Abstract
Seismic vulnerability assessment of urban buildings is among the most crucial procedures to post-disaster response and recovery of infrastructure systems. The present study proceeds to estimate the seismic vulnerability of urban buildings and proposes a new framework training on the two objectives. First, a comprehensive interpretation of the effective parameters of this phenomenon including physical and human factors is done. Second, the Rough Set theory is used to reduce the integration uncertainties, as there are numerous quantitative and qualitative data. Both objectives were conducted on seven distinct earthquake scenarios with different intensities based on distance from the fault line and the epicenter. The proposed method was implemented by measuring seismic vulnerability for the seven specified seismic scenarios. The final results indicated that among the entire studied buildings, 71.5% were highly vulnerable as concerning the highest earthquake scenario (intensity=7MM and acceleration calculated based on the epicenter), while in the lowest earthquake scenario (intensity=5MM), the percentage of vulnerable buildings decreased to approximately 57%. Also, the findings proved that the distance from the fault line rather than the earthquake center (epicenter) has a significant effect on the seismic vulnerability of urban buildings. The model was evaluated by comparing the results with the weighted linear combination (WLC) method. The accuracy of the proposed model was substantiated according to evaluation reports. Vulnerability assessment based on the distance from the epicenter and its comparison with the distance from the fault shows significant reliable results.
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Alam MS, Haque SM (2018) Assessment of urban physical seismic vulnerability using the combination of AHP and TOPSIS models: A case study of residential neighborhoods of Mymensingh city, Bangladesh. J Geosci Environ Prot 6(02): 165–183. https://doi.org/10.4236/gep.2018.62011
Alinia HS, Delavar MR (2011) Tehran’s seismic vulnerability classification using granular computing approach. Appl Geomat 3(4): 229–240. https://doi.org/10.1007/s12518-011-0068-7
Asadi Y, Samany NN, Ezimand K (2019) Seismic vulnerability assessment of urban buildings and traffic networks using a fuzzy ordered weighted average. J Mt Sci 16(3): 677–688. https://doi.org/10.1007/s11629-017-4802-4
Boloorani AD, Shorabeh SN, Samany NN, et al. (2021) Vulnerability mapping and risk analysis of sand and dust storms in Ahvaz, IRAN. Environ Pollut (279): 116859. https://doi.org/10.1016/j.envpol.2021.116859.
Boukri M, Farsi MN, Mebarki A, et al. (2018) Seismic vulnerability assessment at urban scale: Case of Algerian buildings. Int J Disaster Risk Reduct (31):555–575. https://doi.org/10.1016/j.ijdrr.2018.06.014
Delavar MR, Bahrami M, Zare M (2017) Physical seismic vulnerability assessment of Tehran using the integration of granular computing and interval dempster- Shafer. Int Arch Photogramm Remote Sens Spatial Inf Sci 18–22 https://doi.org/10.5194/isprs-archives-XLII-2-W7-469-2017
Delavar MR, Sadrykia M (2020) Assessment of Enhanced Dempster-Shafer Theory for Uncertainty Modeling in a GIS-Based Seismic Vulnerability Assessment Model, Case Study—Tabriz City. Int J Geo-Inf 9(195). https://doi.org/10.3390/ijgi9040195
Desalegn H, Mulu A (2020) Flood vulnerability assessment using GIS at Fetam watershed upper Abbay basin Ethiopia. Heliyon 7(1) e05865. https://doi.org/10.1016/j.heliyon.2020.e05865
Esfandiari F, Ghafari A, Lotfi KH (2013) Modeling the vulnerability of cities to earthquakes using Topsis method GIS environment. J Amst (2):43–79. http://www.geomorphologyjournal.ir/article_77909.html?lang=fa
Ebrahimi M, Salmani M, Amir Ahmadi A, Nouri M (2015) Evaluation of seismic vulnerability of Bardaskan city against earthquake using inverted hierarchical model (IHPW). Environmental Hazards 6:105–137 https://journals.usb.ac.ir/article_2526.html (Persian)
Goda K, Hong HP (2008). Spatial correlation of peak ground motions and response spectra. Bull Seismol Soc Amer 98(1): 354–365. https://doi.org/10.1785/0120070078
Gueguen P (2013) Seismic vulnerability of structures. John Wiley & Sons. pp 1–368. https://www.wiley.com/en-aw/Seismic+Vulnerability+of+Structures-p-9781118603925
Guettiche A, Mimoune M (2014) Analysis of seismic vulnerability-case of buildings of high seismic hazard. J Mater Res (875):416–422. https://doi.org/10.4028/www.scientific.net/AMR.875-877.416
Guettiche A, Guéguen P, Mimoune M (2017) Seismic vulnerability assessment using association rule learning: application to the city of Constantine, Algeria. Environ Hazard 4 86(3):1223–1245. https://doi.org/10.1007/s11069-016-2739-5
Hosseini A (2011) Passive defense criteria for designing urban collective buildings. Armanshahr Archit & Urbab Develop (2): 271–282. https://doi.org/10.1007/978-3-540-87395-2_17.
Jelokhani-Niaraki M, Neysani Samany N, Mohammadi M (2020) A hybrid ridesharing algorithm based on GIS and ant colony optimization through geosocial networks. J Ambient Intell Hum Comput (12): 2387–2407. https://doi.org/10.1007/s12652-020-02364-6.
JICA (Japan international cooperation agency) (2000) The study on Seismic micro zoning of the greater Tehran area in the Islamic Republic of Iran (Final Report). http://open_jicareport.jica.go.jp
Karapetrou S, Manakou M, Bindi D, et al. (2016) Time-building specific seismic vulnerability assessment of a hospital RC building using field monitoring data. Eng Struct (112): 114–132. https://doi.org/10.1016/j.engstruct.2016.01.009
Karapetrou ST, Fotopoulou SD, Pitilakis KD (2017) Seismic vulnerability of RC buildings under the effect of aging. Procedia Environ Sci (38): 461–468. https://doi.org/10.1016/j.proenv.2017.03.137
Karimzadeh S, Miyajima M, Hassanzadeh R, et al. (2014) A GIS-based seismic hazard, building vulnerability and human loss assessment for the earthquake scenario in Tabriz. Soil Dyn Earthq Eng (66): 263–280. https://doi.org/10.1016/j.soildyn.2014.06.026.
Khanlari G (1996) Geological engineering. First Edition. Hamedan, University of Abu Ali Sina. J Eng Geol. https://b2n.ir/z75457
Marasco S, Zamani Noori A, Domaneschi Gian M, et al. (2021) Seismic vulnerability assessment indices for buildings: Proposals, comparisons and methodologies at collapse limit states. Int J Disaster Risk Reduct (102466). https://doi.org/10.1016/j.ijdrr.2021.102466
Mazumder RK, Salman, A M (2019). Seismic damage assessment using RADIUS and GIS: A case study of Sylhet City, Bangladesh. Int J Disaster Risk Reduct (34):243–254. https://doi.org/10.1016/j.ijdrr.2018.11.023
Mesbahi F, Akbari Baghi M, Nadiri A (2020) Assessment and mapping of the seismic vulnerability of Tabriz city using the Fuzzy logic. J Adv Environ Health Res (8):181–192. https://doi.org/10.22102/jaehr.2020.242457.1179
Mouroux P, Le Brun B (2008) Risk-Ue Project: an advanced approach to earthquake risk scenarios with application to different European towns. Springer. pp 479–508. https://doi.org/10.1007/978-1-4020-3608-8_23
Nadizadeh Shorabeh S, Varnaseri A, Firozjaei MK, et al. (2020). Spatial modeling of areas suitable for public libraries construction by integration of GIS and multi-attribute decision making: Case study Tehran, Iran. Libr Inf Sci Res 42(2) 101017. https://doi.org/10.1016/j.lisr.2020.101017.
Naghdizadegan Jahromi M, Gomeh Z, et al. (2021) Developing a SINTACS-based method to map groundwater multi-pollutant vulnerability using evolutionary algorithms. Environ Sci Pollut Res (28): 7854–7869. https://doi.org/10.1007/s11356-020-11089-0
Neisany Samany N, Delavar MR, Saeedi S, et al. (2009) 3D continuous K-NN query for a landmark-based wayfinding location-based service. J 3D GIS 271–282.
Neves F, Costa A, Vicente R, et al. (2012) Seismic vulnerability assessment and characterization of the buildings on Faial Island, Azores. Bull Earthq Eng 10(1):27–44. https://doi.org/10.1007/s10518-011-9276-0
Neysani Samany N (2019) Automatic landmark extraction from geotagged social media photos using deep neural network. Cities (93):1–12. https://doi.org/10.1016/j.cities.2019.04.012.
Neysani Samany N, Toomanian A, Maher A, et al. (2021) The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city. Land Use Policy (109): 1–14. https://doi.org/10.1016/j.landusepol.2021.105725
Neysani Samany N, Delavar MR, Chrisman N, Malek MR (2014) FIA5: a customized Fuzzy Interval Algebra for modeling spatial relevancy in urban context-aware systems. Eng Appl Artif Intell (33):116–126. https://doi.org/10.1016/j.engappai.2014.04.004
Omidipoor M, Toomanian A, Neysani Samany N, Mansourian A (2021) Knowledge Discovery Web Service for Spatial Data Infrastructures. Int J Geo-Inf 10 12. https://doi.org/10.3390/ijgi10010012.
Yariyan P, Avand M, Soltani F, et al. (2020) Earthquake Vulnerability Mapping Using Different Hybrid Models. Symmetry 12(3): 405. https://doi.org/10.3390/sym12030405
Qaed Rahmati S, Bastanifar A, Soltani L (2011) Investigation of the effects of condensation on earthquake vulnerability in Isfahan (with fuzzy approach) (1): 107–122. https://journals.ui.ac.ir/article_18488.html
Qureshi S, Nadizadeh Shorabeh S, Neysani Samany N (2021) a new integrated approach for municipal landfill siting based on urban physical growth prediction: a case study mashhad metropolis in Iran. Remote Sens 13(5):949–964. https://doi.org/10.3390/rs13050949.
Rezaie F, Panahi M (2015). GIS modeling of seismic vulnerability of residential fabrics considering geotechnical, structural, social, and physical distance indicators in Tehran using multi-criteria decision-making techniques. Nat Hazards Earth Syst Sci 15(3): 461–474. https://doi.org/10.5194/nhess-15-461-2015
Rustaie S (2007) zoning of Tabriz Fault Risk for Different Uses of Urban Land. J Geo- Dev 9(2).
Rutkowski L (2008) Computational intelligence: methods and techniques. J SSBM. https://www.springer.com.book
Samanta A, Swain A (2019) Seismic response and vulnerability assessment of representative low, medium, and high-rise buildings in Patna, India. Structures (19): 110–127. https://doi.org/10.1016/j.istruc.2019.01.002
Sarvar H, Amini J, Laleh-Poor M (2011) Assessment of risk caused by the earthquake in region 1 of Tehran using the combination of RADIUS, TOPSIS, and AHP models. J Civil Eng Urban 1(1): 39–48. https://www.ojceu.com
Sheikhian H, Delavar MR, Stein A (2015) Integrated estimation of seismic physical vulnerability of Tehran using rule-based granular computing. Int Arch Photogramm Remote Sens Spatial Inf Sci 40(3):187. https://doi.org/10.5194/isprsarchives-XL-3-W3-187-2015
Shih DCF (2017) Groundwater storage inferred from earthquake activities around East Asia and the West Pacific Ocean. J Hydrol (544): 363–372. https://doi.org/10.1016/j.jhydrol.2016.11.029
Silavi T (2006) Evaluation of Seismic Vulnerability in Tehran City by Using Fuzzy Intuition Models. J MS Tehran University.
Sotoudeh B (2000) Land use planning and correction of roads to secure earthquakes, (Case Study: Garden District Ferdows County Municipality of Tehran). Graduate degree urban planning urban and regional planning Shiraz University. https://www.sid.ir
Yariyan P, Abbaspour RA, Chehreghan A, et al. (2021) GIS-based seismic vulnerability mapping: a comparison of artificial neural networks hybrid models. Geocarto Int. https://doi.org/10.1080/10106049.2021.1892208
Yariyan P, Zabihi H, Wolf I, et al. (2020) Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran. Int J Disaster Risk Reduct (101705):1–59. https://doi.org/10.1016/j.ijdrr.2020.101705
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The National Cartographic Center and Geological Survey of Iran are most appreciation for data preparation. The authors acknowledge with appreciation their persistent care, so vital to the accomplishment of this research.
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Asadi, Y., Neysani Samany, N., Kiavarz Moqadam, M. et al. Seismic vulnerability assessment of urban buildings using the rough set theory and weighted linear combination. J. Mt. Sci. 19, 849–861 (2022). https://doi.org/10.1007/s11629-021-6724-4
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DOI: https://doi.org/10.1007/s11629-021-6724-4