Abstract
Various structural health monitoring techniques have been developed over the years. Due to the lack of a common platform to test the efficiency of these methods, the damage analysis models have been tested on different structures selected according to the choice of researches. Therefore, perfect comparison among the models was not possible. In light of this event, a benchmark structure was developed providing a common ground to analyse the effectiveness of the damage detection strategies. This structural damage analysis consists of different damage patterns, major damages and minor damages. The damage detection algorithms were tested for the detection of these different damage patterns and the effectiveness against noise contamination. Also the amount of data required for the algorithms to effectively detect damage was also recorded, which indicated the efficiency of the method applied. The paper deals with the application of different damage detection techniques on the ASCE benchmark Phase-I and Phase-II structure and studies their efficiency against the other structures. A brief comparison has been made among different damage detection models such as Bayesian model, neural network, autoregressive models, and model update. These methods have been successfully implemented on the benchmark structure and their efficiencies have been measured in terms of noise contamination, the amount of data required to measure the damage and the detection of damage (major and minor). Out of all the techniques discussed, model update technique, wavelet approach, autoregressive technique, convolution neural network and synchrosqueezed wavelet transform have proved to a robust damage analysing tool.
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References
Rytter T (1993) Vibration based inspection of civil engineering structure. Department of building technology and structure engineering. Aalborg University, Denmark
Zhu F, Wu Y (2014) A rapid structural damage detection method using integrated ANFIS and interval modeling technique. Appl Soft Comput 25:473–484
Reddy RRK, Ganguli R (2003) Structural damage detection in a helicopter rotor blade using radial basis function neural networks. Smart Mater Struct 12:232–241
Das S, Saha P, Parto SK (2016) Vibration based damage detction techniques used for health monitoring of structrues: a review. J Civil Struct Health Monit 6(3):477–507
Shi A, Yu XH (2012) Structural damage detection using artificial neural networks and wavelet transform. 2012 IEEE international conference on computational intelligence for measurement system and applications. IEEE, Piscataway
Katebi L, Tehranizadeh M, Mohammadgholibeyki N (2018) A generalized flexibility matrix-based model updating method for damage detection of plane truss and frame structures. J Civil Struct Health Monit 8(2):301–314
Neves AC, Gonza´lez I, Leander J, Karoumi R (2017) Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. J Civil Struct Health Monit 7(5):689–702
Mahato S, Teja MV, Chakraborty A (2017) Combined wavelet–Hilbert transform-based modal identification of road bridge using vehicular excitation. J Civil Struct Health Monit 7(1):29–44
Kodikara KATL, Chan THT, Nguyen T, Thambiratnam DP (2016) Model updating of real structures with ambient vibration data. J Civil Struct Health Monit 6(3):329–341
Ghrib F, Li L (2017) An adaptive filtering-based solution for the Bayesian modal identification formulation. J Civil Struct Health Monit 7:1–13
Johnson EA, Lam HF, Ktafygiotis LS, Beck JL (2004) Phase I IASC–ASCE structural health monitoring benchmark problem using simulated data. J Eng Mech 130(1):3–15
IASC–ASCE SHM Task Group (1999)
Bernal D, Dyke SJ, Lam HF, Beck JL (2002) Phase II of the ASCE benchmark study on SHM. In: Proceedings of 15th engineering mechanics conference. ASCE, Reston
Caicedo JM, Dyke SJ, Johnson EA (2004) Natural excitation technique and eigensystem realization algorithm for Phase I of the IASC–ASCE benchmark problem: simulated data. J Eng Mech 130(1):49–60
Bergland GD (ed) (2000) Random data: analysis and measurement procedures, 4th edn. Wiley, New York
James GH, Carne TG, Lauffer JP (1993) The natural excitation technique for modal parameter extraction from operating wind turbines. Sandia National Laboratories, Sandia
James GH, Carne TG, Mayes RL (1996) Modal parameter extraction from large operating structures using ambient excitation. In: 14th international modal analysis conference, Dearborn, Michigan, pp 77–83
Farrar CR, James GH (1997) System identification from ambient vibration measurements on a bridge. J Sound Vib 205(1):1–18
Farrar CR, Jauregui DA (1998) Comparative study of damage identification algorithms applied to a bridge II: numerical study. Smart Mater Struct 7:721–731
Juang JN, Pappa RS (1985) An eigensystem realization algorithm for modal parameter identification and model reduction. J Guid Control Dyn 8:620–627
Luş H, Betti R, Yu J, De Angelis M (2004) Investigation of a system identification methodology in the context of the ASCE benchmark problem. J Eng Mech 130(1):71–84
Luş H, Betti R, Longman RW (1999) Identification of linear structural systems using earthquake-induced vibration data. Earthq Eng Struct Dyn 28(11):1449–1467
Juang J-N, Phan M, Horta LG, Longman RW (1993) Identification of Observer/Kalman filter Markov parameters: theory and experiments. J Guid Control Dyn 16(2):320–329
Luş H (2001) Control theory based system identification. Columbia University, New York
Luş H, Betti R, Longman RW (2000) Obtaining refined first order predictive models of linear structural systems. Earthq Eng Struct Dyn 31:1413–1440
De Angelis M, Luş H, Betti R, Longman RW (2002) Extracting physical parameters of mechanical models from identified state space representations. ASME J Appl Mech 69(9):617–625
Perez-Ramirez CA, Amezquita-Sanchez JP, Adeli H, Valtierra-Rodriguez M, Camarena-Martinez D, Romero-Troncoso RJ (2016) New methodology for modal parameters identification of smart civil structures using ambient vibrations and synchrosqueezed wavelet transform. Eng Appl Artif Intell 48:1–12
Stubbs N, Kim JT, Tapole K (1992) An efficient and robust algorithm for damage localization in offshore platforms. In: ASCE 10th structures congress. Reston, VA. https://doi.org/10.2307/3716590
Zimmerman DC, Kauk M (1994) Structural damage detection using a minimum rank update theory. J Sound Vib 116(2):221–231
Pandey AK, Biswas M (1991) Damage detection in structures using changes in flexibility`. J Sound Vib 169(1):3–17
Zhang Z, Aktan HM (1995) The damage indices for constructed facilities. In: Proceedings of SPIE - The international society for optical engineering, vol 2460. pp 1520–1529
Farrar CR, Jauregui DA (1998) Comparative study of damage identification algorithms applied to a bridge: 1. experiment. Smart Mater Struct 7:704–719
Barroso LR, Rodriques R (2004) Damage detection utilizing the damage index method to a benchmark structure. J Eng Mech 130(2):142–151
Brinker R, Zhang L, Andersen P (2000) Modal identification from ambient responses using frequency domain decomposition. In: 18th international modal analysis conference, San Antonio, Texas, USA, pp 625–630
Bernal D, Gunes B (2004) Flexibility based approach for damage characterization: benchmark application. J Eng Mech 130(1):61–70
Van Overschee P, Moor BLR (eds) (1996) Subspace identification for linear systems: theory, implementation, applications. Springer: Kluwer Academic, Boston
Bernal D (2002) Load vectors for damage localization. J Eng Mech 128(1):7–14
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodological) 39(1):1–38
McLachlan GJ, Krishnam T (2008) The EM algorithm and extensions, 2nd edn. Wiley, New York
Cara FJ, Carpio J, Jaun J, Alarcon E (2012) An approach to operational modal analysis using the expectation maximization algorithm. Mech Syst Signal Process 31:109–129
Hou Z, Noori M (1999) Application of wavelet analysis for structural health monitoring. 2nd international workshop on structural health monitoring. Stanford University, Stanford
Hou Z, Noori M, Amand RS (2000) Wavelet-based approach for structural damage detection. J Eng Mech 126(7):677–683
Hera A, Hou Z (2004) Application of wavelet approach for ASCE structural health monitoring benchmark studies. J Eng Mech 130(1):96–104
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Ting CC, Liu HH (1971) The empirical mode decomposition and hilbert spectrum for nonlinear and nonstationary time series analysis. R Soc Lond Ser A 1998(454):903–995
Huang NE, Shen Z, Long SR (1999) A new view of nonlinear water waves: the Hilbert spectrum. Annu Rev Fluid Mech 31:417–457
Corbin M, Hera A, Hou Z (2000) Locating damages using wavelet approach. In: 14th engineering mechanics conference (EM2000), Austin, Tex
Chui CK (1992) Introduction to wavelets. Academic Press, San Diego
Daubechies I (1992) Ten lectures on wavelets. In: CBS-NSF regional conference in applied mathematics
Sone A, Yamamoto S, Masuda A, Nakaoka A (1996) Health monitoring system of building by using wavelet analysis. In: Eleventh world conference on earthquake engineering. ISBN: 0060426223
Yang JN, Lei Y, Lin S, Huang N (2004) Hilbert–Huang based approach for structural damage detection. J Eng Mech 130(1):85–95
Yang JN, Lei Y, Huang NE (2001) Damage identification of civil engineering structures using Hilbert–Huang transform. In: 3rd international workshop on structural health monitoring, Stanford, CA
Yang JN, Lin S, Pan S (2002) Damage detection of a health monitoring benchmark building using Hilbert–Huang spectral analysis. Adv Build Technol 2:1017–1024
Lin S, Yang JN, Zhou L (2005) Damage identification of a benchmark building for structural health monitoring. Smart Mater Struct 14:S162–S169
Ibrahim SR (1977) Random decretment technique for modal identification of structures. J Spacecr Rockets (AIAA) 14(11):696–700
Huan SL, Mcinnis BC, Denman ED (1983) Analysis of the random decrement method. Int J Syst Sci 14(4):417–423
Chase JG, Begoc V, Barroso LR (2005) Efficient structural health monitoring for a benchmark structure using adaptive RLS filters. Comput Struct 83:639–647
Haykin S (1991) Adaptive filter theory, 2nd edn. Prentice-Hall, Englewood Cliffs
Ifeachor EC, Jervis BW (2002) Digital signal processing, a practical approach. Prentice Hall, Englewood Cliffs
Sohn H, Farrar CR (2001) Damage diagnosis using time series analysis of vibration signals. Smart Mater Struct 10(3):446–451
da Silva S, Junior MD, Junior VL (2007) Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition. J Braz Soc Mech Sci Eng XXIX(2):174–184
Bezdek J, Pal SK (1992) Fuzzy models for pattern recognition. IEEE Press, Piscataway
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. Los Alamos National Laboratory, Los Alamos (NM 87545)
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 291:349–368
Worden K, Manson G, Fieller NRJ (2000) Damage detection using outlier analysis. J Sound Vib 229(3):647–667
Fugate ML, Sohn H, Farrar CR (2000) Unsupervised learning methods for vibration-based damage detection. In: 18th international modal analysis conference, San Antonio, Texas, USA
Carden EP, Brownjohn JMW (2008) ARMA modelled time-series classification for structural health monitoring of civil infrastructure. Mech Syst Signal Process 22:295–314
Maia NMM, Silva JMM (1998) Theoretical and experimental modal analysis. Research Studies Press, Hertfordshire
Lynch JP (2004) Linear classification of system poles for structural damage detection using piezoelectric active sensors. In: Proceedings of SPIE - The international society for optical engineering, vol 5391, pp 9–20
Ljung L (ed) (1999) System identification: theory for the User. 2. Prentice-Hall, Englewood Cliffs
Carden EP, Brownjohn JMW (2007) ARMA modelled time series classification for structural health monitoring. In: 2007 IMAC-XXV: Conference and exposition on structural dynamics
Nair KK, Kiremidjian AS (2007) Time series based structural damage detection algorithm using gaussian mixtures modeling. J Dyn Syst Meas Control 129:285–293
Hastie T, Tibshirani R, Freidman J (2001) Elements of statistical learning: data mining, inference and prediction. Springer Verlag, New York
Mardia KV, Kent JT, Bibby JM (2003) Multivariate analysis. Academic, London
Bernal D, Beck J (2004) Special section: Phase I of the IASC-ASCE structural health monitoring benchmark. J Eng Mech 130(1):1–2
Yang JN, Lin S, Huang H, Zhou L (2006) An adaptive extended Kalman filter for structural damage identification. Struct Control Health Monit 13:849–867
Yang JN, Huang H, Lin S (2006) Sequential non-linear least-square estimation for damage identification of structures. Int J Non-Linear Mech 41:124–140
Jazwinshi AH (1970) Stochastic processes and filtering theory. Academic Press, New York
Hoshiya M, Saito E (1984) Structural identification by extended Kalman filter. J Eng Mech 110:1757–1771
Sato T, Takei K (1998) Development of a Kalman filter with fading memory. In: Structural safety and reliability, ICOSSAR, vol 1998. pp 387–394
Maruyama O, Hoshiya M (2001) System identification of an experimental model by extended Kalman filter. In: Structural safety and reliability, ICOSSAR 2001
Wang D, Haldar A (1997) System identification with limited observations and without input. J Eng Mech 123:504–511
Al-Hussein A, Haldar A (2015) Structural health assessment at a local level using minimum information. Eng Struct 88:100–110
Yang JN, Pan S, Huang H (2007) An adaptive extended Kalman filter for structural damage identifications II: unknown inputs. Struct Control Health Monit 14:497–521
Lei Y, Jiang Y, Xu Z (2012) Structural damage detection with limited input and output measurement signals. Mech Syst Signal Process 28:229–243
Pan S, Xiao D, Xing S, Law SS, Du P, Li Y (2016) A general extended Kalman filter for simultaneous estimation of system and unknown inputs. Eng Struct 109:85–98
Yang JN, Huang H (2007) Sequential non-linear least-square estimation for damage identification of structures with unknown inputs and unknown outputs. Non-Linear Mech 42:789–801
Huang H (2006) System identification and damage detection of structures, Irvine, CA
Bendat JS, Piersol AG (1993) Engineering applications of correlation and spectral analysis, 2nd edn. John Wiley & Sons, New York
Brownjohn JMW (2003) Ambient vibration studies for system identification of tall buildings. Earthq Eng Struct Dyn 32:71–95
Andersen P, Brincker R, Kirkegaard PH (1996) Theory of covariance equivalent ARMAV models of civil engineering structures. In: Proceedings of XIV international modal analysis conference. Dearborn, MI
Lam HF, Katafygiotis LS, Mickleborough NC (2004) Application of a statistical model updating approach on Phase I of the IASC–ASCE structural health monitoring benchmark study. J Eng Mech 130(1):34–48
Friswelli MI, Mottershead JE (1995) Finite element model updating in structural dynamics, 2nd edn. Kluwer Academic Publishers, Netherlands
Ching J, Muto M, Beck JL (2006) Structural model updating and health monitoring with incomplete modal data using Gibbs. Comput-Aided Civil Infrastruct Eng 21:242–257
Yuen KV, Au SK, Beck JL (2004) Two-stage structural health monitoring approach for Phase I benchmark studies. J Eng Mech 130(1):16–33
Beck JL (1978) Determining models of structures from earthquake records. California Institute of Technology, Earthquake Engineering Research Laboratory, Pasadena
Beck JL (1996) System identification methods applied to measured seismic response. In: 11th world conference on earthquake engineering. Elsevier, New York
Werner SD, Beck JL, Levine MB (1987) Seismic response evaluation of Meloland road overpass using 1979 Imperial Valley earthquake records. Earthq Eng Struct Dyn 15:249–274
Beck JL, Au S-K, Vanik MW (2001) Monitoring structural health using a probabilistic measure. Comput-Aided Civil Infrastruct Eng 16:1–11
Vanik MW, Beck JL, Au SK (2000) Bayesian probabilistic approach to structural health monitoring. J Eng Mech 126(7):738–745
Beck JL, Katafygiotis LS (1998) Updating models and their uncertainties. I: Bayesian statistical framework. J Eng Mech 124(4):455–461
Ching J, Beck JL (2004) Bayesian analysis of the phase II IASC–ASCE structural health monitoring experimental benchmark data. J Eng Mech 130(10):1233–1244
Beck JL, May BS, Polidori DC (1994) Determination of modal parameters from ambient vibration data for structural health monitoring. In: 1st world conference on structural control. Pasadena, California. Department of Civil Engineering, University of Southern California, Los Angeles
Grande E, Imbimbo M (2012) A data-driven approach for damage detection: an application to the ASCE steel benchmark structure. J Civil Struct Health Monit 2:73–85
Ahmed AE, Mahmoud RH, Marzouk H (2010) Damage detection in offshore structures using neural networks. Mar Struct 23(1):131–145
Jiang SF, Zhang CM, Zhang S (2011) Two-stage structural damage detection using fuzzy neural networks and data fusion techniques. Expert Syst Appl 38(1):511–519
Bischop MB (1996) Neural networks for pattern recognition. Oxford University Press, New York
Lam HF, Yuen KV, Beck JL (2006) Structural health monitoring via measured ritz vectors utilizing artificial neural networks. Comput-Aided Civil Infrastruct Eng 21(4):232–241
Yuen KV, Lam HF (2006) On the complexity of artificial neural networks for smart structures monitoring. Eng Struct 28(7):977–984
Vakil-Baghmisheh MT, Peimani M, Sadeghi MH, Ettefagh MM (2008) Crack detection in beam-like structures using genetic algorithms. Appl Soft Comput 8:1150–1160
Pawar PM (2005) Matrix crack detection in thin-walled composite beam usinggenetic fuzzy system. J Intell Mater Syst Struct 16:395–409
Beena P, Ganguli R (2011) Structural damage detection using fuzzy cognitive mapsand Hebbian learning. Appl Soft Comput 11:1014–1020
Zhu F, Deng Z, Zhang J (2013) An integrated approach for structural damage identification using wavelet neuro-fuzzy model. Expert Syst Appl 40:7415–7427
Shim MB, Suh MW (2003) Crack identification of a planar frame structure based on a synthetic artificial intelligence technique. Int J Numer Methods Eng 57:57–82
Lew JS, Horta IG (2007) Uncertainty quantification using interval modeling withperformance sensitivity. J Sound Vib 308:330–336
Arsava KS, Kim Y, El-Korchi T, Park HS (2013) Nonlinear system identification ofsmart structures under high impact loads. Smart Mater Struct 22:55008
Mitchell R, Kim Y, El-Korchi T (2012) System identification of smart structures usinga wavelet neuro-fuzzy model. Smart Mater Struct 21:115009
Red-Horse JR, Paez TI (2008) Sandia National Laboratories Validation Workshop: structural dynamics application. Comput Methods Appl Mech Eng 197:2578–2584
Lew JS, Loh CH (2012) Real-time aircraft structural damage identification with flight condition variations. In: SPIE 8347, nondestructive characterization for composite materials, aerospace engineering, civil infrastructure and homeland security 2012, San Diego, California
Saeed RA, Galybin AN, Popov V (2013) 3D fluid-structure modelling and vibrationanalysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS. Mech Syst Signal Process 34:259–276
Hosseinzadeh AZ, Amiri GG, Koo KY (2016) Optimization-based method for structural damage localization and quantification by means of static displacements computed by flexibility matrix. Eng Optim 48(4):543–561
Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11(8):5508–5518
Holland J (1975) Adaptation in natural and artificial systems. MIT Press, Cambridge
Dyke SJ, Bernal D, Beck JL, Ventura C (2001) An experimental benchmark problem in structural health monitoring. In: Third international workshop on structural health monitoring, Stanford, CA, CRC Press, Boca Raton, FL, pp 488–497. ISBN: 1566768810
Mikami S, Beskhyroun S, Miyamori Y, Oshima T (2007) Application of a vibration-based damage detection algorithm on a benchmark structure. In: The 3rd international conference on structural health monitoring and intelligent infrastructure, pp U205–U212
Sohn H, Law KH (1997) A Bayesian probabilistic approach for structure damage detection. Earthq Eng Struct Dyn 26(12):1259–1281
Sohn H, Law KH (2000) Bayesian probabilistic damage detection of a reinforced-concrete bridge column. Earthq Eng Struct Dyn 29(8):1131–1152
Beck JL, Yuen KV (2004) Model selection using response measurements: Bayesian probabilistic approach. J Eng Mech 130(2):192–203
Jiang X, Mahadevan S (2008) Bayesian probabilistic inference for nonparametric damage detection of structures. J Eng Mech 134:820–831
Adeli H, Jiang X (2006) Dynamic fuzzy wavelet neural network model for structural system identification. J Struct Eng 132(1):102–111
Taha MMR (2010) A neural-wavelet technique for damage identification in the ASCE benchmark structure using Phase II experimental data. Adv Civil Eng 2010:675927
Jiang X, Mahadevan S, Yuan Y (2017) Fuzzy stochastic neural network model for structural system identification. Mech Syst Signal Process 82:394–411
Huang Y, Beck JL, Li H (2017) Hierarchical sparse Bayesian learning for structural damage detection: theory, computation and application. Struct Saf 64:37–53
Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244
Ji S, Xue Y, Carin L (2008) Bayesian compressive sensing. IEEE Trans Signal Process 56(6):2346–2356
Huang H, Beck JL (2015) Hierarchical sparse Bayesian learning for structural health monitoring with incomplete modal data. Int J Uncertain Quantif 5(2):139–169
Dyke SJ, Bernal D, Beck JL, Ventura C (2003) Experimental phase II of the structural health monitoring benchmark problem. In: 16th engineering mechanics conference. ASCE, Reston
Yang X, Mi Y (2012) Damage identification of benchmark structure using ANN. Appl Mech Mater 152–154:796–801
Li ZX, Tang XM (2008) Damage identification for beams using ANN based on statistical property of structural responses. Comput Struct 86(1–2):64–71
Katafygiotis LS, Beck JL (1998) Updating models and their uncertainties. Part II: model identifiability. J Eng Mech 124(4):463–467
Beck JL, Au SK (2002) Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation. J Eng Mech 128:380–391
Sun Z, Chang CC (2003) Structural degradation monitoring using covariance-driven wavelet packet signature. Struct Health Monit 2(4):309–325
Zabrodsky H, Peleg S, Avnir D (1992) Continuous symmetry measures. J Am Chem Soc 114:7843–7851
Chen JG, Büyüköztürk O (2017) A symmetry measure for damage detection with mode shapes. J Sound Vib 408:123–137
Chen B, Zang C (2011) A hybrid immune model for unsupervised structural damage pattern recognition. Expert Syst Appl 38:1650–1658
Lynch JP (2005) Damage characterization of the IASC–ASCE structural health monitoring benchmark structure by transfer function pole migration. In: 2005 ASCE structures congress, New York
Wu JR, Li QS (2006) Structural parameter identification and damage detection for a steel structure using a two-stage finite element model updating method. J Constr Steel Res 62:231–239
Cox RT (1961) The algebra of probable inference. Johns Hopkins University Press, Baltimore
Shumway RH, Stoffer DS (1982) An approach to time series smoothing and forecasting using the EM algorithm. J Time Ser Anal 4:253–263
Papadimitriou C, Beck JL, Katafygiotis LS (1997) Asymptotic expansions for reliabilities and moments of uncertain dynamic systems. J Eng Mech 123(12):1219–1229
de Lautour OR, Omenzetter P (2010) Damage classification and estimation in experimental structures using time series analysis and pattern recognition. Mech Syst Signal Process 24:1556–1569
Altunok E, Taha MMR, Epp DS, Mayes RL, Baca TJ (2006) Damage pattern recognition for structural health monitoring using fuzzy similarity prescription. Comput-Aided Civil Infrastruct Eng 21(8):549–560
Taha MMR, Noureldin A, Osman A, El-Sheimy N (2004) Introduction to the use of wavelet multiresolution analysis for intelligent structural health monitoring. Can J Civ Eng 31(5):719–731
Abdeljaber O, Avci O, Kiranyaz MS, Boashah B, Sodano H, Inman DJ (2017) 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing 275:1308–1317
Abdeljaber O, Avci O, Kiranyaz S, Gabbouj M, Inman DJ (2017) Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J Sound Vib 388:154–170
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Das, S., Saha, P. Structural health monitoring techniques implemented on IASC–ASCE benchmark problem: a review. J Civil Struct Health Monit 8, 689–718 (2018). https://doi.org/10.1007/s13349-018-0292-5
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DOI: https://doi.org/10.1007/s13349-018-0292-5