Development of a rule-based approach for the dynamic behavior classification of soils during earthquakes
Date of Publication
1-15-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Civil Engineering
Subject Categories
Civil Engineering | Engineering | Structural Engineering
College
Gokongwei College of Engineering
Department/Unit
Civil Engineering
Thesis Advisor
Jonathan R. Dungca
Defense Panel Chair
Mary Ann Q. Adajar
Defense Panel Member
Erica Elice S. Uy
Andres Winston C. Oreta
Lessandro Estelito O. Garciano
Raymond Girard R. Tan
Abstract/Summary
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.
Abstract Format
html
Language
English
Format
Electronic
Keywords
Soil liquefaction; Earthquake engineering
Recommended Citation
Torres, E. S. (2025). Development of a rule-based approach for the dynamic behavior classification of soils during earthquakes. Retrieved from https://animorepository.dlsu.edu.ph/etdd_civ/9
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Embargo Period
4-3-2025