A structural damage detection algorithm based on discrete wavelet transform and ensemble pattern recognition models

Journal of Civil Structural Health Monitoring - Tập 12 - Trang 323-338 - 2022
Milad Fallahian1, Ehsan Ahmadi2, Faramarz Khoshnoudian1
1Faculty of Civil Engineering, Amirkabir University of Technology, Tehran, Iran
2Faculty of Engineering and the Built Environment, Birmingham City University, Birmingham, UK

Tóm tắt

Damage detection is of great importance in reducing maintenance cost and preventing collapse of structures. Despite existing damage detection methods, the current literature lacks a comprehensive method, which: (i) is applicable to complex structures with large degrees of freedom, (ii) captures even low-level damages, and (iii) gives reasonable accuracy in the presence of uncertainty conditions such as noise and temperature. Hence, this study proposes a damage detection algorithm based on discrete wavelet transform and an ensemble of pattern recognition models, in which: (1) vibration data is decomposed through discrete wavelet transforms, (2) the decomposed data is compressed using principal component analysis, (3) individual damage models of the structure are trained through pattern recognition models of deep neural network and couple sparse coding, where the compressed decomposed vibration data as well as damage data are inputted, and (4) ultimately, the individual damage models are merged into one by majority voting to predict damage location and severity of the structure. The proposed algorithm is tested on a numerical model of a one-bay three-story steel frame, and experimental data of a large-scale bridge structure. It is found that the algorithm can precisely detect low-level damages at multiple locations, even in beam–column connections and complex structures, in the presence of uncertainty conditions such as noise and temperature.

Tài liệu tham khảo

Khoshnoudian F, Esfandiari A (2011) Structural damage diagnosis using modal data. Sci Iran 18:853–860. https://doi.org/10.1016/j.scient.2011.07.012 Hou R, Beck JL, Zhou X, Xia Y (2021) Structural damage detection of space frame structures with semi-rigid connections. Eng Struct 235:112029. https://doi.org/10.1016/J.ENGSTRUCT.2021.112029 Pereira S, Magalhães F, Gomes JP et al (2021) Vibration-based damage detection of a concrete arch dam. Eng Struct 235:112032. https://doi.org/10.1016/J.ENGSTRUCT.2021.112032 Farrar CR, Jauregui DA (1998) Comparative study of damage identification algorithms applied to a bridge: II. Numerical study. Smart Mater Struct 7:720–731. https://doi.org/10.1088/0964-1726/7/5/013 Zhang D, Bao Y, Li H, Ou J (2012) Investigation of temperature effects on modal parameters of the China National Aquatics Center. Adv Struct Eng 15:1139–1153. https://doi.org/10.1260/1369-4332.15.7.1139 Li H, Li S, Ou J, Li H (2009) Modal identification of bridges under varying environmental conditions: temperature and wind effects. Struct Control Health Monit. https://doi.org/10.1002/stc.319 Salawu OS (1997) Detection of structural damage through changes in frequency: a review. Eng Struct 19:718–723. https://doi.org/10.1016/S0141-0296(96)00149-6 Mehrjoo M, Khaji N, Moharrami H, Bahreininejad A (2008) Damage detection of truss bridge joints using artificial neural networks. Expert Syst Appl 35:1122–1131. https://doi.org/10.1016/j.eswa.2007.08.008 Pandey AK, Biswas M, Samman MM (1991) Damage detection from changes in curvature mode shapes. J Sound Vib 145:321–332. https://doi.org/10.1016/0022-460X(91)90595-B Shadan F, Khoshnoudian F, Inman DJ, Esfandiari A (2016) Experimental validation of a FRF-based model updating method. J Vib Control. https://doi.org/10.1177/1077546316664675 Khoshnoudian F, Talaei S, Fallahian M (2017) Structural damage detection using FRF data, 2D-PCA, artificial neural networks and imperialist competitive algorithm simultaneously. Int J Struct Stab Dyn. https://doi.org/10.1142/S0219455417500730 Zang C, Imregun M (2001) Structural damage detection using artificial neural networks and measured Frf data reduced via principal component projection. J Sound Vib 242:813–827. https://doi.org/10.1006/jsvi.2000.3390 Shadan F, Khoshnoudian F, Esfandiari A (2016) A frequency response-based structural damage identification using model updating method. Struct Control Health Monit 23:286–302. https://doi.org/10.1002/stc.1768 Mousavi AA, Zhang C, Masri SF, Gholipour G (2021) Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study. Struct Health Monit. https://doi.org/10.1177/14759217211013535 Mousavi AA, Zhang C, Masri SF, Gholipour G (2021) Damage detection and characterization of a scaled model steel truss bridge using combined complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification approach. Struct Health Monit. https://doi.org/10.1177/14759217211045901 Bayissa WL, Haritos N, Thelandersson S (2008) Vibration-based structural damage identification using wavelet transform. Mech Syst Signal Process 22:1194–1215. https://doi.org/10.1016/J.YMSSP.2007.11.001 Cocconcelli M, Zimroz R, Rubini R, Bartelmus W (2012) STFT based approach for ball bearing fault detection in a varying speed motor. Condition monitoring of machinery in non-stationary operations. Springer, Berlin, Heidelberg, pp 41–50 Zhang Y, Guo Z, Wang W et al (2003) A comparison of the wavelet and short-time Fourier transforms for Doppler spectral analysis. Med Eng Phys 25:547–557. https://doi.org/10.1016/S1350-4533(03)00052-3 Guo Z, Durand L-G, Allard L et al (1993) Cardiac Doppler blood-flow signal analysis. Med Biol Eng Comput 31:242–248. https://doi.org/10.1007/BF02458043 Hadjileontiadis LJ, Douka E, Trochidis A (2005) Fractal dimension analysis for crack identification in beam structures. Mech Syst Signal Process 19:659–674. https://doi.org/10.1016/J.YMSSP.2004.03.005 Mousavi AA, Zhang C, Masri SF, Gholipour G (2021) Damage detection and localization of a steel truss bridge model subjected to impact and white noise excitations using empirical wavelet transform neural network approach. Measurement 185:110060. https://doi.org/10.1016/J.measurement.2021.110060 Rakowski WJ (2017) Wavelet approach to damage detection of mechanical systems and structures. Procedia Eng 182:594–601. https://doi.org/10.1016/J.PROENG.2017.03.162 Solís M, Algaba M, Galvín P (2013) Continuous wavelet analysis of mode shapes differences for damage detection. Mech Syst Signal Process 40:645–666. https://doi.org/10.1016/J.YMSSP.2013.06.006 Ghanbari Mardasi A, Wu N, Wu C (2018) Experimental study on the crack detection with optimized spatial wavelet analysis and windowing. Mech Syst Signal Process 104:619–630. https://doi.org/10.1016/j.ymssp.2017.11.039 Chiariotti P, Martarelli M, Revel GM (2017) Delamination detection by multi-level wavelet processing of continuous scanning laser Doppler vibrometry data. Opt Lasers Eng 99:66–79. https://doi.org/10.1016/J.OPTLASENG.2017.01.002 Janeliukstis R, Rucevskis S, Akishin P, Chate A (2016) Wavelet transform based damage detection in a plate structure. Procedia Eng 161:127–132. https://doi.org/10.1016/J.PROENG.2016.08.509 Pnevmatikos NG, Hatzigeorgiou GD (2017) Damage detection of framed structures subjected to earthquake excitation using discrete wavelet analysis. Bull Earthq Eng 15:227–248. https://doi.org/10.1007/s10518-016-9962-z Shahsavari V, Chouinard L, Bastien J (2017) Wavelet-based analysis of mode shapes for statistical detection and localization of damage in beams using likelihood ratio test. Eng Struct 132:494–507. https://doi.org/10.1016/J.ENGSTRUCT.2016.11.056 Cao M, Qiao P (2008) Integrated wavelet transform and its application to vibration mode shapes for the damage detection of beam-type structures. Smart Mater Struct 17:055014. https://doi.org/10.1088/0964-1726/17/5/055014 Wu N, Wang Q (2011) Experimental studies on damage detection of beam structures with wavelet transform. Int J Eng Sci 49:253–261. https://doi.org/10.1016/J.IJENGSCI.2010.12.004 Okafor AC, Dutta A (2000) Structural damage detection in beams by wavelet transforms. Smart Mater Struct 9:906–917. https://doi.org/10.1088/0964-1726/9/6/323 Montanari L, Spagnoli A, Basu B, Broderick B (2015) On the effect of spatial sampling in damage detection of cracked beams by continuous wavelet transform. J Sound Vib 345:233–249. https://doi.org/10.1016/J.JSV.2015.01.048 Yeung WT, Smith JW (2005) Damage detection in bridges using neural networks for pattern recognition of vibration signatures. Eng Struct 27:685–698. https://doi.org/10.1016/J.ENGSTRUCT.2004.12.006 Park J-H, Kim J-T, Hong D-S et al (2009) Sequential damage detection approaches for beams using time-modal features and artificial neural networks. J Sound Vib 323:451–474. https://doi.org/10.1016/J.JSV.2008.12.023 Jiang S-F, Zhang C-M, Zhang S (2011) Two-stage structural damage detection using fuzzy neural networks and data fusion techniques. Expert Syst Appl 38:511–519. https://doi.org/10.1016/J.ESWA.2010.06.093 Lam HF, Ng CT (2008) The selection of pattern features for structural damage detection using an extended Bayesian ANN algorithm. Eng Struct 30:2762–2770. https://doi.org/10.1016/J.ENGSTRUCT.2008.03.012 Padil KH, Bakhary N, Hao H (2017) The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection. Mech Syst Signal Process 83:194–209. https://doi.org/10.1016/j.ymssp.2016.06.007 Dackermann U, Li J, Samali B (2013) Identification of member connectivity and mass changes on a two-storey framed structure using frequency response functions and artificial neural networks. J Sound Vib 332:3636–3653. https://doi.org/10.1016/j.jsv.2013.02.018 Marwala T (2000) Damage identification using committee of neural networks. J Eng Mech 126:43–50. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:1(43) Bakhary N, Hao H, Deeks AJ (2007) Neural network based damage detection using substructure technique. In: 5th Australasian Congress on Applied Mechanics (ACAM 2007). pp 204–214 Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks\r. Science 313:504–507. https://doi.org/10.1126/science.1127647 Zolfaghari M, Jourabloo A, Gozlou SG et al (2014) 3D human pose estimation from image using couple sparse coding. Mach Vis Appl 25:1489–1499. https://doi.org/10.1007/s00138-014-0613-6 Wolpert DH (2002) The supervised learning no-free-lunch theorems. Soft computing and industry. Springer, London, pp 25–42 Fallahian M, Khoshnoudian F, Meruane V (2017) Ensemble classification method for structural damage assessment under varying temperature. Struct Health Monit. https://doi.org/10.1177/1475921717717311 Shi C, Pun CM (2019) Adaptive multi-scale deep neural networks with perceptual loss for panchromatic and multispectral images classification. Inf Sci (NY) 490:1–17. https://doi.org/10.1016/j.ins.2019.03.055 Zhang W, Peng G, Li C et al (2017) A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors (Switzerland). https://doi.org/10.3390/s17020425 Chen C, Zhuo R, Ren J (2019) Gated recurrent neural network with sentimental relations for sentiment classification. Inf Sci (NY) 502:268–278. https://doi.org/10.1016/j.ins.2019.06.050 Yang J, Zhang L, Chen C et al (2020) A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection. Inf Sci (NY) 540:117–130. https://doi.org/10.1016/j.ins.2020.05.090 Pearson K (1901) On lines and planes of closest fit to systems of points in space. Lond Edinb Dublin Philos Mag J Sci 2:559–572. https://doi.org/10.1080/14786440109462720 Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140. https://doi.org/10.1016/J.JHYDROL.2010.12.041 Hu W-H, Moutinho C, Caetano E et al (2012) Continuous dynamic monitoring of a lively footbridge for serviceability assessment and damage detection. Mech Syst Signal Process 33:38–55. https://doi.org/10.1016/J.YMSSP.2012.05.012 Yan AM, Kerschen G, De Boe P, Golinval JC (2005) Structural damage diagnosis under varying environmental conditions—part I: a linear analysis. Mech Syst Signal Process 19:847–864. https://doi.org/10.1016/j.ymssp.2004.12.002 Yan AM, Kerschen G, De Boe P, Golinval JC (2005) Structural damage diagnosis under varying environmental conditions—part II: local PCA for non-linear cases. Mech Syst Signal Process 19:865–880. https://doi.org/10.1016/j.ymssp.2004.12.003 Guyon I, Elisseeff A (2001) Journal of machine learning research: JMLR. MIT Press Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2:1–127. https://doi.org/10.1561/2200000006 Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313:504–507. https://doi.org/10.1126/science.1127647 Wright J, Ma Y, Mairal J et al (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98:1031–1044. https://doi.org/10.1109/JPROC.2010.2044470 Erdal HI, Karakurt O, Namli E (2013) High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform. Eng Appl Artif Intell 26:1246–1254. https://doi.org/10.1016/J.ENGAPPAI.2012.10.014 Ismail R, Mutanga O (2010) A comparison of regression tree ensembles: Predicting Sirex noctilio induced water stress in Pinus patula forests of KwaZulu-Natal, South Africa. Int J Appl Earth Obs Geoinf 12:S45–S51. https://doi.org/10.1016/J.JAG.2009.09.004 van Wezel M, Potharst R (2007) Improved customer choice predictions using ensemble methods. Eur J Oper Res 181:436–452. https://doi.org/10.1016/J.EJOR.2006.05.029 Farrar CR, Baker WE, Bell TM et al (1994) Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande Mayes RL (1995) An experimental algorithm for detecting damage applied to the I-40 bridge over the Rio Grande. In: Proc 13th Int Modal Anal Conf, pp 219–225. https://doi.org/10.1117/12.207729 Meruane V, Heylen W (2012) Structural damage assessment under varying temperature conditions. Struct Health Monit 11:345–357. https://doi.org/10.1177/1475921711419995