Optimization Of Cantilever Retaining Wall Design Using Improved Teaching-Learning-Based Optimization Algorithms

Yıl 2024, , 134 - 150, 12.06.2024

Bilal Tayfur , Hakan Alper Kamiloğlu

https://doi.org/10.62520/fujece.1430236

Öz

Retaining structures play a crucial role in geotechnical engineering to support soil levels, prevent slope failure, and create flat surfaces for construction. Designing these structures involves optimizing internal and external stability while minimizing material usage and cost. This study focused on optimizing reinforced concrete cantilever retaining walls using the Teaching-Learning Based Optimization (TLBO) algorithm and an improved version (I-TLBO) with agents. In the context of the study, geometric-structural design variables, geotechnical -structural constraints, and optimization processes were examined. Minimizing weight and minimizing cost of the wall were the objectives considered in the cantilever retaining wall design process. The optimization results were compared with other algorithms in the literature, such as genetic algorithms, evolutionary strategies, and particle swarm optimization. The improved TLBO algorithm demonstrated superior performance, achieved lower design dimensions, and reduced costs. It provided more efficient solutions that pushed design constraints closer to their limits, resulting in a cost-effective and structurally sound cantilever retaining wall design. As a result of the study, the I-TLBO algorithm was found to be more cost and weight-effective than other methods in the optimization of cantilever retaining wall design.

Anahtar Kelimeler

Retaining wall design, Cantilever retaining wall, Stability analyses, Optimization; teaching-learning-based optimization (TLBO)

Etik Beyan

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Geliştirilmiş Öğretme-Öğrenme Tabanlı Optimizasyon Algoritmaları Kullanılarak Konsol İstinat Duvarı Tasarımının Optimizasyonu

Öz

Dayanma yapıları, geoteknik mühendisliğinde zemin seviyelerini desteklenmesi, şev göçmelerinin önlenmesi ve tesfiye yüzeylerinin oluşturulması açısından önem arz etmektedirler. Bu yapıların tasarımı, malzeme kullanımını ve maliyeti en aza indirirken iç ve dış stabilite için optimizasyonu içerir. Bu çalışma, betonarme konsol istinat duvarlarının Öğretme-Öğrenme Tabanlı Optimizasyon (TLBO) algoritması ve aracılarla geliştirilmiş bir versiyonu (I-TLBO) kullanılarak optimize edilmesine odaklanmaktadır. Konsol istinat duvarı tasarım süreci iki amaç fonksiyonunu dikkate almaktadır: ağırlığı ve maliyeti en aza indirme. Çalışma, geometrik ve yapısal tasarım değişkenlerini, geoteknik ve yapısal kısıtları ve optimizasyon süreçlerini incelemektedir. Optimizasyon sonuçları, genetik algoritmalar, evrimsel stratejiler ve parçacık sürüsü optimizasyonu gibi literatürdeki diğer algoritmalarla karşılaştırılmıştır. Geliştirilmiş TLBO algoritması, daha düşük tasarım boyutları ve daha düşük maliyetler elde ederek görece daha başarılı sonuçlar vermiştir. Geliştirilmiş TLBO algoritması tasarım kısıtlamalarını sınırlarına yaklaştıran daha verimli çözümler sunmuş, daha uygun maliyetli ve yapısal olarak daha stabil konsol istinat duvarı tasarımları elde edilmesini olanak tanımıştır. Çalışma sonucunda, I-TLBO algoritmasının konsol istinat duvarı tasarımının optimizasyonunda diğer yöntemlere göre daha düşük maliyet ve daha düşük ağırlıkların ede edilmesi bakımından etkin sonuçlar sunduğu görülmüştür.

Anahtar Kelimeler

İstinat duvarı tasarımı, Konsol istinat duvarı, Stabilite analizleri, Optimizasyon, Öğretme-öğrenme tabanlı optimizasyon (TLBO)

Kaynakça

• A. Scotto di Santolo, and A. Evangelista, “Dynamic active earth pressure on cantilever retaining walls”, Comput. Geotech. vol. 38, pp. 1041–1051. 2011.
• A. Kaveh, M. Kalateh-Ahani and M. Fahimi-Farzam, “Constructability optimal design of reinforced concrete retaining walls using a multi-objective genetic algorithm.” Struct. Eng. Mech., vol. 47, pp. 227–245, 2013.
• R.Temur and G. Bekdas, “Teaching learning-based optimization for design of cantilever retaining walls.” Struct. Eng. Mech. vol. 57, pp. 763–783, 2016.
• A. Kaveh and A.F. Behnam, “Charged system search algorithm for the optimum cost design of reinforced concrete cantilever retaining walls.” Arab. J. Sci. Eng. vol. 38, pp. 563–570, 2013.
• F. Keivanian, R. Chiong, A.R. Kashani, and A.H. Gandomi, “A fuzzy adaptive metaheuristic algorithm for identifying sustainable, economical, lightweight, and earthquake-resistant reinforced concrete cantilever retaining walls.” Ar.Xiv. Prepr. ArXiv. 2302.00198.
• C.V. Camp, and A. Akin, “Design of retaining walls using big bang–big crunch optimization.” J. Struct. Eng. Vol.138, pp.438–448.
• S. Srivastava, S. Pandey,and R. Kumar, “Optimization of reinforced concrete cantilever retaining wall using particle swarm optimization.” IOP Conf Ser Mater Sci Eng vol. 1225:012042, 2022.
• C. Xu, B. Gordan, M. Koopialipoor, D. J.Armaghani, M.M. Tahir, and X. Zhang, “Improving performance of retaining walls under dynamic conditions developing an optimized ANN based on ant colony optimization technique.” IEEE Access, Vol. 7, 94692–700, 2019.
• E.N. Kalemci, S.B İkizler, T. Dede and Z. Angın, "Design of reinforced concrete cantilever retaining wall using Grey wolf optimization algorithm" Structures, vol. 23, pp. 245–253, 2020.
• B. Ceranic, C. Fryer and R.W. Baines, “An application of simulated annealing to the optimum design of reinforced concrete retaining structures.” Comput. Struct. vol. 79, pp.1569–1581, 2001.
• A.H. Gandomi, A.R. Kashani, D.A. Roke and M. Mousavi, "Optimization of retaining wall design using evolutionary algorithms.” Struct. Multidiscip. Optim. vol. 55, pp. 809–825, 2017.
• A.H. Gandomi, and A.R.Kashan, “Automating pseudo-static analysis of concrete cantilever retaining wall using evolutionary algorithms.” Measurement, vol. 115, pp. 104–124.
• R. Sheikholeslami, B.G. Khalili, A. Sadollah and J. Kim, “Optimization of reinforced concrete retaining walls via hybrid firefly algorithm with upper bound strategy.” KSCE J. Civ. Eng. vol. 20, pp. 2428–2438, 2016.
• R.V. Rao, V.J. Savsani, and D.P. Vakharia, “Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems.” CAD Comput. Aided. Des. vol. 43, pp. 303–315, 2011.
• R.V. Rao, V.J. Savsani, and D.P. Vakharia, “Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems.” Inf. Sci. (Ny). vol.183, pp.1–15.
• F. Zou, L. Wang, X. Hei, D. Chen and D. Yang, “Teaching–learning-based optimization with dynamic group strategy for global optimization.” Inf. Sci. (Ny) vol. 273, pp.112–131.
• M. Črepinšek, S.H. Liu, and L. Mernik, "A note on teaching-learning-based optimization algorithm." Inf. Sci. (Ny), vol.212, pp. 79–93, 2012.
• E. Uray, and S. Çarbaş, “ Dynamic loads and different soil characteristics examination on optimum design of cantilever retaining walls utilizing Harmony Search Algorithm.” I. Int. J. Eng. Appl. Sci., vol.213, pp. 140-154, 2021.
• N.H. Ri, Y.S. Ri, Y.A. Kim Z. Ma, and Y. Liu and S. Çarbaş, “ Stability analysis and size optimization of retaining walls on unsaturated soil.” Soil Mech. Found. Eng.., vol.57, pp. 191-199, 2020.
• G. Bath, J. Dhillon and B.Walia, " Optimization of geometric design of retaining wall by Differential Evolution Technique " Int. J. Comput. Eng. Res., vol.8, pp. 191-199, 2018.
• W.J.M. Rankine. “On the stability of loose earth.” Philos Trans R Soc London 1857;147:928.
• K. Terzaghi, Theoretical Soil Mechanics. New York: Wiley; 1943.
• G.G. Meyerhof. The Bearing Capacity of Foundations Under Eccentric and Inclined Loads. 3rd ICSMFE, Zürich: 1953, p. 440–5.
• V. Toĝan, “Design of planar steel frames using Teaching-Learning Based Optimization.” Eng. Struct. vol. 34, pp. 225–232, 2012.
• R.V. Rao and V. Patel, “An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems.” Sci. Iran. vol. 20, pp. 710–720.
• M. Artar, and S. Carbas, “Discrete sizing design of steel truss bridges through teaching-learning-based and biogeography-based optimization algorithms involving dynamic constraints.” Structures, vol. 34 pp. 3533–3547, 2021.
• S.O. Degertekin and M.S. Hayalioglu, “Sizing truss structures using teaching-learning-based optimization.” Comput. Struct. vol. 119, p.p. 177–188, 2013.
• S.O. Degertekin and M.S. Hayalioglu, “Sizing truss structures using teaching-learning-based optimization.” Comput. Struct. vol. 119, p.p. 177–188, 2013.
• Lopez R, “Optimum project of cantilever retaining wall using search group algorithm and backtracking search algorithm.” Rev. Int. Métodos. Numéricos. Para. Cálculoy. Diseño. En. Ing. 2017.
• A.H. Gandomi, A.R. Kashani, and F. Zeighami, “Retaining wall optimization using interior search algorithm with different bound constraint handling.” Int. J. Numer. Anal. Methods. Geomech. vol. 41, pp. 1304–1331, 2017.