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

Year 2024, , 134 - 150, 12.06.2024

Bilal Tayfur , Hakan Alper Kamiloğlu

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

Cited By: 1

Abstract

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.

Keywords

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

Ethical Statement

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

Abstract

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.

Keywords

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

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