A Novel Method for Optimizing Parameters influencing the Bearing Capacity of Geosynthetic Reinforced Sand Using RSM, ANN, and Multiobjective Genetic Algorithm

• Autorzy: Lafifi Brahim , Rouaiguia Ammar , Soltani El Alia

Identyfikatory

DOI

10.2478/sgem-2023-0006

• Języki publikacji: EN

Abstrakty

EN

In this study, a novel method is proposed to optimize the reinforced parameters influencing the bearing capacity of a shallow square foundation resting on sandy soil reinforced with geosynthetic. The parameters to be optimized are reinforcement length (L), the number of reinforcement layers (N), the depth of the topmost layer of geosynthetic (U), and the vertical distance between two reinforcement layers (X). To achieve this objective, 25 laboratory small-scale model tests were conducted on reinforced sand. This laboratory-scale model has used two geosynthetics as reinforcement materials and one sandy soil. Firstly, the effect of reinforcement parameters on the bearing load was investigated using the analysis of variance (ANOVA). Both response surface methodology (RSM) and artificial neural networks (ANN) tools were applied and compared to model bearing capacity. Finally, the multiobjective genetic algorithm (MOGA) coupled with RSM and ANN models was used to solve multi objective optimization problems. The design of bearing capacity is considered a multi-objective optimization problem. In this regard, the two conflicting objectives are the need to maximize bearing capacity and minimize the cost. According to the obtained results, an informed decision regarding the design of the bearing capacity of reinforced sand is reached.

Słowa kluczowe

EN

bearing capacity; model test; square footing; geosynthetic; RSM; response surface methodology; ANN; artificial neural network; multiobjective optimisation

• Wydawca: De Gruyter
• Czasopismo: Studia Geotechnica et Mechanica
• Rocznik: 2023
• Tom: Vol. 45, nr 2
• Strony: 174--196
• Opis fizyczny: Bibliogr. 84 poz., rys., tab.

Twórcy

autor

Lafifi Brahim

• Laboratory of Civil Engineering and Hydraulics, University 8 Mai 1945 Guelma, Guelma, Algeria

autor

Rouaiguia Ammar

• Laboratory of Civil Engineering and Hydraulics, University 8 Mai 1945 Guelma, Guelma, Algeria

autor

Soltani El Alia

• Laboratory of Civil Engineering and Hydraulics, University 8 Mai 1945 Guelma, Guelma, Algeria

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).