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Research Article

Seismic vulnerability and risk assessment at the urban scale using support vector machine and GIScience technology: a case study of the Lixia District in Jinan City, China

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Article: 2173663 | Received 16 Nov 2022, Accepted 24 Jan 2023, Published online: 03 Mar 2023

References

  • Alam N, Alam MS, Tesfamariam S. 2012. Buildings’ seismic vulnerability assessment methods: a comparative study. Nat Hazards. 62(2):405–424.
  • Borfecchia F, De Cecco L, Pollino M, La Porta L, Lugari A, Martini S, Ristoratore E, Pascale C. 2010. Active and passive remote sensing for supporting the evaluation of the urban seismic vulnerability. ItJRS. 42:129–141.
  • Borzi B, Dell’Acqua F, Faravelli M, Gamba P, Lisini G, Onida M, Polli D. 2011. Vulnerability study on a large industrial area using satellite remotely sensed images. Bull Earthquake Eng. 9(2):675–690.
  • Change IPOCJGI. 2007. Climate change 2007: the physical science basis: summary for policymakers.104–116.
  • Chen C-S. 2020. Seismic performance assessments of school buildings in Taiwan using artificial intelligence theories. EC. 37(9):3321–3343.
  • Cockburn G, Tesfamariam S. 2012. Earthquake disaster risk index for Canadian cities using Bayesian belief networks. Georisk Assess Manag Risk Eng Syst Geohazards. 6(2):128–140.
  • Code PJBECfS. 2005. Eurocode 8: design of structures for earthquake resistance-part 1: general rules, seismic actions and rules for buildings.Brussels: European Committee for Standardization.
  • Cortes C, Vapnik V. 1995. Support-vector networks. Mach Learn. 20(3):273–297.
  • Costanzo A, Montuori A, Silva JP, Silvestri M, Musacchio M, Doumaz F, Stramondo S, Buongiorno M. 2016. The combined use of airborne remote sensing techniques within a GIS environment for the seismic vulnerability assessment of urban areas: an operational application. Remote Sens. 8(2):146.
  • de Felice G, Giannini R. 2010. An efficient approach for seismic fragility assessment with application to old reinforced concrete bridges. J Earthquake Eng. 14(2):231–251.
  • El Naqa I, Murphy MJ. 2015. What is machine learning?. In Machine learning in radiation oncology. Cham: Springer.
  • FEMA 310. 1998. Handbook for the seismic evaluation of buildings—A pre-standard. Washington, DC: Federal Emergency Management Agency.
  • French SP, Muthukumar S. 2006. Advanced technologies for earthquake risk. J Earthquake Eng. 10(2):207–236. inventories.10:207-236.
  • Frigerio I, Ventura S, Strigaro D, Mattavelli M, De Amicis M, Mugnano S, Boffi M. 2016. A GIS-based approach to identify the spatial variability of social vulnerability to seismic hazard in Italy. Appl Geogr. 74:12–22.
  • Geiß C, Aravena Pelizari P, Marconcini M, Sengara W, Edwards M, Lakes T, Taubenböck H. 2015. Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques. ISPRS J Photogramm Remote Sens. 104:175–188.
  • Geiß C, Priesmeier P, Aravena Pelizari P, Soto Calderon AR, Schoepfer E, Riedlinger T, Villar Vega M, Santa María H, Gómez Zapata JC, Pittore M, et al. 2022. Benefits of global earth observation missions for disaggregation of exposure data and earthquake loss modeling: evidence from Santiago de Chile. Nat Hazards.1–26.
  • Geiß C, Taubenböck HJNH. 2013. Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap. Nat Hazards. 68(1):7–48.
  • Geiß C, Taubenböck H, Tyagunov S, Tisch A, Post J, Lakes TJES. 2014. Assessment of seismic building vulnerability from space. Earthquake Spectra. 30(4):1553–1583.
  • Ghosh S, Roy A, Chakraborty S. 2018. Support vector regression based metamodeling for seismic reliability analysis of structures. Appl Math Modell. 64:584–602.
  • Grünthal G. 1998. European macroseismic scale 1998.
  • Guéguen P, Michel C, LeCorre L. 2007. A simplified approach for vulnerability assessment in moderate-to-low seismic hazard regions: application to Grenoble (France). Bull Earthquake Eng. 5(3):467–490.
  • Guettiche A, Guéguen P, Mimoune M. 2017. Seismic vulnerability assessment using association rule learning: application to the city of Constantine, Algeria. Nat Hazards. 86(3):1223–1245.
  • Hamdy O, Gaber H, Abdalzaher MS, Elhadidy M. 2022. Identifying exposure of urban area to certain seismic hazard using machine learning and GIS: a case study of greater Cairo. Sustainability. 14(17):10722.
  • Hidalgo PA, Ledezma CA, Jordan R. 2002. Seismic behavior of squat reinforced concrete shear walls. Earthquake Spectra. 18(2):287–308.
  • Hill M, Rossetto T. 2008. Comparison of building damage scales and damage descriptions for use in earthquake loss modelling in Europe. Bull Earthquake Eng. 6(2):335–365.
  • Huang X, Xia J, Xiao R, He T. 2019. Urban expansion patterns of 291 Chinese cities, 1990–2015. Int J Digital Earth. 12(1):62–77.
  • Huang J, Zhao D. 2006. High-resolution mantle tomography of China and surrounding regions. Solid Earth. 111(B9).
  • Jia K, Zhou S, Zhuang J, Jiang C. 2014. Possibility of the independence between the 2013 Lushan earthquake and the 2008 Wenchuan earthquake on Longmen Shan fault, Sichuan, China. Seismol Res Lett. 85(1):60–67.
  • Kaushik HB, Dasgupta K. 2013. Assessment of seismic vulnerability of structures in Sikkim, India, based on damage observation during two recent earthquakes. J Perform Constr Facil. 27(6):697–720.
  • Leggieri V, Ruggieri S, Zagari G, Uva G. 2021. Appraising seismic vulnerability of masonry aggregates through an automated mechanical-typological approach. Autom Constr. 132:103972.
  • Lestuzzi P, Podestà S, Luchini C, Garofano A, Kazantzidou-Firtinidou D, Bozzano C, Bischof P, Haffter A, Rouiller J-D. 2016. Seismic vulnerability assessment at urban scale for two typical Swiss cities using Risk-UE methodology. Nat Hazards. 84(1):249–269.
  • Li X, Li Z, Yang J, Li H, Liu Y, Fu B, Yang F. 2020. Seismic vulnerability comparison between rural Weinan and other rural areas in Western China. Int J Disaster Risk Reduct. 48:101576.
  • Li X, Li Z, Yang J, Liu Y, Fu B, Qi W, Fan XJNH. 2018. Spatiotemporal characteristics of earthquake disaster losses in China from 1993 to 2016. Nat Hazards. 94(2):843–865.
  • Lin X, Zhang H, Chen H, Chen H, Lin JJEE. 2015. Field investigation on severely damaged aseismic buildings in 2014 Ludian earthquake. Earthq Eng Eng Vib. 14(1):169–176.
  • Liu P, Liu X, Liu M, Shi Q, Yang J, Xu X, Zhang YJRS. 2019. Building footprint extraction from high-resolution images via spatial residual inception convolutional neural network. Remote Sens. 11(7):830.
  • Liu Y, Li Z, Wei B, Li X, Fu B. 2019. Seismic vulnerability assessment at urban scale using data mining and GIScience technology: application to Urumqi (China). Geomatics Nat Hazards Risk. 10(1):958–985.
  • Liu Y, So E, Li Z, Su G, Gross L, Li X, Qi W, Yang F, Fu B, Yalikun A, et al. 2020. Scenario-based seismic vulnerability and hazard analyses to help direct disaster risk reduction in rural Weinan, China. Int J Disaster Risk Reduct. 48:101577.
  • Liu Y, Zhang W, Chen X, Yu M, Sun Y, Meng F, Fan X. 2021. Landslide Detection of High-Resolution Satellite Images using Asymmetric Dual-Channel Network. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.
  • Maya M, Yu W. 2019. Short-term prediction of the earthquake through neural networks and meta-learning. Proceedings of the. 2019 16th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE); IEEE.
  • Mouroux P, Le Brun B. 2008. Risk-UE project: an advanced approach to earthquake risk scenarios with application to different European towns. In: Assessing and Managing Earthquake Risk. Springer; p. 479–508.
  • Mueller M, Segl K, Heiden U, Kaufmann H. 2006. Potential of high-resolution satellite data in the context of vulnerability of buildings. Nat Hazards. 38(1-2):247–258.
  • Nie G-z, Gao J-g, Ma Z-j 2002. On the risk of earthquake disaster in China in the coming 10 ∼ 15 years.11:68–73.
  • Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C. 2013. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci. 122(2):349–369.
  • Preciado A, Ramirez-Gaytan A, Santos JC, Rodriguez O. 2020. Seismic vulnerability assessment and reduction at a territorial scale on masonry and adobe housing by rapid vulnerability indicators: the case of Tlajomulco, Mexico. Int J Disaster Risk Reduct. 44:101425.
  • Rahman N, Ansary MA, Islam I. 2015. GIS based mapping of vulnerability to earthquake and fire hazard in Dhaka city, Bangladesh. Int J Disaster Risk Reduct. 13:291–300.
  • Ramírez Eudave R, Ferreira T. 2021. On the potential of using the Mexican National Catalogue of Historical Monuments for assessing the seismic vulnerability of existing buildings: a proof-of-concept study. Bull Earthquake Eng. 19(12):4945–4978.
  • Riedel I, Guéguen P, Dalla Mura M, Pathier E, Leduc T, Chanussot J. 2015. Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods. Nat Hazards. 76(2):1111–1141.
  • Riedel I, Gueguen P, Dunand F, Cottaz S. 2014. Macroscale vulnerability assessment of cities using association rule learning. Seismol Res Lett. 85(2):295–305.
  • Ródenas JL, García-Ayllón S, Tomás A. 2018. Estimation of the buildings seismic vulnerability: a methodological proposal for planning ante-earthquake scenarios in urban areas. Appl Sci. 8(7):1208.
  • Ruggieri S, Calò M, Cardellicchio A, Uva G. 2022. Analytical-mechanical based framework for seismic overall fragility analysis of existing RC buildings in town compartments. Bull Earthquake Eng. 20(15):8179–8216.
  • Ruggieri S, Cardellicchio A, Leggieri V, Uva G. 2021. Machine-learning based vulnerability analysis of existing buildings. Autom Constr. 132:103936.
  • Sichani ME, Padgett JE, Bisadi V. 2018. Probabilistic seismic analysis of concrete dry cask structures. Struct Saf. 73:87–98.
  • Silva V, Akkar S, Baker J, Bazzurro P, Castro JM, Crowley H, Dolsek M, Galasso C, Lagomarsino S, Monteiro R, et al. 2019. Current challenges and future trends in analytical fragility and vulnerability modeling. Earthquake Spectra. 35(4):1927–1952.
  • Sun H, Burton HV, Huang H. 2021. Machine learning applications for building structural design and performance assessment: state-of-the-art review. J Build Eng. 33:101816.
  • Tesfamariam S, Liu Z. 2010. Earthquake induced damage classification for reinforced concrete buildings. Struct Saf. 32(2):154–164.
  • Tesfamariam S, Saatcioglu MJES. 2010. Seismic vulnerability assessment of reinforced concrete buildings using hierarchical fuzzy rule base modeling. Earthquake Spectra. 26(1):235–256.
  • Thomalla F, Downing T, Spanger-Siegfried E, Han G, Rockström JJD. 2006. Reducing hazard vulnerability: towards a common approach between disaster risk reduction and climate adaptation. Disasters. 30(1):39–48.
  • Wang Q, Zhang PZ, Freymueller JT, Bilham R, Larson KM, Lai X, You X, Niu Z, Wu J, Li Y, et al. 2001. Present-day crustal deformation in China constrained by global positioning system measurements. Science. 294(5542):574–577.
  • Wu F, Wang H-T, Li G, Jia J-Q, Li H-N. 2017. Seismic performance of traditional adobe masonry walls subjected to in-plane cyclic loading. Mater Struct. 50(1):14.
  • Xie Y, Ebad Sichani M, Padgett JE, DesRoches R. 2020. The promise of implementing machine learning in earthquake engineering: a state-of-the-art review. Earthquake Spectra. 36(4):1769–1801.
  • Xu X, Wen X, Han Z, Chen G, Li C, Zheng W, Zhnag S, Ren Z, Xu C, Tan X, et al. 2013. Lushan M S7. 0 earthquake: a blind reserve-fault event. Chin Sci Bull. 58(28-29):3437–3443.
  • Xue J, Xu D, Qi L. 2019. Experimental seismic response of a column-and-tie wooden structure. Adv Struct Eng. 22(8):1909–1922.
  • Yi W-J, He Q-F, Xiao Y, Kunnath S. 2008. Experimental study on progressive collapse-resistant behavior of reinforced concrete frame structures. ACI Struct J. 105(4):433.
  • Zhai W, Huang CJE. 2016. Fast building damage mapping using a single post-earthquake PolSAR image: a case study of the 2010 Yushu earthquake. Earth Planet Sp. 68:1–12.