilistic long-term framework for site-specific erosion analysis of wind turbine blades: a case study of 31 dutch sites. Wind Energy2021; 24(11): 1315–1336, doi:10.1002/we.2634.
WernerLFabianBFerdinandK. et al.Distributed fibre-optic sensing applications at the semmering base tunnel, Austria. Smart Infrastruc Cons2019; 172: 148. DOI: 10.1680/jsmic.20.00006.
56.
Elena Saldaeva, Report on the demonstration activities of the BladeSave system, Risk Based Technology for Blade Structural Assessment, BladeSave Project Consortium, 2020
57.
DasSSahaPPatroSK. Vibration-based damage detection techniques used for health monitoring of structures: a review. J Civ Struct Heal Monit2016; 6(3): 477–507. DOI: 10.1007/s13349-016-0168-5.
58.
SuWCHuangCSHungSL, et al.Locating damaged storeys in a shear building based on its sub-structural natural frequencies. Eng Struct2012; 39: 126–138. DOI: 10.1016/j.engstruct.2012.02.002.
59.
WangS. Damage detection in offshore platform structures from limited modal data. Appl Ocean Res2013; 41: 48–56. DOI: 10.1016/j.apor.2013.02.004.
60.
DessiDCamerlengoG. Damage identification techniques via modal curvature analysis: overview and comparison. Mech Syst Signal Process2015; 5253(1): 181–205. DOI: 10.1016/j.ymssp.2014.05.031.
61.
DawariVBVesmawalaGR. Structural damage identification of beam structures using two stage method based on modal strain energy indicators and artificial neural networks. J Vibroeng1877; 18(1): 119–128.
62.
RadzieńskiMKrawczukMPalaczM. Improvement of damage detection methods based on experimental modal parameters. Mech Syst Signal Process2011; 25(6): 2169–2190. DOI: 10.1016/j.ymssp.2011.01.007.
63.
ChenLJYuL. Structural nonlinear damage identification algorithm based on time series ARMA/GARCH model. Adv Struct Eng2013; 16(9): 1597–1609. DOI: 10.1260/1369-4332.16.9.1597.
64.
MosaviAADickeyDSeracinoR, et al.Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distances. Mech Syst Signal Process2012; 26(1): 254–267. DOI: 10.1016/j.ymssp.2011.06.009.
65.
YuLLinJ-C. Cloud computing-based time series analysis for structural damage detection. J Eng Mech2017; 143(1): 1–14. DOI: 10.1061/(asce)em.1943-7889.0000982.
66.
RaoRKSasmalS. Smart nano-engineered cementitious composite sensors for vibration-based health monitoring of large structures. Sens Actuat A Phys2020; 311: 112088.
67.
SepehryNEhsaniMZhuW, et al.Application of scaled boundary finite element method for vibration-based structural health monitoring of breathing cracks. J Vib Control2020; 27: 2870–2886. DOI: 10.1177/1077546320968646.
68.
OuYChatziENDertimanisVK, et al.Vibration-based experimental damage detection of a small-scale wind turbine blade. Struct Heal Monit2017; 16(1): 79–96. DOI: 10.1177/1475921716663876.
69.
HuangTChaves-VargasMYangJ, et al.A baseline-free damage detection method based on node displacement in mode shape. In: Proceedings of the 8th European Workshop on Structural Health Monitoring, Bilbao, Spain, July, 2016, pp. 765–774.
70.
HuangTChaves-VargasMYangJ, et al.A baseline-free structural damage indicator based on node displacement of structural mode shapes. J Sound Vib2018; 433: 366–384. DOI: 10.1016/j.jsv.2018.07.001.
71.
GlisicBYaoYTungSTE, et al.Strain sensing sheets for structural health monitoring based on large-area electronics and integrated circuits. Proc IEEE2016; 104(8): 1513–1528. DOI: 10.1109/JPROC.2016.2573238.
72.
HerszbergIBannisterMKLiHCH, et al.Structural health monitoring for advanced composite structures. ICCM Int Conf Compos Mater2007; 8: 1–15.
73.
GiurgiutiuV. Structural health monitoring (SHM) of aerospace composites. Amsterdam, The Netherlands: Elsevier, 2019.
74.
KaurANagarajanSAnandanS, et al.Embeddable fiber optic strain sensor for structural monitoring. Bellingham, DC: SPIE, 2013, p. 2021. DOI: 10.1117/12.2009462.
75.
XuCKhodaeiZS. A novel fabry-pérot optical sensor for guided wave signal acquisition. Sensors (Basel)2020; 20(6): 1728. DOI: 10.3390/s20061728.
76.
BlazejewskiWCzulakAMaterialsNE, et al.SMART composite high pressure vessels with integrated optical fiber sensors SMART composite high pressure vessels with integrated optical fiber sensors. In: Proceedings of SPIE -The International Society for Optical Engineering, San Diego, California, United States, 31 March 2010, DOI: 10.1117/12.847251.
77.
GuoHXiaoGMradN, et al.Fiber optic sensors for structural health monitoring of air platforms. Sensors2011; 11(4): 3687–3705. DOI: 10.3390/s110403687.
78.
RamakrishnanMRajanGSemenovaY, et al.Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials. Sensors (Switzerland)2016; 16(1): 99. DOI: 10.3390/s16010099.
79.
NicolasMJSullivanRWRichardsWL. Large scale applications using FBG sensors: determination of in-flight loads and shape of a composite aircraft wing. Aerospace2016; 3(3): 18. DOI: 10.3390/aerospace3030018.
80.
ZhuQXuCYangG. Experimental research on damage detecting in composite materials with FBG sensors under low frequency cycling. Int J Fatigue2017; 101: 61–66. DOI: 10.1016/j.ijfatigue.2017.03.034.
81.
ZengHYanRXuL, et al.Application study on fiber bragg grating sensors in damage monitoring of sandwich composite joints. J Sandw Struct Mater2020; 22(5): 1542–1563. DOI: 10.1177/1099636218789621.
82.
KumarSBediAKothariV. Design and analysis of FBG based sensor for detection of damage in oil and gas pipelines for safety of marine life. Proceed SPIE2018: 31: 104880X6. DOI: 10.1117/12.2285923.
83.
LoayssaA. (2020) Structural Health Monitoring Using Distributed Fibre-Optic Sensors. In Optical Fibre Sensors (eds I. Villar and I.R. Matias). https://doi.org/10.1002/9781119534730.ch5
84.
ShanYXuHZhouZ, et al.State sensing of composite structures with complex curved surface based on distributed optical fiber sensor. J Intell Mater Syst Struct2019; 30(13): 1951–1968. DOI: 10.1177/1045389X19849287.
85.
DongYZhangHChenL, et al.2 cm spatial-resolution and 2 km range Brillouin optical fiber sensor using a transient differential pulse pair. Appl Opt2012; 51(9): 1229–1235. DOI: 10.1364/AO.51.001229.
86.
BarriasACasasJRVillalbaS. A review of distributed optical fiber sensors for civil engineering applications. Sensors (Switzerland)2016; 16(5): 748. DOI: 10.3390/s16050748.
87.
MitraMGopalakrishnanS. Guided wave based structural health monitoring: a review. Smart Mater Struct2016; 25(5): 053001. DOI: 10.1088/0964-1726/25/5/053001.
88.
CawleyP. Structural health monitoring: closing the gap between research and industrial deployment. Struct Heal Monit2018; 17(5): 1225–1244. DOI: 10.1177/1475921717750047.
89.
MemmoloVMonacoEBoffaND, et al.Guided wave propagation and scattering for structural health monitoring of stiffened composites. Compos Struct2017; 184: 568–580. DOI: 10.1016/j.compstruct.2017.09.067.
90.
SpadaACapriottiMLanza di ScaleaF. Global-local model for guided wave scattering problems with application to defect characterization in built-up composite structures. Int J Sol Struct2020; 182–183: 267–280. DOI: 10.1016/j.ijsolstr.2019.08.015.
91.
ZhangJLiuKGaoC, et al.Multi-mode guided waves based reference-free damage diagnostic imaging in plates. SDHM Struct Durab Heal Monit2019; 13(1): 41–59. DOI: 10.32604/sdhm.2019.05142.
92.
GaoDMaYWuZ, et al.Guided wave based damage detection method for aircraft composite structures under varying temperatures. Struct Durab Heal Monit2021; 15(1): 23–37. DOI: 10.32604/sdhm.2021.013737.
93.
LiJLuYLeeYF. Debonding detection in CFRP-reinforced steel structures using anti-symmetrical guided waves. Compos Struct2020; 253: 112813. DOI: 10.1016/j.compstruct.2020.112813.
94.
De LucaAPerfettoDPetroneG, et al.Guided-waves in a low velocity impacted composite winglet. Key Eng Mater2018; 774: 343–348. DOI: 10.4028/www.scientific.net/KEM.774.343.
95.
YueNKhodaeiZSAliabadiMH. Damage detectability model of pitch-catch configuration in composite plates. Key Eng Mater2017; 754: 387–390. DOI: 10.4028/www.scientific.net/KEM.754.387.
96.
MemmoloVBoffaNDMaioL, et al.Damage localization in composite structures using a guided waves based multi-parameter approach. Aerospace2018; 5: 4. DOI: 10.3390/aerospace5040111.
97.
de OliveiraMAAraujoNVSda SilvaRN, et al.Use of Savitzky-Golay filter for performances improvement of SHM systems based on neural networks and distributed PZT sensors. Sensors (Switzerland)2018; 18(1): 152. DOI: 10.3390/s18010152.
98.
MalinowskiPHWandowskiTSinghSK. Employing principal component analysis for assessment of damage in GFRP composites using electromechanical impedance. Compos Struct2021; 266: 113820. DOI: 10.1016/j.compstruct.2021.113820.
TangZSLimYYSmithST, et al.Modelling of the electromechanical impedance technique for prediction of elastic modulus of structural adhesives. Struct Heal Monit2020; 20(5): 2245–2260.DOI: 10.1177/1475921720916924.
101.
WangDWangQWangH, et al.Experimental study on damage detection in timber specimens based on an electromechanical impedance technique and RMSD-based mahalanobis distance. Sensors (Switzerland)2016; 16: 1765. DOI: 10.3390/s16101765.
102.
WandowskiTOpokaSMalinowskiPH, et al. The performance of three Electromechanical Impedance damage indicators on structural element with bolted joints. e-Journal of Nondestructive Testing2015; 1.
103.
PearsonMREatonMFeatherstonC, et al.Improved acoustic emission source location during fatigue and impact events in metallic and composite structures. Struct Heal Monit2017; 16(4): 382–399. DOI: 10.1177/1475921716672206.
104.
ZhouZRiyanLYichaoR, et al.A new acoustic emission source location method using tri-variate kernel density estimator. IEEE Access2019; 7: 158379–158388. DOI: 10.1109/ACCESS.2019.2950225.
105.
GodinNReynaudPR’MiliM, et al.Identification of a critical time with acoustic emission monitoring during static fatigue tests on ceramic matrix composites: towards lifetime prediction. Appl Sci2016; 6(2): 43. DOI: 10.3390/app6020043.
106.
AlaimoABarraccoAMilazzoA, et al.Artificial neural networks comparison for a SHM procedure applied to composite structure. In Proceedings of the Italian Association of Aeronautics and Astronautics XXII Conference, Naples, Italy, September 2012, pp. 9–12. DOI: 10.13140/2.1.1538.4006.
107.
SonySDunphyKSadhuA, et al.A systematic review of convolutional neural network-based structural condition assessment techniques. Eng Struct2020; 226: 111347. DOI: 10.1016/j.engstruct.2020.111347.
108.
BilalDKUnelMYildizM, et al.Realtime localization and estimation of loads on aircraft wings from depth images. Sensors (Switzerland)2020; 20(12): 1–24. DOI: 10.3390/s20123405.
109.
RautelaMMonacoEGopalakrishnanS. Delamination detection in aerospace composite panels using convolutional autoencoders. In Proceedings of the Health Monitoring of Structural and Biological Systems XV, Bellingham, DC, March 2021. DOI: 10.1117/12.2582993.
110.
SeventekidisPGiagopoulosD. A combined finite element and hierarchical deep learning approach for structural health monitoring: test on a pin-joint composite truss structure. Mech Syst Signal Process2021; 157: 107735. DOI: 10.1016/j.ymssp.2021.107735.
111.
CuiRAzuaraGLanza di ScaleaF, et al.Damage imaging in skin-stringer composite aircraft panel by ultrasonic-guided waves using deep learning with convolutional neural network. Struct Heal Monit2021; 21(3): 1–16. DOI: 10.1177/14759217211023934.
