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An Efficient Optimization-Based Approach to Determine Benchmark Failure Probability in Slope Reliability
https://orcid.org/https://orcid.org/0000-0002-8784-9458
This study introduces an efficient approach to evaluate benchmark failure probabilities in slope reliability. Particle swarm optimization (PSO) and the firefly algorithm (FA) are compared to determine the most effective strategy for slope stability analysis. It was found that, in FA, the movements of fireflies are adjusted based on the performance of other fireflies, resulting in a computational burden of approximately five times greater than that of PSO. Consequently, PSO was integrated into Monte Carlo simulations for system reliability assessments. Three case studies on system slope reliability were rigorously analyzed, with soil properties modeled as either random variables or random fields. The results obtained using the proposed method showed strong agreement with those from the traditional method and existing literature, affirming its accuracy. Notably, a fivefold reduction in the number of failure surfaces requiring evaluation was achieved when soil properties were treated as random variables. Furthermore, by accounting for spatial variability in soil—thereby increasing problem complexity—the analysis was accelerated by a factor of ten, highlighting the remarkable efficiency of the proposed approach.
An Efficient Optimization-Based Approach to Determine Benchmark Failure Probability in Slope Reliability
https://orcid.org/https://orcid.org/0000-0002-8784-9458
This study introduces an efficient approach to evaluate benchmark failure probabilities in slope reliability. Particle swarm optimization (PSO) and the firefly algorithm (FA) are compared to determine the most effective strategy for slope stability analysis. It was found that, in FA, the movements of fireflies are adjusted based on the performance of other fireflies, resulting in a computational burden of approximately five times greater than that of PSO. Consequently, PSO was integrated into Monte Carlo simulations for system reliability assessments. Three case studies on system slope reliability were rigorously analyzed, with soil properties modeled as either random variables or random fields. The results obtained using the proposed method showed strong agreement with those from the traditional method and existing literature, affirming its accuracy. Notably, a fivefold reduction in the number of failure surfaces requiring evaluation was achieved when soil properties were treated as random variables. Furthermore, by accounting for spatial variability in soil—thereby increasing problem complexity—the analysis was accelerated by a factor of ten, highlighting the remarkable efficiency of the proposed approach.
An Efficient Optimization-Based Approach to Determine Benchmark Failure Probability in Slope Reliability
https://orcid.org/https://orcid.org/0000-0002-8784-9458
This study introduces an efficient approach to evaluate benchmark failure probabilities in slope reliability. Particle swarm optimization (PSO) and the firefly algorithm (FA) are compared to determine the most effective strategy for slope stability analysis. It was found that, in FA, the movements of fireflies are adjusted based on the performance of other fireflies, resulting in a computational burden of approximately five times greater than that of PSO. Consequently, PSO was integrated into Monte Carlo simulations for system reliability assessments. Three case studies on system slope reliability were rigorously analyzed, with soil properties modeled as either random variables or random fields. The results obtained using the proposed method showed strong agreement with those from the traditional method and existing literature, affirming its accuracy. Notably, a fivefold reduction in the number of failure surfaces requiring evaluation was achieved when soil properties were treated as random variables. Furthermore, by accounting for spatial variability in soil—thereby increasing problem complexity—the analysis was accelerated by a factor of ten, highlighting the remarkable efficiency of the proposed approach.
An Efficient Optimization-Based Approach to Determine Benchmark Failure Probability in Slope Reliability
Transp. Infrastruct. Geotech.
Doan, Nhu Son (author)
2025-01-01
Article (Journal)
Electronic Resource
English
A Benchmark Slope for System Reliability Analysis
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A Benchmark Slope For System Reliability Analysis
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Reliability analysis of a benchmark problem for slope stability
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Probability and Risk of Slope Failure
| British Library Online Contents | 2008