Enhancing soil liquefaction risk assessment with metaheuristics and hybrid learning techniques

ABSTRACT

Soil liquefaction has garnered significant research attention over several decades due to its profound impact on infrastructure systems in earthquake-prone regions. Given the limitations inherent in the assumptions and approximations of conventional methods, machine learning has emerged as a robust and efficient approach for predicting soil liquefaction potential. This study aims to develop a synergistic JS-CNN-XGB model, unifying the Jellyfish Search (JS) optimizer with a hybrid deep/machine learning model. This amalgamation leverages the feature extraction capabilities of the Convolutional Neural Network (CNN) alongside the classification prowess of the eXtreme Gradient Boosting (XGB) algorithm. The proposed model seamlessly integrates into a user-friendly prediction system, streamlining the liquefaction prediction process. Validation is achieved through case studies involving historical earthquake recordings with diverse classification ratios and varying input attribute quantities. Compared to existing studies, our proposed system showcases a notable 6.2% enhancement in overall liquefaction assessment accuracy, demonstrating improved detection capabilities in imbalanced and balanced datasets. In conclusion, this study underscores an automated system that presents a robust solution to the challenges posed by liquefaction potential assessment in geotechnical engineering.

KEYWORDS:

• Liquefaction potential assessment
• geohazards
• tabular-to-image transformation
• hybrid deep and machine learning
• jellyfish search optimizer
• automated prediction system

Acknowledgements

The authors would like to thank the National Science and Technology Council, Taiwan, for financially supporting this research under contract NSTC 110-2221-E-011-080-MY3.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Declaration of interest statement

We declare no known conflict of interest associated with this publication, and there has been no significant financial support for this work that could have influenced its outcome.

Data availability statement

The data and source code are currently available at https://www.researchgate.net/profile/Jui-Sheng-Chou/publications.

Additional information

Funding

This work was supported by National Science and Technology Council: [Grant Number: 110-2221-E-011-080-MY3].