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A Review on Technological Advancements in the Field of Data Driven Structural Health Monitoring

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European Workshop on Structural Health Monitoring (EWSHM 2022)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 270))

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Abstract

Recent advancements in sensor technology, as well as fast progress in internet-based cloud computation; data-driven approaches in structural health monitoring (SHM) are gaining prominence. The majority of time is utilized for reviewing & analyzing the data received from various sensors deployed in structures. This data analysis helps in understating the structural stability and its current state with certain limitations. Considering this fact, integration with Machine Learning (ML) in SHM has attracted significant attention among researchers. This paper is principally aimed at understanding and reviewing of vast literature available in sensor-based data-driven approaches using ML. The implementation and methodology of vibration-based, vision-based monitoring, along with some of the ML algorithms used for SHM are discussed. Nevertheless, a perspective on the importance of data-driven SHM in the future is also presented. Conclusions are drawn from the review discuss the prospects and potential limitations of ML approaches in data-driven SHM applications.

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References

  1. Farrar, C.R., Worden, K.: An introduction to structural health monitoring. Philos. Trans. Roy. Soc. A: Math. Phys. Eng. Sci. 365(1851), 303–315 (2007)

    Article  Google Scholar 

  2. Sohn, H., et al.: A review of structural health monitoring literature: 1996–2001. Los Alamos National Laboratory, USA (2003)

    Google Scholar 

  3. Olanitori, L.M.: Causes of structural failures of a building: case study of a building at Oba-Ile, Akure. J. Build. Appraisal 6(3), 277–284 (2011)

    Article  Google Scholar 

  4. Wardhana, K., Hadipriono, F.C.: Study of recent building failures in the United States. J. Perform. Constr. Facil. 17(3), 151–158 (2003)

    Article  Google Scholar 

  5. Garg, R.K., Chandra, S., Kumar, A.: Analysis of bridge failures in India from 1977 to 2017. Struct. Infrastruct. Eng. 18(3), 295–312 (2022)

    Article  Google Scholar 

  6. Chatterjee, P.: Urban building collapse: what are the health implications? BMJ 349 (2014)

    Google Scholar 

  7. Rosales, M.J., Liyanapathirana, R.: Data driven innovations in structural health monitoring. In: Journal of Physics: Conference Series, vol. 842, no. 1, p. 012012. IOP Publishing (2017)

    Google Scholar 

  8. Luckey, D., Fritz, H., Legatiuk, D., Peralta Abadía, J.J., Walther, C., Smarsly, K.: Explainable artificial intelligence to advance structural health monitoring. In: Cury, A., Ribeiro, D., Ubertini, F., Todd, M.D. (eds.) Structural Health Monitoring Based on Data Science Techniques. SI, vol. 21, pp. 331–346. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-81716-9_16

    Chapter  Google Scholar 

  9. Azimi, M., Eslamlou, A.D., Pekcan, G.: Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review. Sensors 20(10), 2778 (2020)

    Article  Google Scholar 

  10. Tibaduiza, D., Torres-Arredondo, M.Á., Vitola, J., Anaya, M., Pozo, F.: A damage classification approach for structural health monitoring using machine learning. Complexity (2018)

    Google Scholar 

  11. Wang, Y., Zhao, Y., Addepalli, S.: Remaining useful life prediction using deep learning approaches: a review. Proc. Manuf. 49, 81–88 (2020)

    Google Scholar 

  12. Cardoso, R., Cury, A., Barbosa, F.: A robust methodology for modal parameters estimation applied to SHM. Mech. Syst. Signal Process. 95, 24–41 (2017)

    Article  Google Scholar 

  13. Deraemaeker, A., Reynders, E., De Roeck, G., Kullaa, J.: Vibration based SHM: comparison of the performance of modal features vs features extracted from spatial filters under changing environmental conditions. In: ISMA2006 International Conference on Noise and Vibration Engineering, pp. 849–864 (2006)

    Google Scholar 

  14. Kamariotis, A., Chatzi, E., Straub, D.: A framework for quantifying the value of vibration-based structural health monitoring. arXiv preprint arXiv:2202.01859 (2022)

  15. Das, S., Saha, P., Patro, S.K.: Vibration-based damage detection techniques used for health monitoring of structures: a review. J. Civ. Struct. Heal. Monit. 6(3), 477–507 (2016). https://doi.org/10.1007/s13349-016-0168-5

    Article  Google Scholar 

  16. Huang, Q., Gardoni, P., Hurlebaus, S.: A probabilistic damage detection approach using vibration-based nondestructive testing. Struct. Saf. 38, 11–21 (2012)

    Article  Google Scholar 

  17. Yan, Y.J., Cheng, L., Wu, Z.Y., Yam, L.H.: Development in vibration-based structural damage detection technique. Mech. Syst. Signal Process. 21(5), 2198–2211 (2007)

    Article  Google Scholar 

  18. Borate, P., Wang, G., Wang, Y.: Data-driven structural health monitoring approach using guided Lamb wave responses. J. Aerosp. Eng. 33(4), 04020033 (2020)

    Article  Google Scholar 

  19. Muin, S., Mosalam, K.M.: Structural health monitoring using machine learning and cumulative absolute velocity features. Appl. Sci. 11(12), 5727 (2021)

    Article  Google Scholar 

  20. Maes, K., Van Meerbeeck, L., Reynders, E.P.B., Lombaert, G.: Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51. Mech. Syst. Signal Process. 165, 108380 (2022)

    Article  Google Scholar 

  21. Zhang, Y., Miyamori, Y., Mikami, S., Saito, T.: Vibration-based structural state identification by a 1-dimensional convolutional neural network. Comput.-Aided Civ. Infrastr. Eng. 34(9), 822–839 (2019)

    Article  Google Scholar 

  22. Ghiasi, R., Torkzadeh, P., Noori, M.: A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function. Struct. Health Monit. 15(3), 302–316 (2016)

    Article  Google Scholar 

  23. Rafiei, M.H., Adeli, H.: A novel machine learning-based algorithm to detect damage in high-rise building structures. Struct. Design Tall Spec. Build. 26(18), e1400 (2017)

    Article  Google Scholar 

  24. Li, S., Sun, L.: Detectability of bridge-structural damage based on fiber-optic sensing through deep-convolutional neural networks. J. Bridg. Eng. 25(4), 04020012 (2020)

    Article  MathSciNet  Google Scholar 

  25. Pathirage, C.S.N., Li, J., Li, L., Hao, H., Liu, W., Ni, P.: Structural damage identification based on autoencoder neural networks and deep learning. Eng. Struct. 172, 13–28 (2018)

    Article  Google Scholar 

  26. Lin, Y.Z., Nie, Z.H., Ma, H.W.: Structural damage detection with automatic feature-extraction through deep learning. Comput.-Aided Civ. Infrastr. Eng. 32(12), 1025–1046 (2017)

    Article  Google Scholar 

  27. Yu, Y., Wang, C., Gu, X., Li, J.: A novel deep learning-based method for damage identification of smart building structures. Struct. Health Monit. 18(1), 143–163 (2019)

    Article  Google Scholar 

  28. Ye, X.W., Dong, C.Z., Liu, T.: A review of machine vision-based structural health monitoring: methodologies and applications. J. Sens. (2016)

    Google Scholar 

  29. Kot, P., Muradov, M., Gkantou, M., Kamaris, G.S., Hashim, K., Yeboah, D.: Recent advancements in non-destructive testing techniques for structural health monitoring. Appl. Sci. 11(6), 2750 (2021)

    Article  Google Scholar 

  30. Kadarla, S., Beeram, S.K., Kalapatapu, P., Pasupuleti, V.D.K.: Concrete crack detection from video footage for structural health monitoring. In: Rizzo, P., Milazzo, A. (eds.) EWSHM 2020. LNCE, vol. 127, pp. 79–88. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64594-6_9

    Chapter  Google Scholar 

  31. Gandham, L.M., Kota, J.R., Kalapatapu, P., Pasupuleti, V.D.K.: A survey on current heritage structural health monitoring practices around the globe. In: Ioannides, M., Fink, E., Cantoni, L., Champion, E. (eds.) EuroMed 2020. LNCS, vol. 12642, pp. 565–576. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73043-7_48

    Chapter  Google Scholar 

  32. Mishra, M.: Machine learning techniques for structural health monitoring of heritage buildings: a state-of-the-art review and case studies. J. Cult. Herit. 47, 227–245 (2021)

    Article  Google Scholar 

  33. Vundekode, N.R., Kalapatapu, P., Pasupuleti, V.D.K.: A study on vision based method for damage detection in structures. In: Rizzo, P., Milazzo, A. (eds.) EWSHM 2020. LNCE, vol. 127, pp. 96–105. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64594-6_11

    Chapter  Google Scholar 

  34. Nedunuri, S., Thota, N., Pasupuleti, V.D.K., Kalapatapu, P.: Investigation of crack properties using image processing: an user interface. In: Babu, K.G., Rao, H.S., Amarnath, Y. (eds.) Emerging Trends in Civil Engineering. LNCE, vol. 61, pp. 81–90. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-1404-3_8

    Chapter  Google Scholar 

  35. Hoła, J., Schabowicz, K.: New technique of nondestructive assessment of concrete strength using artificial intelligence. NDT E Int. 38(4), 251–259 (2005)

    Article  Google Scholar 

  36. Kewalramani, M.A., Gupta, R.: Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Autom. Constr. 15(3), 374–379 (2006)

    Article  Google Scholar 

  37. Cho, Y.S., Hong, S.U., Lee, M.S.: The assessment of the compressive strength and thickness of concrete structures using nondestructive testing and an artificial neural network. Nondestruct. Test. Eval. 24(3), 277–288 (2009)

    Article  Google Scholar 

  38. Beckman, G.H., Polyzois, D., Cha, Y.J.: Deep learning-based automatic volumetric damage quantification using depth camera. Autom. Constr. 99, 114–124 (2019)

    Article  Google Scholar 

  39. Jang, K., Kim, N., An, Y.K.: Deep learning–based autonomous concrete crack evaluation through hybrid image scanning. Struct. Health Monit. 18(5–6), 1722–1737 (2019)

    Article  Google Scholar 

  40. Ni, F., Zhang, J., Chen, Z.: Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning. Comput.-Aided Civ. Infrastr. Eng. 34(5), 367–384 (2019)

    Article  Google Scholar 

  41. Huynh, A.T., et al.: A machine learning-assisted numerical predictor for compressive strength of geopolymer concrete based on experimental data and sensitivity analysis. Appl. Sci. 10(21), 7726 (2020)

    Article  Google Scholar 

  42. Narazaki, Y., Hoskere, V., Yoshida, K., Spencer, B.F., Fujino, Y.: Synthetic environments for vision-based structural condition assessment of Japanese high-speed railway viaducts. Mech. Syst. Signal Process. 160, 107850 (2021)

    Article  Google Scholar 

  43. Asteris, P.G., Skentou, A.D., Bardhan, A., Samui, P., Lourenço, P.B.: Soft computing techniques for the prediction of concrete compressive strength using non-destructive tests. Constr. Build. Mater. 303, 124450 (2021)

    Article  Google Scholar 

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Authors and Affiliations

  1. Ecole Centrale College of Engineering, Mahindra University, Hyderabad, India

    Rakesh Katam, Prafulla Kalapatapu & Venkata Dilip Kumar Pasupuleti

Authors
  1. Rakesh Katam
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  2. Prafulla Kalapatapu
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  3. Venkata Dilip Kumar Pasupuleti
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Correspondence to Rakesh Katam .

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Editors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA

    Piervincenzo Rizzo

  2. Department of Engineering, University of Palermo, Palermo, Italy

    Alberto Milazzo

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Katam, R., Kalapatapu, P., Pasupuleti, V.D.K. (2023). A Review on Technological Advancements in the Field of Data Driven Structural Health Monitoring. In: Rizzo, P., Milazzo, A. (eds) European Workshop on Structural Health Monitoring. EWSHM 2022. Lecture Notes in Civil Engineering, vol 270. Springer, Cham. https://doi.org/10.1007/978-3-031-07322-9_38

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  • DOI: https://doi.org/10.1007/978-3-031-07322-9_38

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  • Print ISBN: 978-3-031-07321-2

  • Online ISBN: 978-3-031-07322-9

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