Abstract
To comprehend the empirical seismic vulnerability capability of various typical structures in towns and suburbs, this study considers, as the research background, the structural damage data observation from the Mw 7.1 Yushu earthquake that occurred on 2010 April 14th in China. The proposed research activity sorts out the actual investigation data (7156 buildings) of five types of typical structures in towns and villages and establishes the vulnerability and seismic fragility evaluation model of a group of buildings considering multidimensional parameters by using the Chinese macroseismic intensity standard. Using spatial modal, numerical calculation, and regression analysis methods, an updated lognormal distribution vulnerability and resilience assessment model based on multidimensional quantitative parameters is performed. Based on the principle of the conditional probability and damage quantification model, an innovative model (domain, point cloud, curve, surface, matrix, and function) is established for regional vulnerability and seismic resilience assessment of typical group buildings. A logarithmic relationship model considering both the updated Chinese macroseismic intensity and three-dimensional composite peak ground acceleration as intensity measures is proposed, and an empirical vulnerability assessment model for typical rural buildings is established. The traditional average earthquake loss index calculation model is updated, and an innovative regional domain vulnerability assessment model is conducted. Taking the samples of various typical structures surveyed in the Yushu earthquake, regional group structure vulnerability domain comparison models of various structures are performed. Ultimately, depending on the typical damage characteristics of structures, the actual damage features and mechanisms of various structures are analysed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ademovic N, Hadzima-Nyarko, Zagora N (2022) Influence of site effects on the seismic vulnerability of masonry and reinforced concrete buildings in Tuzla (Bosnia and Herzegovina). Bull Earthq Eng 20(5):2643–2681. https://doi.org/10.1007/s10518-022-01321-2
Ahmed B, Mangalathu S, Jeon JS (2022) Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks. J Build Eng 46:103737. https://doi.org/10.1016/j.jobe.2021.103737
Bessason B, Rupakhety R, Bjarnason JÖ (2022) Comparison and modelling of building losses in South Iceland caused by different size earthquakes. J Build Eng 46:103806. https://doi.org/10.1016/j.jobe.2021.103806
Bhasker R, Menon A (2022) A seismic fragility model accounting for torsional irregularity in low-rise non-ductile RC moment-resisting frames. Earthq Eng Struct Dyn 51(4):912–934. https://doi.org/10.1002/eqe.3597
Bilgin H, Shkodrani N, Hysenlliu M, Ozmen HB, Isik E, Harirchian E (2022) Damage and performance evaluation of masonry buildings constructed in 1970s during the 2019 Albania earthquakes. Eng Fail Anal 131:105824. https://doi.org/10.1016/j.engfailanal.2021.105824
Binici B, Yakut A, Canbay E, Akpinar U, Tuncay K (2022) Identifying buildings with high collapse risk based on samos earthquake damage inventory in İzmir. Bull Earthq Eng. https://doi.org/10.1007/s10518-021-01289-5
Boukri M, Farsi MN, Mebarki A (2022) Rapid earthquake loss estimation model for Algerian urban heritage: case of Blida city. Int J Archit Herit. https://doi.org/10.1080/15583058.2021.1958394
Chalabi M, Naderpour H, Mirrashid M (2022) Seismic resilience index for RC moment frames of school buildings using neuro-fuzzy approach. Nat Hazards. https://doi.org/10.1007/s11069-022-05377-w
Chatzidaki A, Vamvatsikos D (2021) Mixed probabilistic seismic demand models for fragility assessment. Bull Earthq Eng 19(15):6397–6421. https://doi.org/10.1007/s10518-021-01163-4
Chen W, Zhang L (2022) Building vulnerability assessment in seismic areas using ensemble learning: a Nepal case study. J Clean Prod 350:131418. https://doi.org/10.1016/j.jclepro.2022.131418
Chettri N, Gautam D, Rupakhety R (2021) Seismic vulnerability of vernacular residential buildings in Bhutan. J Earthq Eng. https://doi.org/10.1080/13632469.2020.1868362
Del Gaudio C, Di Martino G, Di Ludovico M, Manfredi G, Prota A, Ricci P, Verderame GM (2019) Empirical fragility curves for masonry buildings after the 2009 L’Aquila, Italy, earthquake. Bull Earthq Eng 17:6301–6330. https://doi.org/10.1007/s10518-019-00683-4
Di Ludovico M, Di Martino G, Prota A, Manfredi G, Dolce M (2022) Relationships between empirical damage and direct/indirect costs for the assessment of seismic loss scenarios. Bull Earthq Eng 20:229–254. https://doi.org/10.1007/s10518-021-01235-5
Formisano A, Florio G, Landolfo R, Mazzolani FM (2015) Numerical calibration of an easy method for seismic behaviour assessment on large scale of masonry building aggregates. Adv Eng Softw 80(C):116–138. https://doi.org/10.1016/j.advengsoft.2014.09.013
Formisano A, Chieffo N, Asteris PG, Lourenço (2023) Seismic risk scenario for the historical centre of castelpoto in Southern Italy. Earthq Eng Struct Dyn. https://doi.org/10.1002/eqe.3887
GB/T 17742 (2020) The Chinese seismic intensity scale. (in Chinese)
Gong MS, Lin SB, Sun JJ, Li SY, Dai JW, Xie LL (2015) Seismic intensity map and typical structural damage of 2010 Ms 7.1 Yushu earthquake in China. Nat Hazards 77:847–866. https://doi.org/10.1007/s11069-015-1631-z
He X, Cheng S, Chen Y (2022) Earthquake damage assessment model based on differential ratio of elastic-plastic dissipated energy. Bull Earthq Eng 20(5):2719–2749. https://doi.org/10.1007/s10518-022-01341-y
Karakas CC, Palanci M, Senel SM (2022) Fragility based evaluation of different code based assessment approaches for the performance estimation of existing buildings. Bull Earthq Eng 20:1685–1716. https://doi.org/10.1007/s10518-021-01292-w
Karic A, Atalić J, Kolbitsch A (2022) Seismic vulnerability of historic brick masonry buildings in Vienna. Bull Earthq Eng. https://doi.org/10.1007/s10518-022-01367-2
Lagomarsino S, Giovinazzi 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
Laguardia R, D’Amato M, Coltellacci M, Trocchio GD, Gigliotti R (2022) Fragility curves and economic loss assessment of RC buildings after L’Aquila 2009 earthquake. J Earthq Eng. https://doi.org/10.1080/13632469.2022.2038726
Li SQ (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
Li SQ (2023a) A simplified prediction model of structural seismic vulnerability considering a multivariate fuzzy membership algorithm. J Earthq Eng. https://doi.org/10.1080/13632469.2023.2217945
Li SQ (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
Li B, Cai Z (2022) Effectiveness of vector intensity measures in probabilistic seismic demand assessment. Soil Dyn Earthq Eng 155:107201. https://doi.org/10.1016/j.soildyn.2022.107201
Li SQ, Chen YS (2023) Vulnerability and economic loss evaluation model of a typical group structure considering empirical field inspection data. Int J Disaster Risk Reduct 88:103617. https://doi.org/10.1016/j.ijdrr.2023.103617
Li SQ, 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
Li SQ, Liu HB (2022a) Analysis of probability matrix model for seismic damage vulnerability of highway bridges. Geomat Nat Haz Risk 13(1):1395–1421. https://doi.org/10.1080/19475705.2022.2077146
Li SQ, Liu HB (2022b) Vulnerability models of brick and wood structures considering empirical seismic damage observations. Structures 39:147–163. https://doi.org/10.1016/j.istruc.2022.03.024
Li SQ, Liu HB (2022c) Vulnerability prediction model of typical structures considering empirical seismic damage observation data. Bull Earthq Eng. https://doi.org/10.1007/s10518-022-01395-y
Li SQ, Liu HB, Chen YS (2021) Vulnerability models of brick and wood structures considering empirical seismic damage observations. Structures 34:2544–2565. https://doi.org/10.1016/j.istruc.2021.09.023
Li SQ, Chen YS, Liu HB, Du K (2022) 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
Li SQ, Chen YS, Liu HB, Del Gaudio C (2023a) Empirical seismic vulnerability assessment model of typical urban buildings. Bull Earthq Eng. https://doi.org/10.1007/s10518-022-01585-8
Li SQ, Liu HB, Farsangi EN, Du K (2023b) Seismic fragility estimation considering field inspection of reinforced concrete girder bridges. Struct Infrastruct Eng. https://doi.org/10.1080/15732479.2023.2208565
Menichini G, Nistri V, Boschi S, Monte ED, Orlando M, Vignoli A (2022) Calibration of vulnerability and fragility curves from moderate intensity Italian earthquake damage data. Int J Disaster Risk Reduct 67:102676. https://doi.org/10.1016/j.ijdrr.2021.102676
Mousavi M, TohidiFar A, Alvanchi A (2022) BIM and machine learning in seismic damage prediction for non-structural exterior infill walls. Autom Constr 139:104288. https://doi.org/10.1016/j.autcon.2022.104288
Najafgholipour MA, Maheri MR, Khajepour M (2022) Performance of confined masonry buildings in November 2017, Sarpole Zahab earthquake (Mw = 7.3), Iran. Bull Earthq Eng. https://doi.org/10.1007/s10518-022-01364-5
Poulos A, Miranda E (2022) Probabilistic characterization of the directionality of horizontal earthquake response spectra. Earthq Eng Struct Dyn 51(9):2077–2090. https://doi.org/10.1002/eqe.3654
Qu Z, Dutu A, Zhong JR, Sun JJ (2015) Seismic damage of masonry infilled timber houses in the 2013 M7.0 Lushan earthquake in China. Earthq Spectra 31(3):1859–1874. https://doi.org/10.1193/012914EQS023T
Radziminovich YB, Gileva NA, Tubanov TA, Lukhneva OF, Novopashina AV, Tcydypova LR (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
Rossetto T, D’Ayala D, Foulser-Piggott R (2014) The value of multiple earthquake missions: the EEFIT L’Aquila earthquake experience. Bull Earthq Eng 12(1):277–305. https://doi.org/10.1007/s10518-014-9588-y
Rota M, Penna A, Strobbia CL (2008) Processing Italian damage data to derive typological fragility curves. Soil Dyn Earthq Eng 28:933–947. https://doi.org/10.1016/j.soildyn.2007.10.010
Sandoli A, Calderoni B, Lignola GP, Prota A (2022) Seismic vulnerability assessment of minor Italian urban centres: development of urban fragility curves. Bull Earthq Eng. https://doi.org/10.1007/s10518-022-01385-0
Sbrogiò L, Saretta Y, Valluzzi MR (2022) Empirical performance levels of strengthened masonry buildings struck by the 2016 central Italy earthquake: proposal of a new taxonomy. Int J Archit Herit. https://doi.org/10.1080/15583058.2021.2011474
Scala SA, Del Gaudio C, Verderame GM (2022) Influence of construction age on seismic vulnerability of masonry buildings damaged after 2009 L’Aquila earthquake. Soil Dyn Earthq Eng 157:107199. https://doi.org/10.1016/j.soildyn.2022.107199
Sheshov V, Apostolska R, Bozinovski Z, Vitanova M, Stojanoski B, Edip K, Bogdanovic A, Salic R, Jekic G, Zafirov T, Zlateski A (2021) Reconnaissance analysis on buildings damaged during Durres earthquake Mw6.4, 26 November 2019, Albania: effects to non-structural elements. Bull Earthq Eng 20(5):795–817. https://doi.org/10.1007/s10518-021-01271-1
Sun JJ, Li SY, Dai JW, Gong MS (2016) Earthquake damage of Mw 7.1 Yushu earthquake in Qinghai Province. Seismological Publishing House, Beijing
Sun BT, Spencer BF, Yan PL, Chen X, Zhang GX (2021) Analysis of the seismic vulnerability of buildings in the Lushan Ms7.0 earthquake in the Sichuan Province of China. J Earthq Eng. https://doi.org/10.1080/13632469.2019.1692742
Tarque N, Blondet M, Vargas-Neumann J, Yallico-Luque RY (2022) Rope mesh as a seismic reinforcement for two-storey adobe buildings. Bull Earthq Eng. https://doi.org/10.1007/s10518-022-01346-7
Wang H, Wang F, Yang H, Feng Y, Jeremic B (2022) Time domain probabilistic seismic risk analysis using ground motion prediction equations of Fourier amplitude spectra. Soil Dyn Earthq Eng 157:107218. https://doi.org/10.1016/j.soildyn.2022.107218
Yang T, Wei Y, Zhong J (2023) Potential bias of conventional structural seismic fragility for bridge structures under pulse-like ground motions: Bias evaluation and strategy improvement. Soil Dyn Earthq Eng 166:107787. https://doi.org/10.1016/j.soildyn.2023.107787
Yuan X, Chen G, Jiao P, Li L, Han J, Zhang H (2022) A neural network-based multivariate seismic classifier for simultaneous post-earthquake fragility estimation and damage classification. Eng Struct 255:113918. https://doi.org/10.1016/j.engstruct.2022.113918
Zapata JCG, Pittore M, Cotton F, Lilienkamp H, Shinde S, Aguirre P, María HS (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
Zhang Y, Ouyang X, Sun B, Shi Y, 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
Zhong J, Shi L, Yang T, Liu X, Wang Y (2022) Probabilistic seismic demand model of UBPRC columns conditioned on pulse-structure parameters. Eng Struct 270:114829. https://doi.org/10.1016/j.engstruct.2022.114829
Zucconi M, Di Ludovico M, Sorrentino L (2022) Census-based typological usability fragility curves for Italian unreinforced masonry buildings. Bull Earthq Eng. https://doi.org/10.1007/s10518-022-01361-8
Acknowledgements
All structural damage pictures and building sample data of this study were derived from the earthquake field observation database of the Institute of Engineering Mechanics of China Earthquake Administration and the Yushu earthquake damage reconnaissance team. I would like to express my sincere gratitude to them.
Funding
The research described in this paper was financially supported by the Basic Scientific Research Business Expenses of Provincial Universities in Heilongjiang Province (2022-KYYWF-1056) and a project funded by Heilongjiang Postdoctoral Science Foundation (LBH-Z22294), China.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest, ethics, or otherwise. Confirm informed consent.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Li, SQ., Formisano, A. Updated empirical vulnerability model considering the seismic damage of typical structures. Bull Earthquake Eng 22, 1147–1185 (2024). https://doi.org/10.1007/s10518-023-01814-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10518-023-01814-8