Structural Engineering and Mechanics

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Empirical seismic vulnerability probability prediction model of RC structures considering historical field observation

  • Si-Qi Li (School of Civil Engineering, Heilongjiang University) ;
  • Hong-Bo Liu (School of Civil Engineering, Heilongjiang University) ;
  • Ke Du (School of Civil Engineering, Heilongjiang University) ;
  • Jia-Cheng Han (School of Civil Engineering, Heilongjiang University) ;
  • Yi-Ru Li (School of Civil Engineering, Heilongjiang University) ;
  • Li-Hui Yin (School of Civil Engineering, Heilongjiang University)
  • Received : 2022.07.12
  • Accepted : 2023.04.17
  • Published : 2023.05.25
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Abstract

To deeply probe the actual earthquake level and fragility of typical reinforced concrete (RC) structures under multiple intensity grades, considering diachronic measurement building stock samples and actual observations of representative catastrophic earth shocks in China from 1990 to 2010, RC structures were divided into traditional RC structures (TRCs) and bottom reinforced concrete frame seismic wall masonry (BFM) structures, and the empirical damage characteristics and mechanisms were analysed. A great deal of statistics and induction were developed on the historical experience investigation data of 59 typical catastrophic earthquakes in 9 provinces of China. The database and fragility matrix prediction model were established with TRCs of 4,122.5284×104 m2 and 5,844 buildings and BFMs of 5,872 buildings as empirical seismic damage samples. By employing the methods of structural damage probability and statistics, nonlinear prediction of seismic vulnerability, and numerical and applied functional analysis, the comparison matrix of actual fragility probability prediction of TRC and BFM in multiple intensity regions under the latest version of China's macrointensity standard was established. A novel nonlinear regression prediction model of seismic vulnerability was proposed, and prediction models considering the seismic damage ratio and transcendental probability parameters were constructed. The time-varying vulnerability comparative model of the sample database was developed according to the different periods of multiple earthquakes. The new calculation method of the average fragility prediction index (AFPI) matrix parameter model has been proposed to predict the seismic fragility of an areal RC structure.

Keywords

Acknowledgement

This study's seismic sample data and pictures of structural earthquake damage observations were derived from the Institute of Engineering Mechanics, China Earthquake Administration. This study was supported by the Basic Scientific Research Business Expenses of Provincial Universities and Colleges in Heilongjiang Province (2022-KYYWF-1056 and 2021-KYYWF-0013) and a project funded by Heilongjiang Postdoctoral Science Foundation (LBH-Z22294), China.

References

  1. Alcocer, S.M., Muria-Vila, D., Fernandez-Sola, L.R., Ordaz, M. and Arce, J.C. (2020), "Observed damage in public school buildings during the 2017 Mexico earthquakes", Earthq. Spectra, 36(S2), 110-129. https://doi.org/10.1177/8755293020926183.
  2. Aljawhari, K., Gentile, R., Freddi, F. and Galasso, C. (2020), "Effects of ground-motion sequences on fragility and vulnerability of case-study reinforced concrete frames", Bull. Earthq. Eng., 19, 6329-6359. https://doi.org/10.1007/s10518-020-01006-8.
  3. Camelbeeck, T., Knuts, E., Alexandre, P., Lecocq, T. and Noten, K. V. (2021), "The 23 February 1828 Belgian earthquake: a destructive moderate event typical of the seismic activity in Western Europe", J. Seismol., 25(2), 369-391. https://doi.org/10.1007/s10950-020-09977-6.
  4. China Earthquake Administration and National Bureau of Statistics (2001), Compilation of Loss Assessment for Earthquake Disasters in Mainland China (1996-2000), Earthquake Press, Beijing.
  5. China Earthquake Administration and National Bureau of Statistics (1996), Compilation of Loss Assessment for Earthquake Disasters in Mainland China (1990-1995), Earthquake Press, Beijing.
  6. China Earthquake Administration and National Bureau of Statistics (2005), Compilation of Loss Assessment for Earthquake Disasters in Mainland China (2001-2005), Earthquake Press, Beijing.
  7. Dona, M., Carpanese, P., Follador, V., Sbrogio, L. and da Porto, F. (2021), "Mechanics-based fragility curves for Italian residential URM buildings", Bull. Earthq. Eng., 19(8), 3099-3127. https://doi.org/10.1007/s10518-020-00928-7.
  8. Ghasemi, H., Cummins, P., Weatherill, G., McKee, C., Hazelwood, M. and Allen, T. (2020), "Seismotectonic model and probabilistic seismic hazard assessment for Papua New Guinea", Bull. Earthq. Eng., 18(15), 6571-6605. https://doi.org/10.1007/s10518-020-00966-1.
  9. Ghotbi, A.R. and Taciroglu, E. (2021), "Structural seismic damage and loss assessments using a multi-conditioning ground motion selection approach based on an efficient sampling technique", Bull. Earthq. Eng., 19(3), 1271-1287. https://doi.org/10.1007/s10518-020-01016-6.
  10. Gomez-Zapata, J.C., Pittore, M., Cotton, F., Lilienkamp, H., Shnde, S., Aguirre, P. and Santa, M.H. (2022), "Epistemic uncertainty of probabilistic building exposure compositions in scenario‑based earthquake loss models", Bull. Earthq. Eng., 20(5), 2401-2438. https://doi.org/10.1007/s10518-021-01312-9.
  11. Heo, Y. and Lee, S.J. (2021), "Development of a multi-unit seismic conditional core damage probability model with uncertainty analysis", Reliab. Eng. Syst. Saf., 207, 107353. https://doi.org/10.1016/j.ress.2020.107353.
  12. Hwang, S.H., Mangalathu, S., Shin, J. and Jeon, J.S. (2021), "Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames", J. Build. Eng., 34, 101905. https://doi.org/10.1016/j.jobe.2020.101905.
  13. Jeon, J.S., Mangalathu, S., Song, J. and Desroches, R. (2019), "Parameterized seismic fragility curves for curved multi-frame concrete box-girder bridges using Bayesian parameter estimation", J. Earthq. Eng., 23(6), 954-979. https://doi.org/10.1080/13632469.2017.1342291.
  14. Kalfarisi, R., Hmosze, M. and Wu, Z.Y. (2022), "Detecting and geolocating city-scale soft-story buildings by deep machine learning for urban seismic resilience", Nat. Hazard. Rev., 23(1), 04021062. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000541.
  15. Kassem, M.M., Nazri, F.M., Farsangi, E.N. and Ozturk, B. (2022a), "Development of a uniform seismic vulnerability index framework for reinforced concrete building typology", J. Build. Eng., 47, 103838. https://doi.org/10.1016/j.jobe.2021.103838.
  16. Kassem, M.M., Nazri, F.M., Farsangi, E.N. and Ozturk, B. (2022b), "Improved vulnerability index methodology to quantify seismic risk and loss assessment in reinforced concrete buildings", J. Earthq. Eng., 26(12), 6172-6207. https://doi.org/10.1080/13632469.2021.1911888.
  17. Kohrangi, M., Bazzurro, P. and Vamvatsikos, D. (2021), "Seismic risk and loss estimation for the building stock in Isfahan. Part I: exposure and vulnerability", Bull. Earthq. Eng., 19(4), 1709-1737. https://doi.org/10.1007/s10518-020-01036-2.
  18. Lagomarsino, S. and Giovinazzin, S. (2006), "Macroseismic and mechanical models for the vulnerability and damage assessment of current buildings", Bull. Earthq. Eng., 4, 415-443. https://doi.org/10.1007/s10518-006-9024-z.
  19. Lan, Y.J., Stavridis, A., Kim, I., Diaz-Fanas, G., Heintz, J., Hernandez-Bassal, L., ... & Gilsanz, R. (2020), "ATC Mw7.1 Puebla-Morelos earthquake reconnaissance observations: Structural observations and instrumentation", Earthq. Spectra, 36(S2), 31-48. https://doi.org/10.1177/8755293020977520.
  20. Lee, U. and Lee, I. (2022), "Efficient sampling-based inverse reliability analysis combining Monte Carlo simulation (MCS) and feedforward neural network (FNN)", Struct. Multidisc. Optim., 65(1), 8. https://doi.org/10.1007/s00158-021-03144-2.
  21. Li, S.Q. (2022), "Analysis of an empirical seismic fragility prediction model of wooden roof truss buildings", Case Stud. Constr. Mater., 17, e01420. https://doi.org/10.1016/j.cscm.2022.e01420.
  22. Li, S.Q. (2023a), "Comparison of empirical structural vulnerability rapid prediction models considering typical earthquakes", Struct., 49, 377-401. https://doi.org/10.1016/j.istruc.2023.01.130.
  23. Li, S.Q. (2023b), "Empirical resilience and vulnerability model of regional group structure considering optimized macroseismic intensity measure", Soil Dyn. Earthq. Eng., 164, 107630. https://doi.org/10.1016/j.soildyn.2022.107630.
  24. Li, S.Q. (2023c), "Empirical vulnerability estimation models considering updating the structural earthquake damage database", Soil Dyn. Earthq. Eng., 169, 107864. https://doi.org/10.1016/j.soildyn.2023.107864.
  25. Li, S.Q. and Chen, Y.S. (2020), "Analysis of the probability matrix model for the seismic damage vulnerability of empirical structures", Nat. Hazard., 104(1), 705-730. https://doi.org/10.1007/s11069-020-04187-2.
  26. Li, S.Q. and Chen, Y.S. (2023), "Vulnerability and economic loss evaluation model of a typical group structure considering empirical field inspection data", Int. J. Disast. Risk Reduct., 88, 103617. https://doi.org/10.1016/j.ijdrr.2023.103617.
  27. Li, S.Q. and Gardoni, P. (2023), "Empirical seismic vulnerability models for building clusters considering hybrid intensity measures", J. Build. Eng., 68, 106130. https://doi.org/10.1016/j.jobe.2023.106130
  28. Li, S.Q. and Liu, H.B. (2022a), "Comparison of vulnerabilities in typical bridges using macroseismic intensity scales", Case Stud. Constr. Mater., 16, e01094. https://doi.org/10.1016/j.cscm.2022.e01094.
  29. Li, S.Q. and Liu, H.B. (2022b), "Analysis of probability matrix model for seismic damage vulnerability of highway bridges", Geomat., Nat. Hazard. Risk, 13(1), 1395-1421. https://doi.org/10.1080/19475705.2022.2077146.
  30. Li, S.Q. and Liu, H.B. (2022c), "Statistical and vulnerability prediction model considering empirical seismic damage to masonry structures", Struct., 39, 147-163. https://doi.org/10.1016/j.istruc.2022.03.024.
  31. Li, S.Q. and Liu, H.B. (2022d), "Vulnerability prediction model of typical structures considering empirical seismic damage observation data", Bull. Earthq. Eng., 20, 5161-5203. https://doi.org/10.1007/s10518-022-01395-y.
  32. Li, S.Q., Chen, Y.S. and Yu, T.L. (2021a), "Comparison of macroseismic intensity scales by considering empirical observations of structural seismic damage", Earthq. Spectra, 37(1), 449-485. https://doi.org/10.1177/8755293020944174.
  33. Li, S.Q., Chen, Y.S., Liu, H.B. and Del Gaudio, C. (2023a), "Empirical seismic vulnerability assessment model of typical urban buildings", Bull. Earthq. Eng., 21, 2217-2257. https://doi.org/10.1007/s10518-022-01585-8.
  34. Li, S.Q., Chen, Y.S., Liu, H.B. and Du, K. (2022b), "Empirical seismic fragility rapid prediction probability model of regional group reinforced concrete girder bridges", Earthq. Struct., 22(6), 609-623. https://doi.org/10.12989/eas.2022.22.6.609.
  35. Li, S.Q., Chen, Y.S., Liu, H.B., Du, K. and Chi, B. (2022a), "Assessment of seismic damage inspection and empirical vulnerability probability matrices for masonry structure", Earthq. Struct., 22(4), 387-399. https://doi.org/10.12989/eas.2022.22.4.387.
  36. Li, S.Q., Liu, H.B. and Chen, Y.S. (2021b), "Vulnerability models of brick and wood structures considering empirical seismic damage observations", Struct., 34, 2544-2565. https://doi.org/10.1016/j.istruc.2021.09.023.
  37. Lin, K.Q., Chen, Z.F., Li, Y. and Lu, X.Z. (2022), "Uncertainty analysis on progressive collapse of RC frame structures under dynamic column removal scenarios", J. Build. Eng., 46, 103811. https://doi.org/10.1016/j.jobe.2021.103811.
  38. Luo, H. and Paal, S.G. (2019), "A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments", Comput. Aid. Civil Infrastr. Eng., 34(11), 935-950. https://doi.org/10.1111/mice.12456.
  39. Luo, H. and Paal, S.G. (2022), "A data-free, support vector machine-based physics-driven estimator for dynamic response computation", Comput. Aid. Civil Infrastr. Eng., 38(1), 26-48. https://doi.org/10.1111/mice.12823.
  40. Luo, H. and Paal, S.G. (2022a), "Artificial intelligence-enhanced seismic response prediction of reinforced concrete frames", Adv. Eng. Inform., 52, 101568. https://doi.org/10.1016/j.aei.2022.101568.
  41. Luo, H. and Paal, S.G. (2022b), "Data-driven seismic response prediction of structural components", Earthq. Spectra, 38(2), 1382-1416. https://doi.org/10.1177/87552930211053345.
  42. Mangalathu, S. and Jeon, J.S. (2020), "Regional seismic risk assessment of infrastructure systems through machine learning: Active learning approach", J. Struct. Eng., 146(12), 04020269. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002831.
  43. Mangalathu, S., Heo, G. and Jeon, J.S. (2018), "Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes", Eng. Struct., 162, 166-176. https://doi.org/10.1016/j.engstruct.2018.01.053.
  44. Mangalathu, S., Sun, H., Nweke, C.C., Yi, Z. and Burton, H.V. (2020), "Classifying earthquake damage to buildings using machine learning", Earthq. Spectra, 36(1), 183-208. https://doi.org/10.1177/8755293019878137.
  45. Martins, L. and Silva, V. (2021), "Development of a fragility and vulnerability model for global seismic risk analyses", Bull. Earthq. Eng., 19(15), 6719-6745. https://doi.org/10.1007/s10518-020-00885-1.
  46. Masi, A., Lagomarsino, S., Dolce, M., Manfredi, V. and Ottonelli, D. (2021), "Towards the updated Italian seismic risk assessment: exposure and vulnerability modelling", Bull. Earthq. Eng., 19, 3253-3286. https://doi.org/10.1007/s10518-021-01065-5.
  47. Menichini, G., Nistri, V., Boschi, S., Monte, E.D., Orlando, M. Vignoli, A. (2022), "Calibration of vulnerability and fragility curves from moderate intensity Italian earthquake damage data", Int. J. Disast. Risk Reduct., 67, 102676. https://doi.org/10.1016/j.ijdrr.2021.102676.
  48. Murcia-Delso, J., Alcocer, S.M., Arnau, O., Martinez, Y. and Muria-Vila, D. (2020), "Seismic rehabilitation of concrete buildings after the 1985 and 2017 earthquakes in Mexico City", Earthq. Spectra, 36(S2), 175-198. https://doi.org/10.1177/8755293020957372.
  49. Pang, R., Xu, B., Zhou, Y. and Song, L.F. (2021), "Seismic time-history response and system reliability analysis of slopes considering uncertainty of multi-parameters and earthquake excitations", Comput. Geotech., 136, 104245. https://doi.org/10.1016/j.compgeo.2021.104245.
  50. Pavel, F., Vacareanu, R. and Scupin, A. (2020), "Seismic fragility assessment for post-1977 high-rise reinforced concrete structures in Romania", Bull. Earthq. Eng., 19, 6521-6544. https://doi.org/10.1007/s10518-020-01014-8.
  51. Radziminovich, Y.B., Gileva, N.A., Tubanov, T.A., Lukhneva, O.F., Novopashina, A.V. and Tcydypova, L.R. (2022), "The December 9, 2020, Mw 5.5 Kudara earthquake (Middle Baikal, Russia): internet questionnaire hard test and macroseismic data analysis", Bull. Earthq. Eng., 20, 1297-1324. https://doi.org/10.1007/s10518-021-01305-8.
  52. Samadian, D., Eghbali, M., Dehkordi, M.R. and Ghafory-Ashtiany, M. (2020), "Recovery and reconstruction of schools after M 7.3 Ezgeleh-Sarpole-Zahab earthquake of Nov. 2017; part I: Structural and nonstructural damages after the earthquake", Soil Dyn. Earthq. Eng., 139, 106305. https://doi.org/10.1016/j.soildyn.2020.106305.
  53. Sandoli, A., Calderoni, B., Lignola, G. P. and Prota, A. (2022), "Seismic vulnerability assessment of minor Italian urban centres: Development of urban fragility curves", Bull. Earthq. Eng., 20(10), 5017-5046. https://doi.org/10.1007/s10518-022-01385-0.
  54. Sarno, L.D. and Pugliese, F. (2021), "Effects of mainshock-aftershock sequences on fragility analysis of RC buildings with ageing", Eng. Struct., 232, 111837. https://doi.org/10.1016/j.engstruct.2020.111837.
  55. Sousa, R., Batalha, N., Silva, V. and Rodrigues, H. (2021), "Seismic fragility functions for Portuguese RC precast buildings", Bull. Earthq. Eng., 19, 6573-6590. https://doi.org/10.1007/s10518-020-01007-7.
  56. Tafti, M.F., Hosseini, K.A. and Mansouri, B. (2020), "Generation of new fragility curves for common types of buildings in Iran", Bull. Earthq. Eng., 18(7), 3079-3099. https://doi.org/10.1007/s10518-020-00811-5.
  57. Yurdakul, O., Duran, B., Tunaboyu, O. and Avsar, O. (2021), "Field reconnaissance on seismic performance of RC buildings after the January 24, 2020 Elazig-Sivrice earthquake", Nat. Hazard., 105(1), 859-887. https://doi.org/10.1007/s11069-020-04340-x.
  58. Zhang, Y., Ouyang, X., Sun, B., Shi, Y. and Wang, Z. (2022), "A comparative study on seismic fragility analysis of RC frame structures with consideration of modeling uncertainty under far‑field and near‑field ground motion excitation", Bull. Earthq. Eng., 20, 1455-1487. https://doi.org/10.1007/s10518-021-01254-2.