نقش مدیریت هوشمند در برنامه‌ریزی کالبدی شهر برای کاهش آثار زمین لرزه

محورهای موضوعی : شهرسازی

اسماعیل شیعه ¹ (استاد شهرسازی، دانشکده شهرسازی و معماری، دانشگاه علم و صنعت ایران، تهران، ایران)
کیومرث حبیبی ² (دانشیار شهرسازی، دانشکده معماری و شهرسازی، دانشگاه کردستان، سنندج، ایران.)
مهران احسانی ³ (د‌‌‌‌‌‌‌کتری شهرسازی ، د‌‌‌‌‌‌انشکد‌‌‌‌‌‌‌‌ه معماری و شهرسازی ، د‌‌‌‌‌‌انشگاه علم و صنعت ایران، تهران، ایران.)

تاریخ دریافت : 1348/10/11 تاریخ پذیرش : 1348/10/11 تاریخ انتشار : 1348/10/11

کلید واژه: زمین‌لرزه, برنامه‌ریزی کالبدی, شهر هوشمند, مدیریت هوشمند, تصمیم‌گیری هوشمند,

چکیده مقاله :

ارزیابی و پایش برنامه‌های مدیریت خطرپذیری از شاخص‌های مدیریت هوشمند شهری است. در این تحقیق ضمن تأکید بر الزامات شهر هوشمند، به نقش کلیدی مدیریت هوشمند در پشتیبانی از تصمیم‌گیری برای پیشگیری، آمادگی و کاهش خطرپذیری کالبدی یک شهر برای زمین‌لرزه اشاره می‌گردد. روش مطالعه از نوع کتابخانه‌ای و شامل گردآوری اطلاعات و سوابق موضوع، غربالگری، دسته‌بندی و تحلیل توصیفی می‌باشد. بدین منظور از مفهوم پشتیبان تصمیم‌گیری هوشمند استفاده گردید. نتایج نشان داد با اعمال مدیریت هوشمند و به کارگیری فنآوری‌های نوین میزان مخاطرات زمین‌لرزه کاهش می‌یابد. شیوه‌های سنتی از انعطاف‌پذیری، خوداصلاحی و تطابق‌پذیری لازم برای مواجهه کارآمد با زمین‌لرزه برخوردار نیستند. کارآمدی الگوی مدیریت هوشمند از طریق مداخله در نظام برنامه‌ریزی کالبدی شهر مستلزم بهره‌مندی از سامانه‌های هوشمند اطلاع‌رسانی وقوع زمین‌لرزه و پایش زیرساخت‌های شهر است که می‌توان با طراحی سامانه‌ای، میزان مداخله‌پذیری مدیریت شهری را به صورت هوشمندانه در یک شهر و قبل از وقوع زمین‌لرزه تعیین نمود.

چکیده انگلیسی:

Abstract Quantitative and qualitative Assessment and monitoring of risk managed planning and crisis management and their examining their effectiveness reveals need for intelligent urban management in terms of physical planning, especially management of natural disasters such as earthquake. Study of strong earthquakes happening in large urban areas that highlight positive effect of crisis management explicitly indicate an intelligent management model with regard to urban physical planning. Present modern cities with a large extent of complexity and intertwined network of connection under the umbrella of high-tech information technology as well as electronic technologies require a revision of traditional approaches together with creative intelligent management. This study not only emphasizes the necessity for the requirements of intelligent cities, including employing digital information systems versus analogue information and inevitability of updating as well as integrating urban physical data, but also elaborates the key role of intelligent management in supporting any kind of decision making in order to prevent, prepare for, and mitigate risk associated with physical fabric in a city in the event of earthquakes. The methodology employed in this study includes literature review, including gathering information and history of the field, screening of this information, and ultimately its categorization as well as descriptive analysis. To this end, theoretical fundamentals of intelligent decision support are taken into consideration. The results show that by employing intelligent management through incorporating physical planning along with modern technology into urban infrastructures, hazards of earthquakes can be mitigated. Using this management approach in urban physical planning is necessary due to rapid expansion of cities. Furthermore, other approaches lack flexibility, self correction, and adaptation required effectively and efficiently to cope with seismic crises. Efficiency of intelligent urban managing model through intervention in physical planning requires benefitting smart seismic-alarm systems and monitoring vital urban infrastructures, where the extent of intervention in urban management within urban physical fabric can be determined in an intelligent fashion and prior to occurrence of earthquakes. The application of intelligent management within the framework of physical planning is an efficient and excellent way of preventing, preparing, coping and relief and rebuilding a city against earthquake crises. Because using the concept of urban intelligent management, decision making for earthquake interventions to confront the earthquake has self-improvement capabilities, flexibility and adaptation to urban variable needs. In this model, an assessment of various urban criteria is undertaken to prevent, prepare and reduce the risk to earthquakes. An analysis of the relationship between physical planning and urban land use and the communication and infrastructure network with the degree of earthquake vulnerability is considered as one of the intelligent management policies in reducing vulnerability. So that any crisis decision, requires intelligent arrangements to support the continuous monitoring of indicators and self-improvement, supporting a variety of methods, replacing indices and creating productivity (flexibility) and supporting Establish future studies and adaptation at all times (adaptability), in order to finally identify the effective variables in the city's vulnerability and create the integrity between intelligent management of efficiency for prevention and preparedness against earthquakes.

منابع و مأخذ:

1. احسانی، مهران. (۱۳۸۴). نقش مد‌‌‌‌‌‌‌‌یریت و برنامه‌ریزی شهری جهت پیشگیری از آسیب‌پذیری ‌‌د‌‌‌‌‌‌‌‌ر بحران‌های طبیعی، مطالعه مورد‌‌‌‌‌‌‌‌ی منطقه 10 تهران. پایان‌نامه کارشناسی ارشد‌‌‌‌‌‌‌، د‌‌‌‌‌‌انشگاه علم و صنعت، تهران.
2. سعید‌‌‌‌‌‌‌‌نیا، احمد‌‌‌‌‌‌‌. (۱۳۸۷). کاربری زمین شهری. نشریه د‌‌‌‌‌‌انش شهر مرکز مطالعات برنامه‌ریزی شهری. 99، 18-24
3. فتحیان، محمد‌‌‌‌‌‌‌؛ و مهد‌‌‌‌‌‌‌وی‌نور، سید‌‌‌‌‌‌‌ حاتم. (۱۳۹3). مبانی و مد‌‌‌‌‌‌‌‌یریت فناوری اطلاعات. (ویرایش ۲). تهران: د‌‌‌‌‌‌انشگاه علم و صنعت ایران.
4. کیانی، اکبر. (۱۳۸۳). شبیه‌سازی و ارزیابی سنسور های هوشمند‌‌‌‌‌‌‌ و آتوماتای سلولی ‌‌د‌‌‌‌‌‌‌‌ر تحلیل کاربری فیزیکی ابر نقشه شهر (مطالعه مورد‌‌‌‌‌‌‌‌ی: کلان‌شهر تهران )، رساله د‌‌‌‌‌‌‌کتری، د‌‌‌‌‌‌انشگاه تربیت مد‌‌‌‌‌رس. تهران.

5. Alvino, K. (2010). Revitalizing urban texture. Building Research & Information: New Jersey
6. Berke, P., & Smith, G. (2006). Hazard Mitigation, Planning, and Disas‌ter Resiliency: Challenges and Strategic Choices for the 21s‌t Century. In Sus‌tainable Development and Disas‌ter Resiliency, IOS Press: Ames‌terdam.
7. Blaikie, P., Cannon, T., Davis, I., & Wisner, B. (1994). At Risk, natural hazards, people’s vulnerability, and disas‌ters. Routledge: London
8. Boyle, D., Yates, D., & Yeatman, E. (2013). Urban Sensor Data Streams. IEEE Internet Computing, 17, 30- 64.
9. CDMPS .(2013). Center for Disas‌ter Management and Public Safety, Retrieved March,13, 2018 , from http://cdmps.unimelb.edu.au.
10. Chen, R. (2012). Pragmatic development s‌trateguc thinking about could compute intelligence and wisdom of the city emergency response. Mobile Communications, 36 (3), 1-16.
11. Chun-Nen, H. (2014). A method for exploring the interdepencies and important of critical infras‌tructures. Knowledge-Based Sys‌tems, 55, 66 – 74.
12. Correia, L. M. (2011). Smart cities applications and requirments, Network European Technology Platform.
13. Deakin, M. (2013). Smart Cities: Governing, Modelling and Analysing the Transition ,15-70. Routledge: London and NewYork.
14. Dutta, V. (2012). War on the Dream, How Land use Dynamics and Peri-urban Growth Characteris‌tics of a Sprawling City Devour the Mas‌ter Plan and Urban Suitability, A Fuzzy Multi-criteria Decision Making Approach. Retrieved june 2018, from http://www.gdn.int/sites/default/files/3_3_Venkatesh%20Dutta_Paper_T3%20%28Revised%29.pdf
15. European Commission. (2015). Digital Agenda for Europe. European Ins‌titute for Gender Equality (eige)
16. Fuchs, R. J., Brennan, E., Chamie, J., Lo, F ., & Uitto, J. I. (1994) .Mega-City Growth and the Future, (Firs‌t ed). United Nations University Press :United State of America.
17. Fuller, R. & Crawford, M. (2011). Impact of pas‌t and future residential housing development patterns on energy demand and related emissions. Journal of Housing and the Built Environment, 26 (2), 8–183.
18. Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanović, N., & Meijers, E. (2007). Smart Cities: Ranking of European Medium-Sized Cities. Retrieved june 2018, from http://www.smartcities.eu/download/smart_cities_final_report.pdf.
19. Hyeok,Y. J. (2012). Smart city smart s‌trategy. Retrieved june 2018, from https://docplayer.net/20947589-Smart-city-smart-s‌trategy.html.
20. Hollands, R. G. (2008). Will the real smart city please s‌tand up?. City , 12 (3), 303–320.
21. James, P., Holden, M., Lewin, M., Neilson, L., Oakley, C., Truter, A., & Wilmoth, D. (2013). Managing Metropolises by Negotiating Mega-Urban Growth. In Harald Mieg and Klaus Töpfer. Ins‌titutional and Social Innovation for Sus‌tainable Urban Development. Routledge: London.
22. Komninos, N. (2013). What makes cities intelligent. In Deakin, Mark. Smart Cities: Governing, Modelling and Analysing the Transition, 21-95. Routledge:London.
23. León, J., & March, A. (2014). Urban morphology as a tool for supporting tsunami rapid resilience: A case s‌tudy of Talcahuano, Chile. Habitat International, 43, 250-262.
24. Moehle , J., Barkley, C., Boniwitz, D., Karlinsky, S., Maffei, J & Polland, C. (2009). The Resilient City – A Way of Thinking about Preparedness, Mitigation, and Rebuilding. Earthquake Engineering. Proceeding of the NZSEE conference. April 3-5. Retrieved june 2018, from https://www.nzsee.org.nz/db/2009/Paper01.pdf
25. Odendaal, N. (2003). Information and communication technology and local governance: Unders‌tanding the difference between cities in developed and emerging economies. Computers, Environment and Urban Sys‌tems, 27(6), 585-607.
26. Pearce, F. (2006). How big can cities get, New Scientis‌t, 190, 8-56.
27. Russell, S. J. & Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Prentice Hall: New Jersey.
28. Sanderson, D. (2000). Cities, disas‌ters and livelihoods. Environment and Urbanization, 12(2), 50-102.
29. Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nilsson, M., & Oliveira, A. (2011, May). Smart cities and the future internet: Towards cooperation frameworks for open innovation. In The future internet assembly ,15, 431-446. Springer: Berlin, Heidelberg.
30. Tang, A., Wen, A. (2009). An Intelligent Simulation Sys‌tem for Earthquake Disas‌ter Assessment. Computers & Geosciences, 35 (5), 871-879.
31. Liang, T. P., Turban, E., & Aronson, J. E. (2005). Decision Support Sys‌tems and Intelligent Sys‌tems. Penerbit Andi: Yogyakarta.
32. UNDP. (2016). UN DP’s supp ort to sus‌tainable, inclusive and resilient cities in the developing world, (vol.7), 1-254. United Nations Development Programme One United Nations Plaza: New York.
33.UNISDR.(2010).Resilientcities:mycityisgettingready.Geneva:UNISDRpress. Retrieved june 2018 , from https://www.unisdr.org/we/campaign/cities
34. UN-HABITAT. (2003). The challenge of slums, Global report on human settlements. Earthscan: London.
35. Van der Spek, R. & Spijkervet, A. (1997). Knowledge management: dealing intelligently with knowledge, in Liebowitz, J. & Wilcox, L.C. (Eds), Knowledge Management and its Integrative Elements. CRC Press:Boca Raton, FL.
36. Vasseur, J. (2010). Smart cities and urban networks, In Vasseur, J. & Dunkels, A. (Eds.), Interconnecting Smart Objects with IP: the Next Internet, 360-377. Morgan Kaufmann: Burlington, MA,USA
37. Wolfram, M. (2012). Decons‌tructing smart cities: An intertextual reading of concepts and practices for integrated urban and ICT development in Manfred SCHRENK, Vasily V. POPOVICH, Peter ZEILE, Pietro ELISEI (Ed). Re-Mixing the City. Towards Sus‌tainability and Resilience? . Proceedings
of 17 th International Conference on Urban Planning, Regional Development and Information Society (REAL CORP 2012), May 14–16. Retrieved june 2018 ,from https://conference.corp.at/archive/RP2012_192.pdf
38. Wooldridge, M. J. & Jennings, N. R. (1995). Intelligent
agents: theory and practice. Knowledge engineering review, 10 (2), 115-152.
39.World Economic Forum. (2011). A vision for managing natural disas‌ter risk. Mc GrowHill: London