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ORIGINAL RESEARCH article

Front. Built Environ.
Sec. Geotechnical Engineering
Volume 10 - 2024 | doi: 10.3389/fbuil.2024.1495472
This article is part of the Research Topic Advancement of Computational Mechanics in Geotechnical Engineering View all articles

A Smarter Approach to Liquefaction Risk: Harnessing Dynamic Cone Penetration Test Data and Machine Learning for Safer Infrastructure

Provisionally accepted
Shubhendu V. Singh Shubhendu V. Singh Sufyan Ghani Sufyan Ghani *
  • Sharda University, Greater Noida, India

The final, formatted version of the article will be published soon.

    This paper presents a novel approach for assessing liquefaction potential by integrating Dynamic Cone Penetration Test (DCPT) data with advanced machine learning (ML) techniques. DCPT offers a cost-effective, rapid, and adaptable method for evaluating soil resistance, making it suitable for liquefaction assessment across diverse soil conditions. This study establishes a threshold criterion based on the ratio of the penetration rate to the dynamic resistance (e/qd), where values exceeding 4 indicate high liquefaction susceptibility. ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. The SVM-PSO model demonstrated superior performance, with high R² values of 0.999 and 0.989 in the training and testing phases, respectively. The proposed methodology offers a sustainable and accurate approach for liquefaction assessment, reducing the environmental impact of geotechnical investigations, while ensuring reliable predictions. This study bridges the gap between field testing and advanced computational techniques, providing a powerful tool for geotechnical engineers to assess liquefaction risks and design resilient infrastructures.

    Keywords: Liquefaction risk, Dynamic cone penetration test (DCPT), machine learning, sustainable infrastructure, Seismic risk assessment, resilient infrastructure

    Received: 12 Sep 2024; Accepted: 14 Oct 2024.

    Copyright: © 2024 Singh and Ghani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sufyan Ghani, Sharda University, Greater Noida, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.