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<p>Structures that are built on soft soil are more likely to be affected by earthquakes. Standard seismic assessment techniques do not take into account the interaction between the soil and the structure; as a result, the estimations of resilience are inaccurate. The Soil-Structure Interaction (SSI) analysis and Light Gradient Boosting Machine with pre-trained transformer embeddings are the two methods that are recommended for the prediction of significant seismic events in this paper. The numerical simulation framework and soil parameters associated with OpenSeesPy are included in this collection. Seismic Immediate Occupancy, Life Safety, and Collapse Prevention performance was categorized with the use of machine learning. There are a number of factors that influence earthquake performance, including soil stiffness, concrete modulus of elasticity, beam and column cross-sectional areas. The use of advanced machine learning in conjunction with SSI helps to enhance seismic evaluations, hence increasing earthquake safety and resilience.</p>

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Enhancing Seismic Resilience of Concrete Structures: A Light GBM Based Approach for the Soil-Structure Interaction

  • 期刊论文

  • 发表日期：

    2026年4月26日

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  • Narayan Malviya,
  • Ragavendra Singh

摘要

Structures that are built on soft soil are more likely to be affected by earthquakes. Standard seismic assessment techniques do not take into account the interaction between the soil and the structure; as a result, the estimations of resilience are inaccurate. The Soil-Structure Interaction (SSI) analysis and Light Gradient Boosting Machine with pre-trained transformer embeddings are the two methods that are recommended for the prediction of significant seismic events in this paper. The numerical simulation framework and soil parameters associated with OpenSeesPy are included in this collection. Seismic Immediate Occupancy, Life Safety, and Collapse Prevention performance was categorized with the use of machine learning. There are a number of factors that influence earthquake performance, including soil stiffness, concrete modulus of elasticity, beam and column cross-sectional areas. The use of advanced machine learning in conjunction with SSI helps to enhance seismic evaluations, hence increasing earthquake safety and resilience.