Development of a rule-based approach for the dynamic behavior classification of soils during earthquakes

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ID: 285095
2025
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Abstract
Soil liquefaction during earthquakes poses significant risks to urban infrastructure, leading to ground instability, structural damage, and economic losses. Traditional liquefaction prediction methods fail to capture the complex, non-linear interactions between seismic and soil parameters, limiting their accuracy and practical application. This study aimed to develop rule-based models for assessing soil liquefaction potential using historical case histories to address these challenges. The research employed rough set machine learning (RSML) as a viable tool to analyze case history datasets and experimental data, generating interpretable rules for six liquefaction RSML models. Model validation involved sensitivity analysis, theory-driven interpretation of rules, and comparisons with traditional liquefaction models, confirming the robustness of the proposed framework. A decision support tool was developed to translate the findings into actionable insights, featuring user-friendly interfaces for practical application. The results demonstrated high prediction accuracies ranging from 70.9% to 96.5%, validating the RSML models’ reliability and interpretability. Scenario maps and parameter interaction charts enhanced the practical understanding of soil behavior under seismic loading. Key insights included the discovery of threshold effects among grain size distribution parameters and the influence of fines content on cyclic softening, challenging traditional assumptions about soil liquefaction resistance. The study concludes that RSML provides a robust and interpretable framework for liquefaction assessment, outperforming conventional models in both accuracy and interpretability. The decision support tool developed in this study bridges the gap between complex machine learning models and practical engineering applications, providing engineers and decision-makers a supplementary tool in seismic risk mitigation efforts.
Reference Key
persistent_1760654606_68f1750ea7340 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Torres, Emerzon Santos
Journal Malay Journal
Year 2025
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdd_civ/9
Keywords Keywords not found

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