ارزیابی آسیب‌پذیری بلوک‌های ساختمانی شهر کرمانشاه در برابر زلزله و مکان‌یابی محل اسکان موقت جمعیت آسیب‌پذیر

نوع مقاله : مقاله کامل

نویسندگان

گروه مهندسی نقشه‌برداری، دانشکده مهندسی عمران، دانشگاه تربیت دبیر شهید رجایی، تهران، ایران

10.22059/jphgr.2024.368595.1007799

چکیده

یکی از خطراتی که سکونتگاه‌ها و مخصوصاً شهرها را تهدید می‌کند، وقوع زلزله است. در این پژوهش ابتدا نقشه آسیب‌پذیری بلوک‌های ساختمانی کلان‌شهر کرمانشاه تهیه و تعداد افراد آسیب‌پذیر در زمان وقوع زلزله برآورد شده و سپس سایت‌های مناسب برای اسکان جمعیت آسیب‌پذیر شناسایی گردیده است. برای اجرای بخش اول با استفاده از لایه‌های اطلاعاتی گسل‌ها، عمر سازه، استحکام بلوک‌های ساختمانی و مساحت زیربنای بلوک‌ها و بهره‌گیری از یک سیستم استنتاج فازی نقشه آسیب‌پذیری آماده‌شده و با حد آستانه گذاری بر روی نقشه، بلوک‌های با ریسک بالا شناسایی و مجموع جمعیت ساکن در آن بلوک‌ها به‌دست‌آمده است. برای بخش دوم این پژوهش نیز از لایه‌های اطلاعاتی فاصله از رودخانه‌ها، گسل‌ها، خطوط برق فشارقوی، پست برق، جایگاه‌های سوخت، راه‌ها، بیمارستان‌ها، ایستگاه‌های آتش‌نشانی و پلیس استفاده‌شده است. سپس نقشه‌های تولیدشده فازی سازی شده و وزن معیارها با فرآیند تحلیل سلسه‌مراتبی فازی (FAHP) برآورد گردیده است. پس‌ازآن با اعمال وزن هر معیار به نقشه مربوط به آن معیار و ادغام نقشه‌ها، نقشه‌های شایستگی مکانی برای اسکان جمعیت آسیب‌پذیر در زلزله به‌دست‌آمده است. در نهایت بهترین سایت‌های پیشنهادی داخل شهری و برون‌شهری برای اسکان مردم در زمان وقوع زلزله انتخاب‌شده است. علاوه بر آن، مکان‌های اسکان داخل سایت‌های پیشنهادی و ظرفیت آن‌ها برای اسکان موردبررسی قرار گرفت. بررسی میدانی نشان داد که سایت‌های انتخاب‌شده دارای شرایط موردنظر برای اسکان در زمان بحران زلزله می‌باشند

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Assessing the Vulnerability of Kermanshah Building Blocks in Earthquake and Site Selection for the Temporary Housing of the Vulnerable Population

نویسندگان [English]

  • Ali Veysi
  • Farhad Hosseinali
Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
چکیده [English]

ABSTRACT
The risk of natural hazards, particularly earthquakes, is a highly significant concern in urban environments. In this research, the initial phase entails estimating the vulnerability map of the building blocks in Kermanshah metropolis, as well as determining the number of individuals at risk during an earthquake. Subsequently, the research identifies and introduces safe zones or suitable locations for accommodating the vulnerable population during such seismic events. For the first part, the vulnerability map was obtained using maps of distance from faults, the structural integrity of the buildings, the strength of the building blocks, and the size of the infrastructure. A fuzzy inference system was utilized. Thus, the high-risk blocks were determined, and their population was estimated. In the second part of the research, distance maps derived from rivers, faults, high-voltage power lines, electric substations, fuel stations, roads, hospitals, fire stations, and police stations were used. These generated maps have then undergone fuzzification, and the weight of the criteria employed has been assessed using the fuzzy hierarchical analysis process (FAHP). Subsequently, by applying the weight associated with each criterion to the corresponding map and merging the resultant maps, maps depicting the suitability for temporal settlement of the vulnerable population during an earthquake were obtained. Finally, the most optimal inner-city and outer-city sites for individuals' lodging amid an earthquake have been chosen. Moreover, certain locations inside the suggested sites and their capacity were assessed. The field research revealed that the selected sites involve the expected conditions for settlement after the earthquake.
Extended Abstract
Introduction
The risk of natural hazards, particularly earthquakes, is a significant concern in urban environments. This issue has garnered increased attention in light of recent seismic activity in Iran. Within the framework of this study, the initial phase entails estimating the vulnerability map of the building blocks in Kermanshah metropolis, as well as determining the number of individuals at risk during an earthquake. Subsequently, the research identifies and introduces safe zones or suitable locations for accommodating the vulnerable population during such seismic events. Various factors have been taken into account to construct the vulnerability map of the building blocks, including the distance from fault lines, the structural integrity of the buildings, the building blocks' strength, and the infrastructure's size. Additionally, population data for Kermanshah city has been utilized to estimate the vulnerable population. This study used a fuzzy inference system with 81 rules to estimate earthquake vulnerability. Blocks at high risk have been identified through thresholding on the vulnerability map, and the total population residing within those blocks has been acquired. The subsequent segment of this investigation involves the utilization of distance maps derived from rivers, faults, high-voltage power lines, electric substations, fuel stations, roads, hospitals, fire stations, and police stations. These generated maps have then undergone fuzzification, and the weight of the criteria employed has been assessed using the fuzzy hierarchical analysis process (FAHP). Subsequently, by applying the weight associated with each criterion to the corresponding map and merging the resultant maps, maps depicting the suitability of certain locations for housing the vulnerable population during an earthquake have been obtained. Finally, by examining the map indicating the appropriateness of locations, the most optimal inner-city and outer-city sites for individuals' lodging amid an earthquake have been chosen. The outcomes of the initial phase of this investigation demonstrate that no less than 65,110 individuals will sustain injuries in an earthquake with a moderate level of intensity. The outcomes of the subsequent phase likewise suggest that a region close to the central area of the city's western boundary is apt for accommodating the vulnerable populace in an earthquake.
 
Methodology
In this research, the shape files of faults, rivers, urban roads, high voltage power lines, hospitals, police stations, fire stations, fuel stations, building blocks, and urban places such as the location of schools and educational centers, rescue and rescue centers, clinics and clinics, shelters, Basij offices and bases, etc. of Kermanshah city were used. The vulnerability criteria were first fuzzified to prepare the vulnerability map of building blocks. Then, using MATLAB software, a fuzzy inference system was designed based on the Mamdani inference system with 81 fuzzy rules, and by defining the input and output membership functions, the vulnerability map of building blocks due to earthquakes was obtained. Finally, the number of vulnerable populations was estimated by combining this map with the population density map. Also, in order to prepare the map of accommodation sites, all the maps related to the location were fuzzy using ArcMap software, then a weight was assigned to each of the maps, and finally, by overlapping the maps, the suitable places for the construction of the sites were determined.
 
Results and discussion
An inner-city and an outer-city site were identified to accommodate the earthquake victims. There are 151 places available in the proposed inner-city accommodation site during the earthquake that can be used. These places include schools and colleges, green spaces, parks and gardens, hospitals, clinics and medical centers, administrative and organizational centers, and service centers such as parking lots, terminals, etc. It was found that the total area of habitable places in the inner-city site is over 978 thousand square meters. If the estimated population of vulnerable people in moderate and severe earthquakes are 65,110 and 448,282 people, respectively, a space of 15.02 square meters and 2.18 square meters can be allocated for each vulnerable person. In other words, if an earthquake with moderate strength occurs during the day, the density of vulnerable people in the proposed sites inside the city will be approximately 7 people per 100 square meters. Also, suppose a strong earthquake occurs at night; in that case, the density of vulnerable people in the proposed sites inside the city will be approximately 46 people per 100 square meters, which is a high value. In case of a strong earthquake affecting many people, it is advisable to consider using the proposed out-of-town site, which is located near the inner-city site, simultaneously. This will help accommodate a larger number of people and ensure their safety.
 
Conclusion
In this research, a method based on the fuzzy inference system for estimating the vulnerability of building blocks during an earthquake and estimating the vulnerable population, as well as locating safe points for housing the vulnerable population of Kermanshah metropolis, was introduced and described. The output results of the vulnerability map of building blocks show that the middle and western building blocks are in better conditions than other building blocks due to their distance from the northern and southern faults and the high strength of their structures, and they are less damaged in case of an earthquake. Considering the vulnerable population during an earthquake, securing and renovating blocks that do not have sufficient strength against earthquakes is necessary. Regarding safe places for earthquake victims, the factors of distance from faults and proximity or access to the hospital have the highest degrees of importance, according to experts in crisis management and the Red Crescent. In this regard, construction near existing faults should be strictly avoided. Also, the location suitability map produced shows that the area close to the middle of the western border of Kermanshah city is suitable for housing the vulnerable population during an earthquake, and this area is suggested as an urban site during an earthquake. In case of an increase in the affected population, the injured or other displaced people should be accommodated in the out-of-town site by establishing a field hospital.
 
Funding
This work was supported by Shahid Rajaee Teacher Training University under grant number 4939
 
Authors’ Contribution
All of the authors approved the content of the manuscript and agreed on all aspects of the work.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to Kermanshah Management and Planning Organization for providing the data for this research.

کلیدواژه‌ها [English]

  • Fuzzy Analytic Hierarchical Process (FAHP)
  • Fuzzy Inference System (FIS)
  • Site Selection
  • Temporary Housing
  • Earthquake
  1. ذرکیش، محسن؛ حافظ رضازاده، معصومه؛ و میری، غلامرضا. (1396). کاربرد روش تحلیل سلسله مراتبی (AHP) و سامانه اطلاعات جغرافیایی (GIS) در مکان‌یابی محل های اسکان موقت پس از وقوع حوادث طبیعی (مطالعه موردی: منطقه دو شهرداری زاهدان). فضای جغرافیایی، 17(58)، 169-189.
  2. بوزرجمهری، خدیجه؛ جوانی, خدیجه و کاتبی، مجیدرضا. (1394). مکان‌یابی بهینه پایگاه اسکان موقّت در مدیریت بحران نواحی روستایی (نمونه موردمطالعه: بخش مرکزی شهرستان فاروج).  جغرافیا و مخاطرات محیطی، 4(4)، 1-20. doi: 10.22067/GEO.V4I4.42299
  3. حسن‌پور کازرونی، ناصر؛ علویان، سیدمحمدحسین؛ طهماسبی‌زاده، فرشاد. (1399). تحلیل تناسب و اولویت‌بندی بناهای عمومی و دولتی برای مرکز اسکان جمعی در شرایط بحران زلزله با استفاده از سیستم اطلاعات جغرافیایی، مطالعه موردی منطقه ۵ شهر تهران. دانش پیشگیری و مدیریت بحران. ۱۰ (۱)، 91-103. doi: ‎20.1001.1.23225955.1399.10.1.7.3
  4. حسنی، نعمت؛ رفیعی انزاب، نعیمه و شاداب فر، مهدی. (1391). بهسازی لرزه‌های مدارس و مقایسه روش‌های به‌کاررفته با استفاده از تحلیل استاتیکی غیرخطی پوش‌آور. دومین کنفرانس ملی مدیریت بحران، تهران.
  5. رشیدی ابراهیم‌حصاری، اصغر؛ عطار، محمدامین؛ گیوه‌چی، سعید؛ نصبی، نسترن. (1392). مکان‌یابی اسکان موقت پس از زلزله با استفاده از GIS و تکنیک AHP مطالعه موردی: منطقه شش شهر شیراز. مطالعات و پژوهش‌های شهری و منطقه‌ای، 5(17)، 101-118.
  6. پیام راد، داوود و وفایی‌نژاد، علیرضا. (1394). کمک به مدیریت بحران زلزله با مکان‌یابی مراکز اسکان موقت با استفاده از یک سیستم حامی تصمیم‌گیری GIS مبنا (مطالعه موردی: منطقه 8 شهرداری اصفهان). علوم و فنون نقشه‌برداری، 5(2)، 231-246.
  7. رنگزن، کاظم؛ کابلی‌زاده، مصطفی؛ کریمی، دانیال و منصورنعیمی، ابراهیم. (1395). پهنه‌بندی خطرپذیری زلزله و مکان‌یابی مناطق امن در زمان مخاطرات طبیعی با استفاده الگوریتم‌های هوش مصنوعی و GIS (مطالعه موردی: منطقه یک شهرداری کلان‌شهر اهواز). جغرافیا و برنامه‌ریزی محیطی، 27(3)، 49-66. doi: 10.22108/GEP.2017.97958
  8. فرقانی، محمدعلی و دربندی، سمانه. (1394). ارزیابی عوامل مؤثر در انتخاب مکان‌های اسکان موقت پس از زلزله با استفاده از GIS و تکنیک AHP (مطالعه موردی: منطقه 4 کرمان). امداد و نجات، 7(2)، 54-80.
  9. قدرتی امیری، غلامرضا؛ اثمری سعدآباد، سهیل و زارع حسین‌زاده، علی. (1392). تحلیل ریسک زلزله با استفاده از سیستم استنتاج‌گر فازی و کاربرد آن در مطالعات بهسازی لرزه‌ای ساختمان‌های موجود. مهندسی عمران مدرس، 13(۴)، 84-71.
  10. قنبری، ابوالفضل؛ سالکی ملکی، محمدعلی و قاسمی، معصومه. (1392). مکان‌یابی بهینه پایگاه‌های اسکان موقت زلزله‌زدگان با رویکرد فازی (مطالعه موردی: شهر تبریز). امداد و نجات، 5(2)، 52-69.
  11. نارویی، خدیجه و آقائی‌زاده، اسماعیل. (1396). مکان‌یابی سایت اسکان موقت در برابر زلزله در شهرها (مطالعه موردی: شهر زاهدان). جغرافیا و توسعه فضای شهری، 4(1)، 155-173. doi:  10.22067/gusd.v4i1.54032
  12. نوجوان، مهدی؛ امیدوار، بابک؛ و صالحی، اسماعیل. (1392). مکان‌یابی اسکان موقت با استفاده از الگوریتم‌های فازی؛ مطالعه موردی: منطقه یک شهرداری تهران. مدیریت شهری، 11(31)، 205-221.
  13. Allali, S.A., Abed, M., & Mebarki, A. (2018). Post-earthquake assessment of buildings damage using fuzzy logic. Engineering Structures. 166, 117-127. doi: 10.1016/j.engstruct.2018.03.055
  14. Anhorn, J., & Khazai, B. (2014). Open space suitability analysis for emergency shelter after an earthquake. Natural Hazards and Earth System Sciences. 2, 4263–4297. doi: 10.5194/nhessd-2-4263-2014
  15. ArcGIS-DevelopTeam. (2023). An overview of fuzzy classes (ArcGIS Online Help) Retrieved 2023.03.05, from http://desktop.arcgis.com/en/arcmap/10.3/analyze/arcpy-spatial-analyst/an-overview-of-fuzzy-classes.htm
  16. Azarkish, M., Hafez Rezazadeh, M. & Miri, G. (2017). Application of Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) in locating temporary shelters after natural disasters (Case study: District 2 of Zahedan Municipality). Geographic Space, 17(58), 169-189. [In Persian]
  17. Bouzarjomehri, K., Javani, K., & Katebi, M.R. (2015). Optimal location of temporary shelters in crisis management of rural areas (Case study: Central part of Farouj County). Geography and Environmental Hazards, 4(4), 1-20. doi: 10.22067/GEO.V4I4.42299 [In Persian]
  18. Chu, J., & Su, Y. (2012). The Application of TOPSIS method in selecting fixed seismic shelter for evacuation in cities. Systems Engineering Procedia, 3, 391-397. doi: 10.1016/j.sepro.2011.10.061
  19. Donevska, K., & Gorsevski, P., Jovanovski, M. & Peshevski, I. (2011). Regional non-hazardous landfill site selection by integrating fuzzy logic, AHP and Geographic Information Systems. Environmental Earth Sciences, 67, 121-131. doi: 10.1007/s12665-011-1485-y
  20. Forghani, M. A., & Darbandi, S. (2015). Evaluation of effective factors in selecting temporary shelter locations after earthquakes using GIS and AHP technique (Case study: District 4 of Kerman). Journal of Rescue and Relief, 7(2), 54-80. [In Persian]
  21. Ghanbari, A., Salekimaleki, M. A., & Ghasemi, M. (2013). Optimal location of temporary shelters for earthquake victims with fuzzy approach (Case study: Tabriz city). Journal of Rescue and Relief, 5(2), 52-69. [In Persian]
  22. Ghodrati Amiri, G., Esmari Saadabad, S. & Zare Hosseinzadeh, A. (2013). Earthquake risk analysis using fuzzy inference system and its application in seismic rehabilitation studies of existing buildings. Modares Civil Engineering Journal, 13(4), 71-84. [In Persian]
  23. Hassani, N., Rafiei Anzab, N., & Shadabfar, M. (2012). Seismic retrofitting of schools and comparison of applied methods using pushover nonlinear static analysis. 2nd National Crisis Management Conference, Tehran. [In Persian]
  24. Hassanpour Kazerouni, N., Alavian, S.M.H. & Tahmasebizadeh, F. (2020). Analyzing the suitability and prioritizing public and governmental buildings for collective sheltering centers in earthquake crisis conditions using Geographic Information System, Case study: District 5 of Tehran. Knowledge of Crisis Prevention and Management, 10(1), 91-103. doi: ‎20.1001.1.23225955.1399.10.1.7.3 [In Persian]
  25. Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]. IEEE Transactions on Automatic Control, 42(10), 1482-1484. doi: 10.1109/TAC.1997.633847
  26. Kılcı, F., Bahar Yetiş, K. & Bozkaya, B. (2015). Locating temporary shelter areas after an earthquake: A case for Turkey. European Journal of Operational Research, 243(1), 323-332. doi: 10.1016/j.ejor.2014.11.035
  27. Li, H., Zhao, L., Huang, R., & Hu, Q. (2017). Hierarchical earthquake shelter planning in urban areas: A case for Shanghai in China. International Journal of Disaster Risk Reduction, 22, 431-446. doi: 10.1016/j.ijdrr.2017.01.007
  28. Ling, W. K. (2007). Nonlinear Digital Filters: Analysis and Applications (1st ed.). Academic Press.
  29. Liu, J., Fan, Y., & Shi, P. (2011). Response to a high-altitude earthquake: The Yushu Earthquake example. International Journal of Disaster Risk Science, 2, 43-53. doi: 10.1007/s13753-011-0005-8
  30. Malakar, S., & Rai, A. K. (2021). Earthquake vulnerability in the Himalaya by integrated multi-criteria decision models. Natural Hazards, 111, 213-237. doi: 10.1007/s11069-021-05050-8
  31. Narouei, K., & Aghaeezadeh, E. (2017). Locating temporary shelter sites against earthquakes in cities (Case study: Zahedan city). Geography and Urban Space Development, 4(1), 155-173. doi:  10.22067/gusd.v4i1.54032 [In Persian]
  32. Nojavaan, M., Omidvar, B., & Salehi, E. (2013). Locating temporary shelters using fuzzy algorithms; Case study: District 1 of Tehran Municipality. Urban Management, 11(31), 205-221. [In Persian]
  33. Pasten, D., Saravia, G., Vogel, E. E. & Posadas, A. (2022). Information theory and earthquakes: Depth propagation seismicity in northern Chile. Chaos, Solitons & Fractals, 165(P2), 112874. doi: 10.1016/j.chaos.2022.112874
  34. Payamrad, D., & Vafainezhad, A.R. (2015). Assisting earthquake crisis management by locating temporary shelter centers using a GIS-based decision support system (Case study: District 8 of Isfahan Municipality). Geomatics Science and Technology, 5(2), 231-246. [In Persian]
  35. Rangzan, K., Kabolizadeh, M., Karimi, D. & Mansoornaeimi, E. (2016). Earthquake vulnerability zoning and safe area locating during natural hazards using artificial intelligence algorithms and GIS (Case study: District 1 of Ahvaz Municipality). Geography and Environmental Planning, 27(3), 49-66. doi: 10.22108/GEP.2017.97958 [In Persian]
  36. Rashidi Ebrahimhesari, A., Attar, M.A., Givehchi, S. & Nasbi, N. (2013). Locating temporary shelters after earthquake using GIS and AHP technique, case study: District 6 of Shiraz. Urban and Regional Studies and Research, 5(17), 101-118. [In Persian]
  37. Shariar Alam, Md., & Mahbubul Haque, S. (2021). Multi-dimensional earthquake vulnerability assessment of residential neighborhoods of Mymensingh City, Bangladesh: A spatial multi-criteria analysis based approach. Journal of Urban Management, 11(1), 37-58. doi: 10.1016/j.jum.2021.09.001
  38. Sugeno, M. (1985). An introductory survey of fuzzy control. Information Sciences, 36(1-2), 59-83. doi: 10.1016/0020-0255(85)90026-X
  39. Tudes, S., & Yigiter, N.D. (2010). Preparation of land use planning model using GIS based on AHP: case study Adana-Turkey. Bulletin of Engineering Geology and the Environment, 69, 235-245. doi: 10.1007/s10064-009-0247-5
  40. Van Leekwijck, W. & Kerre, E.E. (1999). Defuzzification: criteria and classification. Fuzzy Sets and Systems, 108(2), 159-178. doi: 10.1016/S0165-0114(97)00337-0
  41. Wu, D., & Mendel, J. (2019). Recommendations on designing practical interval type-2 fuzzy systems. Engineering Applications of Artificial Intelligence. 85. 182-193. doi: 10.1016/j.engappai.2019.06.012