Mechanically Stabilized Earth Wall Reliability Analysis Using Response Surface Methodology, ANN, ANFIS and Multi-objective Genetic Algorithm
Αποθηκεύτηκε σε:
| Εκδόθηκε σε: | Periodica Polytechnica. Civil Engineering vol. 69, no. 1 (2025), p. 159 |
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| Κύριος συγγραφέας: | |
| Άλλοι συγγραφείς: | |
| Έκδοση: |
Periodica Polytechnica, Budapest University of Technology and Economics
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| Διαθέσιμο Online: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.3311/PPci.22830 |2 doi | |
| 035 | |a 3175289103 | ||
| 045 | 2 | |b d20250101 |b d20250331 | |
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| 100 | 1 | |a Nasser SekfaliDepartment of Architecture, Faculty of Earth Science, Badji Mokhtar University, P. O. B. 12, 23000 Annaba, Algeria | |
| 245 | 1 | |a Mechanically Stabilized Earth Wall Reliability Analysis Using Response Surface Methodology, ANN, ANFIS and Multi-objective Genetic Algorithm | |
| 260 | |b Periodica Polytechnica, Budapest University of Technology and Economics |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Reliability analysis | ||
| 653 | |a Parameters | ||
| 653 | |a Mathematical analysis | ||
| 653 | |a Random variables | ||
| 653 | |a Algorithms | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Reliability | ||
| 653 | |a Soil mechanics | ||
| 653 | |a Mathematical models | ||
| 653 | |a Response surface methodology | ||
| 653 | |a Earth reinforcement | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Variance analysis | ||
| 653 | |a Uncertainty | ||
| 653 | |a Limit states | ||
| 653 | |a Parameter identification | ||
| 653 | |a Adaptive systems | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Robust design | ||
| 653 | |a Optimization | ||
| 653 | |a Neural networks | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Lafifi, Brahim | |
| 773 | 0 | |t Periodica Polytechnica. Civil Engineering |g vol. 69, no. 1 (2025), p. 159 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3175289103/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3175289103/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |