Earthquake Induced Liquefaction — Design & Simulation

Abstract — Earthquake Induced Liquefaction

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Analytical & Numerical Modeling Methodology

Earthquake-inducedliquefaction of soils poses a serious georisk in geotechnical designs, construction and the application of geotechnical structures around the world. In this study, the applicability of three soft computing models for liquefaction classification, a topic of significant importance within the fields of geotechnical and earthquake engineering has been evaluated. Twelve input parameters are used to classify the liquefaction potential for 234 data sets collected from an earthquake-induced liquefaction prone granular material environment. For developing the SVM_Poly, SVM_RBK models, an extensive number of trials were conducted using various combinations of C and d for polynomial kernels and C and ∂ for radial basis function kernel-based support vector machines (SVMs) utilizing user-defined parameters. In the same way, several experiments were conducted with a fixed value of C and ∂ kernel specific parameters in order to determine an appropriate value of error-insensitive zone (∋).Similarly, for the random forest classifier (RFC) model, the number of variables used (m) and the number of trees to be grown (k) are two user-defined parameters. These optimum values of m and k parameters are fixed using trial and error process and the same fixed values. The best model was developed as evidence from the confusion matrixes and statistical indicators. The calculated values of confusion matrixes and statistical indicators for training and testing shows that an accuracy of 0.89 indicates the model is correct in its predictions 89% of the time. A sensitivity of 0.85 signifies the model correctly identifies 85% of actual positive instances, while a specificity of 0.94 implies correct identification of 94% of actual negative instances. A precision of 0.94 suggests that when the model predicts a positive instance, it is correct 94% of the time. The Phi Correlation Coefficient, with a value of 0.82, indicates a strong positive correlation between predicted and actual values.Furthermore, the model exhibits a Mean Absolute Error (MAE) of 0.2351, reflecting a relatively low average error in predictions. The Root Mean Squared Error (RMSE) value of 0.3115 indicates better accuracy in predicting the target variable.Finally, all the developed models exhibit promising performance across various evaluation metrics, with low error measures (MAE and RMSE), high accuracy, and strong performance in correctly identifying both positive and negative instances, as evidenced by sensitivity and specificity. The high precision and Phi Correlation Coefficient further affirm the reliability and accuracy of the model’s predictions. However, among the three models FRC model is the best for classifying the RFC models using a comprehensive set of seismic and soil parameters to accurately classify earthquake-induced liquefaction potential, with the RFC model demonstrating superior predictive performance.

Finite Element Results & Code Compliance

Liquefaction of granular materials refers to a phenomenon where a saturated or partially saturated granular material, such as sand or silt, temporarily loses its strength and behaves like a liquid under stress, often as a result of seismic activity, such as an earthquake 1 . Liquefaction occurs when the pore water pressure within the soil increases, reducing the effective stress that holds the soil particles together 2 . Sands and silts with low cohesion are most susceptible due to the voids between soil particles which are filled with water, which can increase pressure during shaking 3 .Vibrations from an earthquake cause the soil to compact, which pushes water into the voids, leading to increased pore water pressure 4 .As pore water pressure rises, the ability of the particles to support weight (effective stress) diminishes 5 .Structures can settle, tilt, or collapse due to the loss of support from the liquefied soil 6 . Soil may behave like a fluid, causing landslides or flow of material 7 . Ground displacement can cause large, horizontal movements and excess water and sand may be ejected to the surface, forming mounds 8 . Techniques like vibro-compaction or dynamic compaction increase the density of the soil, reducing the likelihood of liquefaction 5 . Installing drainage systems helps reduce pore water pressure by allowing water to escape more easily 3 .

Scope & Practical Applications

• High-rise building and spatial frame analysis under seismic & wind loading
• Finite element continuum modeling in ABAQUS for non-linear material plasticity
• Classical lamination theory (CLT) and failure prediction for composite structures
• Spatial hazard mapping, landslide susceptibility, and catchment terrain analysis in QGIS
• Detailed bar bending schedules (BBS), structural drawings, and calculation booklets