Research Article
Design of Reinforced Concrete Retaining Wall by Hybrid Teaching Learning Based Optimization
Honar Issa
,
Charles Camp*
Issue:
Volume 14, Issue 3, June 2026
Pages:
134-148
Received:
9 April 2026
Accepted:
25 April 2026
Published:
13 May 2026
DOI:
10.11648/j.ajce.20261403.11
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Abstract: Reinforced concrete (RC) retaining walls are widely used in civil engineering applications, where economical and efficient designs are essential, given their extensive use and material demands. This study aims to optimize the weight and cost of RC cantilever retaining walls by developing a hybrid Teaching–Learning-Based Optimization (TLBO) algorithm with enhanced performance characteristics. The proposed method introduces a multi-population selection strategy that improves exploration of the design space in early iterations and promotes convergence in later stages. In addition, a pre-generated list of feasible reinforcement configurations is incorporated to eliminate repetitive constraint checks, thereby reducing computational effort. The optimization framework considers both geotechnical and structural constraints, including stability against sliding and overturning, bearing capacity, and compliance with ACI 318-19 design requirements. Two benchmark problems—retaining walls with and without a shear key—are analyzed to evaluate the effectiveness of the proposed hybrid TLBO. The results are compared with several established optimization techniques, including genetic algorithms, particle swarm optimization, grey wolf optimization, and other heuristic methods. The findings demonstrate that the hybrid TLBO algorithm provides more consistent, near-optimal solutions, as indicated by lower standard deviation values and improved convergence. The optimized designs achieve reduced cost and weight while satisfying all design constraints, with several critical constraints approaching their capacity limits, indicating optimal resource utilization. Furthermore, the proposed modifications reduce computational time by eliminating up to 20% of constraint evaluations. Overall, the study confirms that the hybrid TLBO approach is a robust and efficient tool for the optimal design of RC retaining walls, offering superior performance compared to conventional optimization methods.
Abstract: Reinforced concrete (RC) retaining walls are widely used in civil engineering applications, where economical and efficient designs are essential, given their extensive use and material demands. This study aims to optimize the weight and cost of RC cantilever retaining walls by developing a hybrid Teaching–Learning-Based Optimization (TLBO) algorith...
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