Optimizing Retaining Walls through Reinforcement Learning Approaches and Metaheuristic Techniques

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dc.contributor.authorLemus-Romani, Jose
dc.contributor.authorOssandon, Diego
dc.contributor.authorSepulveda, Rocio
dc.contributor.authorCarrasco-Astudillo, Nicolas
dc.contributor.authorYepes, Victor
dc.contributor.authorGarcia, Jose
dc.date.accessioned2025-01-20T20:14:23Z
dc.date.available2025-01-20T20:14:23Z
dc.date.issued2023
dc.description.abstractThe structural design of civil works is closely tied to empirical knowledge and the design professional's experience. Based on this, adequate designs are generated in terms of strength, operability, and durability. However, such designs can be optimized to reduce conditions associated with the structure's design and execution, such as costs, CO2 emissions, and related earthworks. In this study, a new discretization technique based on reinforcement learning and transfer functions is developed. The application of metaheuristic techniques to the retaining wall problem is examined, defining two objective functions: cost and CO2 emissions. An extensive comparison is made with various metaheuristics and brute force methods, where the results show that the S-shaped transfer functions consistently yield more robust outcomes.
dc.fuente.origenWOS
dc.identifier.doi10.3390/math11092104
dc.identifier.eissn2227-7390
dc.identifier.urihttps://doi.org/10.3390/math11092104
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/92192
dc.identifier.wosidWOS:000987314700001
dc.issue.numero9
dc.language.isoen
dc.revistaMathematics
dc.rightsacceso restringido
dc.subjectmetaheuristics
dc.subjectconcrete retaining walls
dc.titleOptimizing Retaining Walls through Reinforcement Learning Approaches and Metaheuristic Techniques
dc.typeartículo
dc.volumen11
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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