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Structural Damage Condition of Buildings with a Sparse Number of Sensors Using Machine Learning: Case Study

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Building for the Future: Durable, Sustainable, Resilient (fib Symposium 2023)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 350))

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Abstract

The optimum location of signal sensors in actual buildings to determine the structural damage condition using machine learning is discussed in this study. The target buildings are a local government office and a Fire Station in Japan, with two acceleration sensors located on the ground and the roof level of the buildings. An additional sensor location is considered in this study. The structural damage condition is evaluated by machine learning (ML) methods from the sensor signals for five cases of single and multiple sensor locations. The maximum story drift is used as an identifier of the structural damage condition. Seven ML methods are developed, and their accuracy is compared. Several intensity measures (IM) obtained from each sensor signal are used as input features for the ML models, and the prediction importance level of each IM is evaluated in order to establish its usefulness. Finally, the results are compared to the methodology using wavelet power spectrum and convolutional neural network to predict the damage condition of buildings.

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References

  1. Akelyan MS et al (2020) An alternative procedure for seismic analysis and design of tall buildings located in the los angeles region 2020 edition. Los Angeles Tall Buildings Structural Design Council 2020

    Google Scholar 

  2. PEER Center (2010) Guidelines for performance-based seismic design of tall buildings; Pacific Earthquake Engineering Research Center, College of Engineering

    Google Scholar 

  3. Xu K, Mita A (2020) Estimation of maximum drift of MDOF shear structures using only one accelerometer. In: Materials Research Proceedings 2020, p 18

    Google Scholar 

  4. Moscoso Alcantara EA, Saito T (2022) Convolutional neural network-based rapid post-earthquake structural damage detection: case study. Sensors 22:6426

    Article  Google Scholar 

  5. Capellari G, Chatzi E, Mariani S (2016) An optimal sensor placement method for SHM based on Bayesian experimental design and Polynomial Chaos Expansion. In: Proceedings of the ECCOMAS congress 2016 PROCEEDINGS, pp 6272–6282

    Google Scholar 

  6. Zhang J, Maes K, De Roeck G, Reynders E, Papadimitriou C, Lombaert G (2017) Optimal sensor placement for multi-setup modal analysis of structures. J Sound Vib 401:214–232

    Article  Google Scholar 

  7. Tan Y, Zhang L (2020) Computational methodologies for optimal sensor placement in structural health monitoring: a review. Struct Health Monit 19:1287–1308

    Article  Google Scholar 

  8. Buratti N (2012) A comparison of the performances of various ground–motion intensity measures. In: Proceedings of the Proceedings of the 15th world conference on earthquake engineering, Lisbon, Portugal, pp 24–28

    Google Scholar 

  9. Xu Y, Lu X, Tian Y, Huang Y (2020) Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning. J Earthq Eng, 1–21

    Google Scholar 

  10. Douglas J (2003) Earthquake ground motion estimation using strong-motion records: a review of equations for the estimation of peak ground acceleration and response spectral ordinates. Earth Sci Rev 61:43–104

    Article  Google Scholar 

  11. Chopra AK (2007) Elastic response spectrum: a historical note. Earthquake Eng Struct Dynam 36:3–12

    Article  Google Scholar 

  12. Baker JW, Allin Cornell C (2006) Spectral shape, epsilon and record selection. Earthquake Eng Struct Dynam 35:1077–1095

    Article  Google Scholar 

  13. Newmark NM, Hall WJ (1982) Earthquake spectra and design. Engineering monographs on earthquake criteria

    Google Scholar 

  14. Mehanny SS (2009) A broad-range power-law form scalar-based seismic intensity measure. Eng Struct 31:1354–1368

    Article  Google Scholar 

  15. Housner G (1975) Measures of severity of earthquake ground shaking. In: Proceedings of the Proceedings of US National Conference on Earthquake Engineering, p 6

    Google Scholar 

  16. Bojórquez E, Iervolino I (2011) Spectral shape proxies and nonlinear structural response. Soil Dyn Earthq Eng 31:996–1008

    Article  Google Scholar 

  17. Arias A (1970) A measure of earthquake intensity. Seismic design for nuclear power plants. Massachusetts Institute of Technology

    Google Scholar 

  18. Sarma S, Yang K (1987) An evaluation of strong motion records and a new parameter A95. Earthq Eng Struct Dynam 15:119–132

    Article  Google Scholar 

  19. Park Y-J, Ang AH-S, Wen YK (1985) Seismic damage analysis of reinforced concrete buildings. J Struct Eng 111:740–757

    Article  Google Scholar 

  20. Riddell R, Garcia JE (2001) Hysteretic energy spectrum and damage control. Earthq Eng Struct Dynam 30:1791–1816

    Article  Google Scholar 

  21. Reed JW, Kassawara RP (1990) A criterion for determining exceedance of the operating basis earthquake. Nucl Eng Des 123:387–396

    Article  Google Scholar 

  22. Campbell KW, Bozorgnia Y (2011) Prediction equations for the standardized version of cumulative absolute velocity as adapted for use in the shutdown of US nuclear power plants. Nucl Eng Des 241:2558–2569

    Article  Google Scholar 

  23. Cordova PP, Deierlein GG, Mehanny SS, Cornell CA (2000) Development of a two-parameter seismic intensity measure and probabilistic assessment procedure. In: Proceedings of the second US-Japan workshop on performance-based earthquake engineering methodology for reinforced concrete building structures, pp 187–206

    Google Scholar 

  24. Bommer JJ, Alarcon JE (2006) The prediction and use of peak ground velocity. J Earthquake Eng 10:1–31

    Article  Google Scholar 

  25. Fajfar P, Vidic T, Fischinger M (1990) A measure of earthquake motion capacity to damage medium-period structures. Soil Dyn Earthq Eng 9:236–242

    Article  Google Scholar 

  26. Housner GW (1952) Intensity of ground motion during strong earthquakes

    Google Scholar 

  27. Cosenza E, Manfredi G (1998) A seismic design method including damage effect. In: Proceedings of the 11th european conference on earthquake engineering, pp 6–11

    Google Scholar 

  28. Géron A (2022) Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly Media, Inc. Sebastopol

    Google Scholar 

  29. Bishop CM, Nasrabadi NM (2006) Pattern recognition and machine learning, vol 4. Springer, Heidelberg

    Google Scholar 

  30. Shalev-Shwartz S, Ben-David S (2014) Understanding machine learning: From theory to algorithms. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  31. Su X, Yan X, Tsai CL (2012) Linear regression. Wiley Interdiscip Rev Comput Stat 4:275–294

    Article  Google Scholar 

  32. Daumé, H (2017) A course in machine learning. Hal Daumé III

    Google Scholar 

  33. Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD (2004) An introduction to decision tree modeling. J Chemometrics J Chemometrics Soc 18:275–285

    Article  Google Scholar 

  34. Biau G, Scornet E (2016) A random forest guided tour. TEST 25(2):197–227. https://doi.org/10.1007/s11749-016-0481-7

    Article  MathSciNet  MATH  Google Scholar 

  35. Chen T et al (2015) Xgboost: extreme gradient boosting. R package version 0.4-2, 1:1–4

    Google Scholar 

  36. Noriega L (2005) Multilayer perceptron tutorial. School of Computing. Staffordshire University, vol 4, p 5

    Google Scholar 

  37. Center for Engineering Strong Motion Data (CESMD). https://www.strongmotioncenter.org/. Accessed 1 Mar 2021

  38. Shome N (1999) Probabilistic seismic demand analysis of nonlinear structures. Stanford University

    Google Scholar 

  39. Moscoso Alcantara EA, Bong MD, Saito T (2021) Structural response prediction for damage identification using wavelet spectra in convolutional neural network. Sensors 21:6795

    Article  Google Scholar 

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Authors and Affiliations

  1. Toyohashi University of Technology, Toyohashi, 441-8580, Aichi, Japan

    Edisson Alberto Moscoso Alcantara & Taiki Saito

Authors
  1. Edisson Alberto Moscoso Alcantara
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  2. Taiki Saito
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Corresponding author

Correspondence to Edisson Alberto Moscoso Alcantara .

Editor information

Editors and Affiliations

  1. Civil Engineering Faculty, Istanbul Technical University, Sarıyer-Istanbul, Türkiye

    Alper Ilki

  2. Civil Engineering Faculty, Istanbul Technical University, Sarıyer-Istanbul, Türkiye

    Derya Çavunt

  3. Civil Engineering Faculty, Istanbul Technical University, Sarıyer-Istanbul, Türkiye

    Yavuz Selim Çavunt

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Moscoso Alcantara, E.A., Saito, T. (2023). Structural Damage Condition of Buildings with a Sparse Number of Sensors Using Machine Learning: Case Study. In: Ilki, A., Çavunt, D., Çavunt, Y.S. (eds) Building for the Future: Durable, Sustainable, Resilient. fib Symposium 2023. Lecture Notes in Civil Engineering, vol 350. Springer, Cham. https://doi.org/10.1007/978-3-031-32511-3_15

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  • DOI: https://doi.org/10.1007/978-3-031-32511-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32510-6

  • Online ISBN: 978-3-031-32511-3

  • eBook Packages: EngineeringEngineering (R0)

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