Probabilistic assessment of heavy-haul railway track using multi-gene genetic programming
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- @Article{BARDHAN:2024:apm,
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author = "Abidhan Bardhan",
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title = "Probabilistic assessment of heavy-haul railway track
using multi-gene genetic programming",
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journal = "Applied Mathematical Modelling",
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volume = "125",
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pages = "687--720",
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year = "2024",
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ISSN = "0307-904X",
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DOI = "doi:10.1016/j.apm.2023.08.009",
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URL = "https://www.sciencedirect.com/science/article/pii/S0307904X2300358X",
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keywords = "genetic algorithms, genetic programming, Railway
embankment, Reliability analysis, Bearing capacity,
Heavy-haul freight corridor, Slope/W modelling,
Artificial intelligence",
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abstract = "This study presented a probabilistic assessment of
heavy-haul railway track using a high-performance
computational model called multi-gene genetic
programming (MGGP). A reliability analysis (RA) method
based on MGGP and the first-order second-moment method
(FOSM) has been proposed in this study. First, GP was
used to map the implicit performance functions;
therefore, arriving at GP-based explicit performance
functions. Subsequently, the developed GP model was
used to perform RA of a soil slope of heavy-haul
railway track under both seismic and non-seismic
conditions. Using the FOSM, soil uncertainties were
mapped based on the concepts of probability theory and
statistics, and a ready-made expression was developed.
Simulated results demonstrate that the GP-based FOSM
approach can predict the probability of failure (POF)
of slope with rational accuracy. The probabilistic
analysis against bearing capacity failure was also
investigated in this study to ensure serviceability of
the soil slope. Based on the outcomes, it can be
deduced that the coefficient of variation of soil
properties affects the POF of slope significantly. With
the aid of the developed expression, the POF of the
soil slope of heavy-haul railway track can be assessed
rationally and efficiently",
- }
Genetic Programming entries for
Abidhan Bardhan
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