ﺗﺠﺰﯾﻪ و ﺗﺤﻠﯿﻞ ریسک ﭘﺎﯾﺪاری دﯾﻮار ﺣﺎﺋﻞ وزﻧﯽ بر مبانی اعتمادپذیری ﺑﺎ اﺳﺘﻔﺎده از ﺗﺌﻮری ﻣﺠﻤﻮﻋﻪ ﻓﺎزی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 گروه مهندسی عمران، دانشکده عمران و معماری، دانشگاه شهید چمران اهواز، اهواز، ایران

2 مربی گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه لرستان، خرم آباد، ایران

چکیده

نگاه تک بعدی به مسئله‌ی بهینه سازی و در نظر نگرفتن سایر جنبه‌ها از جمله ریسک و عدم قطعیت در طراحی‌های مهندسی ممکن است باعث ایجاد خطراتی در حین بهره برداری از آن سیستم مهندسی شود. در اﯾﻦ ﻣﻘﺎﻟﻪ ﺑﻪ ﺑﺮرﺳﯽ ﺗﺎﺛﯿﺮ ﻋﺪم ﻗﻄﻌﯿﺖ و ریسک ﺑﺮ روی ﺿﺮاﯾﺐ اﻃﻤﯿﻨﺎن دﯾﻮار ﺣﺎﺋﻞ وزﻧﯽ ﭘﺮداﺧﺘﻪ ﺷﺪه اﺳﺖ. در اﯾﻦ ﻣﻘﺎﻟﻪ ﻧﺸﺎن داده ﺷﺪه اﺳﺖ که وجود ﻋﺪم ﻗﻄﻌﯿﺖ در پارامترهای طراحی همیشه باعث عملکرد نامطلوب سیستم نمی‌شود بلکه ممکن است در جهت تقویت آن سیستم مهندسی نیز عمل کند. ﺑدین ﻣﻨﻈﻮر از ﯾﮏ ﻣﺜﺎل از دﯾﻮار ﺣﺎﺋﻞ وزﻧﯽ زده ﺷﺪه اﺳﺖ و در اﺑﺘﺪا ﺑﺎ استفاده از ﺑﻬﯿﻨﻪ ﺳﺎزی، اﺑﻌﺎد ﺑﻬﯿﻨﻪ محاسبه و ﺳﭙﺲ با استفاده از تئوری مجموعه فازی، ﺗﺎﺛﯿﺮﻋﺪم ﻗﻄﻌﯿﺖﻫﺎی پارامترهای ورودی ﺑﺮ ﭘﺎﯾﺪاری دﯾﻮار ﺣﺎﺋﻞ وزﻧﯽ بهینه شده ﻧﺸﺎن داده ﺷﺪه اﺳﺖ. در انتها نیز با استفاده از مفهوم اعتمادپذیری، ریسک موجود در ضرایب اطمینان دیوار حائل وزنی به دست آمده است در اﯾﻦ مقاله ﻧﺸﺎن داده ﺷﺪه اﺳﺖ ﮐﻪ ﺑا وجود 10± درصد ﻋﺪم ﻗﻄﻌﯿﺖ در ﭘﺎراﻣﺘﺮﻫﺎی ﻃﺮاﺣﯽ، ﻣﻤﮑﻦ اﺳﺖ ﺗﺎ %(345+،80-) ﻋﺪم ﻗﻄﻌﯿﺖ در ﺿﺮﯾﺐ اﻃﻤﯿﻨﺎن اﯾﺠﺎد شود اما این عدم قطعیت باعث افزایش اعتماد پذیری به میزان 98.6039 درصد و باعث ایجاد ریسک با احتمال 1.3961 درصد می شود. در انتها نیز با استفاده از بلوک دیاگرام اعتماد پذیری مقدار اعتماد پذیری و ریسک کل برای دیوار حائل وزنی به ترتیب برابر با 79.3169 و 20.6830 درصد محاسبه شده است. استفاده از الگوریتم‌های Self-Adaptive Genetic Algorithm و Many Objective Genetic Algorithm که به ترتیب برای بهینه‌سازی دیوارحائل وزنی و محاسبه‌ی مقادیر حداقل و حداکثر ضرایب اطمینان استفاده شده است و همچنین محاسبه‌ی اعتمادپذیری و ریسک با استفاده از تئوری مجموعه فازی و استفاده از بلوک دیاگرام اعتماد پذیری برای محاسبه‌ی اعتماد پذیری و ریسک کل از نوآوری‌های مقاله‌ی حاضر می‌باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Gravity retaining wall stability risk analysis based on reliability using fuzzy set theory

نویسندگان [English]

  • Mohammad Mehdi Riyahi 1
  • Iman Bahrami chegeni 2
1 Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
2 Faculty of Engineering, Lorestan University, Khorramabad, Iran.
چکیده [English]

One-dimensional view of optimized design of engineering systems focusing on costs without considering other aspects such as risk and uncertainty in engineering design can increase risks during the operation of that engineering system. This paper indicates that if there are uncertainties in design parameters this cannot always cause poorly system operation, whereas may strengthen that system operation. For this purpose a gravity retaining wall is optimized and the optimal dimensions of that retaining wall are calculated. Then the effect of uncertainties, which are in the design parameters of that optimal gravity retaining wall, on the stability factors is calculated. Finally, using the concept of reliability, the risk in the safety factors of the retaining wall is obtained. In this paper, it is shown that if there is 10% uncertainty in design parameters the uncertainty propagation on safety factors is about (-80,+345)%, but this uncertainty propagation can increase the reliability(positive aspect of uncertainty) and risk(negative aspect of uncertainty) with a probability of 98.6039% and 1.3961% respectively. Then using the reliability block diagram, the total amount of reliability and risk for the gravity retaining wall is calculated which are equal to 79.3169 and 20.6830%, respectively. The innovations of this paper can be listed as below: 1) using Self-Adaptive Genetic Algorithm and Many Objective Genetic Algorithm, which have been used to optimize the retaining wall and calculate the uncertainty propagation on safety factors respectively. 2) calculating reliability and risk using fuzzy set theory.3) using reliability block diagram (RBD) to calculate the overall reliability and risk.

کلیدواژه‌ها [English]

  • Retaining wall
  • Optimization
  • Uncertainty
  • Fuzzy set theory
  • Reliability
  • Risk

مراجع

[1] Talatahari, S., Sheikholeslami, R., Shadfaran, M., & Pourbaba, M. (2012). Optimum design of gravity retaining walls using charged system search algorithm. Mathematical Problems in Engineering, 2012.
 [2] Das, B. M., & Sivakugan, N. (2016). Fundamentals of geotechnical engineering. Cengage Learning.
[3] S. Talatahari, R. Sheikholeslami, M. Shadfaran, M. Pourbaba, Optimum design of gravity retaining walls using charged system search algorithm, Mathematical Problems in Engineering, 2012 (2012).
[4] Haghighi, A., & Ayati, A. H. (2015). Uncertainty analysis of stability of gravity dams using the fuzzy set theory. Iran University of Science & Technology, 5(4), 465-478.
[5] Haghighi, A., & Asl, A. Z. (2014). Uncertainty analysis of water supply networks using the fuzzy set theory and NSGA-II. Engineering Applications of Artificial Intelligence, 32, 270-282.
 [6] Zadeh, L. A., Klir, G. J., & Yuan, B. (1996). Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers (Vol. 6). World Scientific.
[7] Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13.
[8] Zimmermann, H. J. (1986). Fuzzy set theory and mathematical programming. Fuzzy sets theory and applications, 99-114.
[9] Bit, A. K., Biswal, M. P., & Alam, S. S. (1992). Fuzzy programming approach to multicriteria decision making transportation problem. Fuzzy sets and systems, 50(2), 135-141.
[10] Zimmermann, H. J. (2010). Fuzzy set theory. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 317-332.
[11] Ghazavi, M., & BAZZAZIAN, B. S. (2011). Learning from ant society in optimizing concrete retaining walls.
[12] Kaveh, A., Talatahari, S., & Sheikholeslami, R. (2011). Optimum seismic design of gravity retaining walls using the heuristic big bang-big crunch algorithm. In Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering, Civil-Comp Press, Stirlingshire, Scotland, Paper (Vol. 4).
[13] Camp, C. V., & Akin, A. (2012). Design of retaining walls using big bang–big crunch optimization. Journal of Structural Engineering, 138(3), 438-448.
[14] Talatahari, S., Sheikholeslami, R., Shadfaran, M., & Pourbaba, M. (2012). Optimum design of gravity retaining walls using charged system search algorithm. Mathematical Problems in Engineering, 2012.
[15] Talatahari, S., & Sheikholeslami, R. (2014). Optimum design of gravity and reinforced retaining walls using enhanced charged system search algorithm. KSCE Journal of Civil Engineering, 18(5), 1464-1469.
[16] Li, Q. G. (2012). Reliability Analysis on Stability of Retaining Wall. In Advanced Materials Research (Vol. 368, pp. 1213-1216). Trans Tech Publications Ltd.
[17] Haghighi, A., & Ayati, A. H. (2016). Stability analysis of gravity dams under uncertainty using the fuzzy sets theory and a many-objective GA. Journal of Intelligent & Fuzzy Systems, 30(3), 1857-1868.
[18] Ghaleini, E. N., Koopialipoor, M., Momenzadeh, M., Sarafraz, M. E., Mohamad, E. T., & Gordan, B. (2019). A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls. Engineering with Computers, 35(2), 647-658.
[19] Gordan, B., Koopialipoor, M., Clementking, A., Tootoonchi, H., & Mohamad, E. T. (2019). Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques. Engineering with Computers, 35(3), 945-954.
[20] Pouraminian, M., Pourbakhshian, S., & Noroozinejad Farsangi, E. (2020). Reliability assessment and sensitivity analysis of concrete gravity dams by considering uncertainty in reservoir water levels and dam body materials. Civil and Environmental Engineering Reports, 30(1).
[21] Koopialipoor, M., Murlidhar, B. R., Hedayat, A., Armaghani, D. J., Gordan, B., & Mohamad, E. T. (2020). The use of new intelligent techniques in designing retaining walls. Engineering with Computers, 36(1), 283-294.
[22] Al Sebai, H. M., Barakat, S., & Arab, M. A Reliability-Based Design Optimization of Cantilever Reinforced Concrete Retaining Walls. In IFCEE 2021 (pp. 267-277).
[23] Moradi Kia, F., Ghafouri, H. R., & Riyahi, M. M. (2022). Uncertainty analysis and risk identification of the gravity dam stability using fuzzy set theory. Journal of Hydraulic Structures, 7(4), 76-92.
[24] Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning.
[25] Mays, L. W. (2010). Water resources engineering. John Wiley & Sons.

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سابقه مقاله

  • تاریخ دریافت: 05 دی 1400
  • تاریخ بازنگری: 12 فروردین 1401
  • تاریخ پذیرش: 09 اردیبهشت 1401

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