Plasticity-Based Liquefaction Prediction Using Support Vector Machine and Adaptive Neuro-Fuzzy Inference System

Ghani, Sufyan; Kumari, Sudesh

doi:10.1007/978-981-19-6998-0_44

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…The synergy between fuzzy logic and neural networks in ANFIS provides a robust framework for capturing and interpreting intricate relationships in diverse datasets [61][62][63] . In ANFIS, membership functions determine how well input data belongs to different fuzzy sets.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…ANFIS dynamically adjusts its fuzzy inference system parameters through a learning mechanism, fostering adaptability to the underlying patterns in the sand-silt mixture data. Integrating fuzzy logic enhances interpretability, providing valuable insights into the rationale behind predictions (61)(62)(63). ANFIS-Sub introduces a sub-clustering technique to refine the clustering process within the dataset further.…”

Section: Anfis With Sub-clustering (Anfis-sub)mentioning

confidence: 99%

“…For example, in the case of K-Means sub-clustering, the centroid vj of cluster j can be updated using Eq. 8 (61)(62)(63). Here, vj represents the updated centroid of cluster j; N denotes the total number of data points.…”

Section: Anfis With Sub-clustering (Anfis-sub)mentioning

confidence: 99%

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Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures

Hameed,

Masood,

Srivast

et al. 2024

Preprint

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Liquefaction is a devastating consequence of earthquakes that occur in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameters is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines the Extreme Learning Machine (ELM) with the Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with 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 (24.9%), 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 to the ELM-DOA model, to aid engineers and researchers in effectively utilizing the 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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Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Section: Anfis With Sub-clustering (Anfis-sub)mentioning

confidence: 99%