Vulnerability evaluation of urban buildings to various earthquake intensities: a case study of the municipal zone 9 of Tehran

Abstract The municipal zone 9 in Tehran is highly vulnerable to earthquake owing to possessing a highly weathered residential fabric, proximity to North Tehran Fault, and existing industrial usages. In order to take preventive measures and reduce the damages caused by earthquake, it sounds essential to designate the vulnerable areas and make the required arrangements. In this connection, the present study aims at avaluating the vulnerability of urban buildings in zone 9 of Tehran to various earthquake intensities. For this purpose, 10 effective factors on vulnerability of the aforesaid zone were employed under weighting and fuzzification against earthquake, adopting Analitical Network Process (ANP) - Fuzzy planning models. The selected layers in GIS environment were composed and finally, the generalized vulnerability mapping for the zone was prepared. To predict the damage caused to the urban buildings, the earthquake scenarios in the modified Mercalli intensities of 6, 7, and 8 were developed and implemented on the generalized vulnerability mapping of the zone. Ultimately, the vulnerability degrees of the buildings were grouped into five categories of very low, low, average, high, and very high based on the obtained results. The results of this research indicated that the vulnerability degrees of the urban buildings in the abovesaid range of were respectively 26, 56, 17, 1, and 0% for an earthquake with modified Mercalli intensity of 6, and 21, 10, 52, 16, and 1% for an earthquake with modified Mercalli intensity of 7, and 7, 4, 10, 61, and 18% for an earthquake with modified Mercalli intensity of 8. The results of this research are useful in understanding the capability of GIS spatial anlysis for vulnerability mapping.The information provided by these maps could help citizens, planners and engineers to reduce losses caused by existing and future erthquick by means of prevention, mitigation, and avoidance.

[1]  Piotr Jankowski,et al.  An optimized solution of multi-criteria evaluation analysis of landslide susceptibility using fuzzy sets and Kalman filter , 2010, Comput. Geosci..

[2]  Thomas Blaschke,et al.  An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping , 2014, Int. J. Geogr. Inf. Sci..

[3]  Dragos Toma-Danila,et al.  Vulnerability to Earthquake Hazard: Bucharest Case Study, Romania , 2017, International Journal of Disaster Risk Science.

[4]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[5]  Huicong Jia,et al.  Resilience to natural hazards: a geographic perspective , 2010 .

[6]  T. Blaschke,et al.  GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran , 2012, Natural Hazards.

[7]  G. Bocco,et al.  Predicting land-cover and land-use change in the urban fringe A case in Morelia city, Mexico , 2001 .

[8]  Thomas Blaschke,et al.  Multi-criteria risk evaluation by integrating an analytical network process approach into GIS-based sensitivity and uncertainty analyses , 2018 .

[9]  C. Kahraman,et al.  Multi‐criteria supplier selection using fuzzy AHP , 2003 .

[10]  Hadi Bahadori,et al.  Development of an integrated model for seismic vulnerability assessment of residential buildings: Application to Mahabad City, Iran , 2017 .

[11]  Jie Shan,et al.  A comprehensive review of earthquake-induced building damage detection with remote sensing techniques , 2013 .

[12]  Saeed Fallah Aliabadi,et al.  The social and physical vulnerability assessment of old texture against earthquake (case study: Fahadan district in Yazd City) , 2015, Arabian Journal of Geosciences.

[13]  R. Hassanzadeh,et al.  A GIS-based seismic hazard, building vulnerability and human loss assessment for the earthquake scenario in Tabriz , 2014 .

[14]  Thomas Blaschke,et al.  GIS-based ordered weighted averaging and Dempster–Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran , 2014, Int. J. Digit. Earth.

[15]  T. Blaschke,et al.  Uncertainty Analysis of GIS-based Ordered Weighted Averaging Method for Landslide Susceptibility Mapping in Urmia Lake Basin , Iran , 2012 .

[16]  Wenguo Weng,et al.  A scenario-based model for earthquake emergency management effectiveness evaluation , 2017 .

[17]  Hui Qian,et al.  Building a new and sustainable “Silk Road economic belt” , 2015, Environmental Earth Sciences.

[18]  Zeshui Xu,et al.  Emergency decision making for natural disasters: An overview , 2018 .

[19]  D. Lewis,et al.  Urban Vulnerability and Good Governance1 , 2005 .

[20]  Ali A. Nowroozi,et al.  Observed and probable intensity zoning of Iran , 1978 .

[21]  E. Quarantelli,et al.  Urban vulnerability to disasters in developing countries: managing risks , 2003 .

[22]  R. P. Mohanty,et al.  A fuzzy ANP-based approach to R&D project selection: A case study , 2005 .

[23]  Behzad Shokati,et al.  Sensitivity and uncertainty analysis of agro-ecological modeling for saffron plant cultivation using GIS spatial decision-making methods , 2019 .

[24]  Wanfang Zhou,et al.  Finding harmony between the environment and humanity: an introduction to the thematic issue of the Silk Road , 2017, Environmental Earth Sciences.

[25]  Sankar Kumar Nath,et al.  Seismic vulnerability and risk assessment of Kolkata City, India , 2014 .

[26]  Chunxiang Cao,et al.  Change detection of an earthquake-induced barrier lake based on remote sensing image classification , 2010 .

[27]  Peiyue Li,et al.  Environment: Accelerate research on land creation , 2014, Nature.

[28]  Natural-disaster shocks and government's behavior: Evidence from middle-income countries , 2018 .

[29]  Thomas Blaschke,et al.  An interval matrix method used to optimize the decision matrix in AHP technique for land subsidence susceptibility mapping , 2018, Environmental Earth Sciences.

[30]  Thomas Blaschke,et al.  A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping , 2014, Comput. Geosci..

[31]  Mattia De Amicis,et al.  A GIS-based approach to identify the spatial variability of social vulnerability to seismic hazard in Italy , 2016 .

[32]  B V Howard,et al.  Development of an integrated model for analysis of the kinetics of apolipoprotein B in plasma very low density lipoproteins, intermediate density lipoproteins, and low density lipoproteins. , 1985, The Journal of clinical investigation.

[33]  Brian E. Tucker,et al.  Some Remarks Concerning Worldwide Urban Earthquake Hazard and Earthquake Hazard Mitigation , 1994 .

[34]  Slobodan Zecevic,et al.  A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection , 2014, Expert Syst. Appl..

[35]  Ahmed Mebarki,et al.  Seismic vulnerability assessment at urban scale: Case of Algerian buildings , 2018, International Journal of Disaster Risk Reduction.

[36]  Ryuzo Ohno,et al.  Examination of vulnerability of various residential areas in China for earthquake disaster mitigation , 2012 .

[37]  Thomas L. Saaty,et al.  Decision making with dependence and feedback : the analytic network process : the organization and prioritization of complexity , 1996 .