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Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine

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This study evaluated soft computing models for classifying earthquake-induced soil liquefaction. The Random Forest Classifier (RFC) model demonstrated superior predictive performance compared to Support Vector Machine models.

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Area of Science:

  • Geotechnical Engineering
  • Earthquake Engineering
  • Computational Intelligence

Background:

  • Earthquake-induced soil liquefaction is a significant georisk affecting geotechnical designs and structures globally.
  • Accurate classification of liquefaction potential is crucial for mitigating seismic hazards.

Purpose of the Study:

  • To evaluate the applicability of three soft computing models for classifying earthquake-induced liquefaction potential.
  • To compare the performance of Support Vector Machine (Polynomial and Radial Basis Function kernels) and Random Forest Classifier models.

Main Methods:

  • Utilized 234 datasets with twelve input parameters from liquefaction-prone environments.
  • Developed and optimized Support Vector Machine (SVM_Poly, SVM_RBK) and Random Forest Classifier (RFC) models through extensive trials and parameter tuning.
  • Employed a trial-and-error process to determine optimal user-defined parameters for each model.

Main Results:

  • All developed models showed promising performance with high accuracy (0.89), sensitivity (0.85), specificity (0.94), and precision (0.94).
  • The RFC model outperformed SVM_Poly and SVM_RBK, exhibiting a strong Phi Correlation Coefficient (0.82) and low error measures (MAE: 0.2351, RMSE: 0.3115).

Conclusions:

  • The Random Forest Classifier (RFC) is the most effective model for classifying earthquake-induced liquefaction potential among the evaluated soft computing methods.
  • The research highlights the potential of optimized soft computing models in accurately assessing seismic-related geotechnical risks.