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Mechanically Stabilized Earth Wall Reliability Analysis Using Response Surface Methodology, ANN, ANFIS and Multi-objective Genetic Algorithm

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Udgivet i:Periodica Polytechnica. Civil Engineering vol. 69, no. 1 (2025), p. 159
Hovedforfatter: Nasser SekfaliDepartment of Architecture, Faculty of Earth Science, Badji Mokhtar University, P. O. B. 12, 23000 Annaba, Algeria
Andre forfattere: Lafifi, Brahim
Udgivet:
Periodica Polytechnica, Budapest University of Technology and Economics
Fag:
Reliability analysis
Parameters
Mathematical analysis
Random variables
Algorithms
Artificial neural networks
Reliability
Soil mechanics
Mathematical models
Response surface methodology
Earth reinforcement
Multiple objective analysis
Variance analysis
Uncertainty
Limit states
Parameter identification
Adaptive systems
Genetic algorithms
Robust design
Optimization
Neural networks
Environmental
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Resumen:Considering uncertainty in the analysis of geotechnical structures is a necessary condition for optimal and robust design. An alternative method for studying the reliability of a mechanically reinforced earth wall in granular soil is used to account for these uncertainties more rigorously. This allows for the inclusion of various uncertainties in a mathematical risk formulation based on random variables. The deterministic model is a benchmark taken from the literature used in a numerical simulation to determine the maximum horizontal displacement of the wall. In this case, the serviceability limit state is considered, allowing the wall's actual behavior to be described. ANOVA was used to identify the most influential parameters on the system's response. As uncorrelated random variables, only the parameters (E, φ and γ) were considered. The mathematical model serving as the limit state function was numerically predictedby three methods, response surface methodology (RSM), artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), and their predictive capacities were then compared. The results showed that the ANN model outperformed the RSM and ANFIS models regarding prediction. ANN models and multi-objective genetic algorithm (MOGA) are used to optimize the Hasofer-Lind reliability index. The analysis is then carried out by taking into account the various types of functions of parameter distributions, which allowed us to better appreciate the effects of the uncertainties and identify the set of parameters with a high incidence.
ISSN:0553-6626
1587-3773
DOI:10.3311/PPci.22830
Fuente:Engineering Database