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<p>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 (<italic>N</italic>), 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.</p>
</abstract>

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.

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

Laboratory of Civil Engineering and Hydraulics

Studia Geotechnica et Mechanica

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this study, a novel method is proposed to optimize the reinforced parameters influencing the bearing capacity of a shallow square foundation resting on...

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

Article Category: Original Study

Online veröffentlicht: 31. Mai 2023

Seitenbereich: 174 - 196

Eingereicht: 21. Mai 2022

Akzeptiert: 03. Apr. 2023

DOI: https://doi.org/10.2478/sgem-2023-0006

Schlüsselwörter
Bearing capacity, model tests, square footing, geosynthetic, RSM, ANN, multi objective optimization

© 2023 Brahim Lafifi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Article Category: Original Study

Online veröffentlicht: 31. Mai 2023

Seitenbereich: 174 - 196

Eingereicht: 21. Mai 2022

Akzeptiert: 03. Apr. 2023

DOI: https://doi.org/10.2478/sgem-2023-0006

SchlüsselwörterBearing capacity, model tests, square footing, geosynthetic, RSM, ANN, multi objective optimization

© 2023 Brahim Lafifi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Bearing capacity, model tests, square footing, geosynthetic, RSM, ANN, multi objective optimization