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• W4286509899 endingPage "100815" @default.
• W4286509899 startingPage "100815" @default.
• W4286509899 abstract "With the rising freight demand, specialized heavy-haul railway corridors allow heavier trains to transport heavy freight, improving productivity and lowering unit costs. Generally, a heavy-haul corridor necessitates a significant investment, thus the risk assessment of a rail-track system must be extensively evaluated during the design phase. From the standpoint of serviceability, this study presents a probabilistic slope stability analysis of heavy-haul freight corridor using an efficient hybrid computational technique. The present approach, i.e., ANN-MPA, is an amalgamation of an artificial neural network (ANN) and marine predators algorithm (MPA). The newly constructed ANN-MPA was used to perform probabilistic analysis of a 12.293 m high embankment of heavy-haul freight corridor of Indian Railways with a design axle load of 32.5 MT. The concept of probability theory and statistics were used to map the soil uncertainties through the first-order second-moment method. The results of the proposed ANN-MPA model were evaluated and compared with other hybrid ANNs constructed with seven distinct swarm intelligence algorithms. In the validation phase, the proposed ANN-MPA outperformed (R2 = 0.9931 and RMSE = 0.0233) other hybrid ANNs and was used to perform probabilistic analysis of a 12.293 m high embankment. The reliability index and the probability of failure were computed under seismic and non-seismic conditions, taking into consideration the influence of uncertainties in soil parameters. Using the proposed approach, the failure probability of the 12.293 m high soil slope under different seismic conditions can be evaluated rationally and efficiently." @default.
• W4286509899 created "2022-07-22" @default.
• W4286509899 creator A5004350213 @default.
• W4286509899 creator A5064414390 @default.
• W4286509899 date "2022-11-01" @default.
• W4286509899 modified "2023-10-05" @default.
• W4286509899 title "Probabilistic slope stability analysis of Heavy-haul freight corridor using a hybrid machine learning paradigm" @default.
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• W4286509899 doi "https://doi.org/10.1016/j.trgeo.2022.100815" @default.
• W4286509899 hasPublicationYear "2022" @default.
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