Results show that, the meth-odology is feasible for obtaining the optimum design of RC cantilever retaining walls.
📌 3 Key Takeaways
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Objective & Hypothesis:An approach is presented for optimum design of cantilever reinforced concrete (RC) retaining wall via teaching-learning based optimization (TLBO) algorithm.
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Key Findings & Data:The objec-tive function of the optimization is to minimize total material cost including concrete and reinforcing steel bars of the cantilever retaining wall by considering overturning, sliding and bearing stabilities, bending moment and shear capacities and require-ments for design and construction of reinforced concrete structures (TS 500/2000).
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Conclusion & Impact:Results show that, the meth-odology is feasible for obtaining the optimum design of RC cantilever retaining walls.
🔬Design:Modeling & Simulation
📍Scope:2000 Verileri
⚙️Variables:Domain Parameters
Özet
An approach is presented for optimum design of cantilever reinforced concrete (RC) retaining wall via teaching-learning based optimization (TLBO) algorithm. The objec-tive function of the optimization is to minimize total material cost including concrete and reinforcing steel bars of the cantilever retaining wall by considering overturning, sliding and bearing stabilities, bending moment and shear capacities and require-ments for design and construction of reinforced concrete structures (TS 500/2000). TLBO algorithm is a simple algorithm without any special algorithm parameters. This innovative approach is providing an advantage to TLBO in terms of easily applying to the problem. The proposed method has been performed on numerical examples and the results are compared with previous approaches. Results show that, the meth-odology is feasible for obtaining the optimum design of RC cantilever retaining walls.
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Cite This Article
BEKDAŞ, G., TEMÜR, R. (2017). Metaheuristic approaches for optimum design of cantilever reinforced concrete retaining walls. Challenge Journal of Structural Mechanics, Cilt: 3 - Sayı: 1, 23–30. https://ajindex.com/articles/144841
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