Energy-based approach to study liquefaction triggering in homogeneous and stratified soils under consolidated undrained cyclic loading

Jain, Arpit; Mittal, Satyendra; Shukla, Sanjay Kumar

doi:10.1016/j.enggeo.2023.107151

Cited by 6 publications

(1 citation statement)

References 49 publications

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“…The energy-based approach focuses on the energy imparted by seismic waves and compares it with the energy dissipated by the soil during cyclic loading. This method considers factors such as stress–strain hysteresis loops to assess liquefaction susceptibility 12 – 14 . There are techniques like the empirical methods, which rely on observed correlations between liquefaction and factors such as standard penetration test (SPT) N-values or shear wave velocity 15 – 17 .…”

Section: Introductionmentioning

confidence: 99%

Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures

Hameed,

Masood,

Srivastava

et al. 2024

Sci Rep

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Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

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