Abstract
Structural Health Monitoring (SHM) via data-driven techniques can be based upon vibrations acquired by sensor networks. However, technical and economic reasons may prevent the deployment of pervasive sensor networks over civil structures, thus limiting their reliability in terms of damage detection. Moreover, the effects of environmental (and operational) variability may lead to false alarms. To address these challenges, a multi-stage machine learning (ML) method is here proposed by exploiting autoregressive (AR) spectra as damage-sensitive features. The proposed method is framed as follows: (i) computing the distances between different sets of the AR spectra via the log-spectral distance (LSD), providing also the training and test datasets; (ii) removing the potential environmental variability by an auto-associative artificial neural network (AANN), to set normalized training and test datasets; (iii) running a statistical analysis via the Mahalanobis-squared distance (MSD) for early damage detection. The effectiveness of the proposed approach is assessed in the case of limited vibration data for the laboratory truss structure known as the Wooden Bridge. Comparative studies show that the AR spectrum is a reliable feature, sensitive to damage even in the presence of a limited number of sensors in the network; additionally, the multi-stage ML methodology succeeds in early detecting damage under environmental variability.
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References
Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. John Wiley & Sons Ltd., Hoboken (2013)
He, L., Lian, J., Ma, B.: Intelligent damage identification method for large structures based on strain modal parameters. J. Vib. Control 20(12), 1783–1795 (2014)
Yan, W.-J., Ren, W.-X.: Closed-form modal flexibility sensitivity and its application to structural damage detection without modal truncation error. J. Vib. Control 20(12), 1816–1830 (2014)
Quaranta, G., Carboni, B., Lacarbonara, W.: Damage detection by modal curvatures: numerical issues. J. Vib. Control 22(7), 1913–1927 (2016)
Entezami, A., Shariatmadar, H., Sarmadi, H.: Structural damage detection by a new iterative regularization method and an improved sensitivity function. J. Sound Vib. 399, 285–307 (2017)
Eftekhar Azam, S., Mariani, S.: Online damage detection in structural systems via dynamic inverse analysis: a recursive Bayesian approach. Eng. Struct. 159, 28–45 (2018)
Entezami, A., Shariatmadar, H.: Damage localization in shear buildings by direct updating of physical properties. Int. J. Adv. Struct. Eng. (IJASE) 6(2), 1–12 (2014). https://doi.org/10.1007/s40091-014-0058-1
Entezami, A., Shariatmadar, H., Ghalehnovi, M.: Damage detection by updating structural models based on linear objective functions. J. Civ. Struct. Heal. Monit. 4(3), 165–176 (2014). https://doi.org/10.1007/s13349-014-0072-9
Sarmadi, H., Karamodin, A., Entezami, A.: A new iterative model updating technique based on least squares minimal residual method using measured modal data. Appl. Math. Model. 40(23–24), 10323–10341 (2016)
Shadan, F., Khoshnoudian, F., Inman, D.J., Esfandiari, A.: Experimental validation of a FRF-based model updating method. J. Vib. Control 24(8), 1570–1583 (2018)
Rezaiee-Pajand, M., Entezami, A., Sarmadi, H.: A sensitivity-based finite element model updating based on unconstrained optimization problem and regularized solution methods. Struct. Control Health Monit. 27, e2481 (2019)
Entezami, A., Shariatmadar, H., Karamodin, A.: Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods. Struct. Health Monit. 18(5–6), 1416–1443 (2019)
Entezami, A., Shariatmadar, H., Mariani, S.: Fast unsupervised learning methods for structural health monitoring with large vibration data from dense sensor networks. Struct. Health Monit. 19(6), 1685–1710 (2020)
Entezami, A., Shariatmadar, H., De Michele, C.: Non-parametric empirical machine learning for short-term and long-term structural health monitoring. Struct. Health Monit. (2022)
Entezami, A., Sarmadi, H., Salar, M., De Michele, C., Arslan, A. N.: A novel data-driven method for structural health monitoring under ambient vibration and high-dimensional features by robust multidimensional scaling. Struct. Health Monit. (2020)
Deza, M., Deza, E.: Encyclopedia of Distances. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44342-2
Feltrin, G., Jalsan, K.E., Flouri, K.: Vibration monitoring of a footbridge with a wireless sensor network. J. Vib. Control 19(15), 2285–2300 (2013)
Capellari, G., Chatzi, E., Mariani, S.: Cost-benefit optimization of sensor networks for SHM applications. In: Multidisciplinary Digital Publishing Institute Proceedings (2017)
Capellari, G., Chatzi, E., Mariani, S.: Structural health monitoring sensor network optimization through bayesian experimental design. ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A: Civil Eng. 4(2), 04018016 (2018)
Entezami, A., Shariatmadar, H., Mariani, S.: Structural health monitoring for condition assessment using efficient supervised learning techniques. In: Proceedings, vol. 42, no. 1, p. 17 (2020)
Entezami, A., Shariatmadar, H., Mariani, S.: Early damage assessment in large-scale structures by innovative statistical pattern recognition methods based on time series modeling and novelty detection. Adv. Eng. Softw. 150, 102923 (2020)
Entezami, A., Sarmadi, H., Behkamal, B., Mariani, S.: Big data analytics and structural health monitoring: a statistical pattern recognition-based approach. Sensors 20(8), 2328 (2020)
Entezami, A., Sarmadi, H., Behkamal, B., Mariani, S.: Health monitoring of large-scale civil structures: an approach based on data partitioning and classical multidimensional scaling. Sensors 21(5), 1646 (2021)
Ostachowicz, W., Soman, R., Malinowski, P.: Optimization of sensor placement for structural health monitoring: a review. Struct. Health Monit. 18(3), 963–988 (2019)
Bagheri, A., Zare Hosseinzadeh, A., Rizzo, P., Ghodrati Amiri, G.: Time domain damage localization and quantification in seismically excited structures using a limited number of sensors. J. Vib. Control 23(18), 2942–2961 (2017)
Nie, Z., Lin, J., Li, J., Hao, H., Ma, H.: Bridge condition monitoring under moving loads using two sensor measurements. Struct. Health Monit. 19, 917–937 (2019)
Entezami, A., Mariani, S., Shariatmadar, H.: Damage detection in largely unobserved structures under varying environmental conditions: an autoregressive spectrum and multi-level machine learning methodology. Sensors 22(4), 1400 (2022)
Figueiredo, E., Park, G., Farrar, C.R., Worden, K., Figueiras, J.: Machine learning algorithms for damage detection under operational and environmental variability. Struct. Health Monit. 10(6), 559–572 (2011)
Sarmadi, H., Karamodin, A.: A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects. Mech. Syst. Signal Process. 140, 106495 (2019)
Sarmadi, H., Entezami, A., Salar, M., De Michele, C.: Bridge health monitoring in environmental variability by new clustering and threshold estimation methods. J. Civ. Struct. Heal. Monit. 11(3), 629–644 (2021). https://doi.org/10.1007/s13349-021-00472-1
Castanié, F.: Spectral Analysis: Parametric and Non-Parametric Digital Methods. John Wiley & Sons, Hoboken (2013)
Stoica, P., Moses, R.L.: Introduction to Spectral Analysis, vol. 1. Prentice hall, Upper Saddle River (1997)
Entezami, A., Shariatmadar, H.: An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification. Struct. Health Monit. 17(2), 325–345 (2018)
Rabiner, L., Rabiner, L.R., Juang, B.H.: Fundamentals of Speech Recognition. PTR Prentice Hall, Upper Saddle River (1993)
Kramer, M.A.: Autoassociative neural networks. Comput. Chem. Eng. 16(4), 313–328 (1992)
Sarmadi, H., Karamodin, A.: A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects. Mech. Syst. Signal Process. 140, 106495 (2020)
Sarmadi, H., Entezami, A.: Application of supervised learning to validation of damage detection. Arch. Appl. Mech. 91(1), 393–410 (2020). https://doi.org/10.1007/s00419-020-01779-z
Kullaa, J.: Distinguishing between sensor fault, structural damage, and environmental or operational effects in structural health monitoring. Mech. Syst. Signal Process. 25(8), 2976–2989 (2011)
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Entezami, A., Mariani, S., Shariatmadar, H. (2023). A Multi-stage Machine Learning Methodology for Health Monitoring of Largely Unobserved Structures Under Varying Environmental Conditions. In: Rizzo, P., Milazzo, A. (eds) European Workshop on Structural Health Monitoring. EWSHM 2022. Lecture Notes in Civil Engineering, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-031-07258-1_31
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