Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Probabilistic Analysis of a Geosynthetic Reinforced Soil Retaining Wall Under Seismic Conditions Using Genetic Programming
https://orcid.org/http://orcid.org/0000-0003-4599-2544
https://orcid.org/http://orcid.org/0000-0002-7514-8099
Reliability analysis of a geosynthetic reinforced retaining wall (GRRW) is performed under seismic conditions. GRRW is analysed deterministically using the horizontal slice method (HSM). To rule out the limitations of deterministic approach, a comprehensive probabilistic analysis is carried out using a biologically inspired evolutionary algorithm called genetic programming (GP). GP, commonly known as symbolic regression, automatically evolves both the structure and the parameters of the considered mathematical model. The tension mode of failure is considered in the probabilistic analysis. The stochastic parameters involved in the study include internal friction angle of soil (ϕ) and the unit weight (γ). The results are validated using the Monte Carlo simulation (MCS) method, for the same set of parameters. This is done to scrutinize the efficiency and accuracy of the proposed method. A parametric study including the influence of coefficient of variation of ϕ on the probability of failure of GRRW is conducted. The results depict the efficiency and robustness of the proposed methodology. The genetic programming is a precise evolutionary method that delivers high performance in estimating the probability of failure of GRRW.
Probabilistic Analysis of a Geosynthetic Reinforced Soil Retaining Wall Under Seismic Conditions Using Genetic Programming
https://orcid.org/http://orcid.org/0000-0003-4599-2544
https://orcid.org/http://orcid.org/0000-0002-7514-8099
Reliability analysis of a geosynthetic reinforced retaining wall (GRRW) is performed under seismic conditions. GRRW is analysed deterministically using the horizontal slice method (HSM). To rule out the limitations of deterministic approach, a comprehensive probabilistic analysis is carried out using a biologically inspired evolutionary algorithm called genetic programming (GP). GP, commonly known as symbolic regression, automatically evolves both the structure and the parameters of the considered mathematical model. The tension mode of failure is considered in the probabilistic analysis. The stochastic parameters involved in the study include internal friction angle of soil (ϕ) and the unit weight (γ). The results are validated using the Monte Carlo simulation (MCS) method, for the same set of parameters. This is done to scrutinize the efficiency and accuracy of the proposed method. A parametric study including the influence of coefficient of variation of ϕ on the probability of failure of GRRW is conducted. The results depict the efficiency and robustness of the proposed methodology. The genetic programming is a precise evolutionary method that delivers high performance in estimating the probability of failure of GRRW.
Probabilistic Analysis of a Geosynthetic Reinforced Soil Retaining Wall Under Seismic Conditions Using Genetic Programming
https://orcid.org/http://orcid.org/0000-0003-4599-2544
https://orcid.org/http://orcid.org/0000-0002-7514-8099
Reliability analysis of a geosynthetic reinforced retaining wall (GRRW) is performed under seismic conditions. GRRW is analysed deterministically using the horizontal slice method (HSM). To rule out the limitations of deterministic approach, a comprehensive probabilistic analysis is carried out using a biologically inspired evolutionary algorithm called genetic programming (GP). GP, commonly known as symbolic regression, automatically evolves both the structure and the parameters of the considered mathematical model. The tension mode of failure is considered in the probabilistic analysis. The stochastic parameters involved in the study include internal friction angle of soil (ϕ) and the unit weight (γ). The results are validated using the Monte Carlo simulation (MCS) method, for the same set of parameters. This is done to scrutinize the efficiency and accuracy of the proposed method. A parametric study including the influence of coefficient of variation of ϕ on the probability of failure of GRRW is conducted. The results depict the efficiency and robustness of the proposed methodology. The genetic programming is a precise evolutionary method that delivers high performance in estimating the probability of failure of GRRW.
Probabilistic Analysis of a Geosynthetic Reinforced Soil Retaining Wall Under Seismic Conditions Using Genetic Programming
Lecture Notes in Civil Engineering
Muthukkumaran, Kasinathan (Herausgeber:in) / Ayothiraman, R. (Herausgeber:in) / Kolathayar, Sreevalsa (Herausgeber:in) / Agarwal, Ekansh (Autor:in) / Verma, Ajeet Kumar (Autor:in) / Pain, Anindya (Autor:in) / Sarkar, Shantanu (Autor:in)
Indian Geotechnical Conference ; 2021 ; Trichy, India
Soil Dynamics, Earthquake and Computational Geotechnical Engineering ; Kapitel: 20 ; 217-226
01.01.2023
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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