Assessment of Soil Liquefaction Potential Prediction Using Synthetic Data and Soft Computing Techniques

Liquefaction prediction using conventional approaches often relies on empirical correlations and involves costly, time-consuming field studies. Using large databases of post-liquefaction observations, machine learning methods have recently been developed to evaluate liquefaction potential. However, the availability of adequate real-world data limits the efficacy of these approaches. This study investigates the efficacy of the deep learning technique, i.e., Conditional Tabular Generative Adversarial Networks (CTGAN), for generating synthetic data. A comparison between original and synthetic data is made based on the absolute log mean, numeric data standard deviation, cumulative sums per feature, a correlation matrix, principal component analysis (PCA), distributional features and p-values from the Kolmogorov–Smirnov (KS) test. It is found that the synthetic data statistically resembles the original, making it viable for developing predictive models. There is a notable increase in the accuracy of liquefaction predictions when using 10,000 synthetic datasets generated from 288 original datasets. The synthetic data outperforms the original datasets across various machine learning methods, including Logistic Regression, Random Forest, SVM, KNN, and Decision Tree, with improvements in liquefaction classification accuracy of 89%, 98%, 92%, 98%, and 98%, respectively.

• Record URL:
  • https://doi.org/10.1007/s40515-026-00818-6
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/21967202
• Supplemental Notes:
  • © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.
• Authors:
  • Naik, Jajati Keshari
  • Muduli, Pradyut Kumar
  • Behera, Gopal Charan
• Publication Date: 2026-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 38
• Serial:
  • Transportation Infrastructure Geotechnology
  • Volume: 13
  • Issue Number: 2
  • Publisher: Springer Publishing
  • ISSN: 2196-7202
  • EISSN: 2196-7210
  • Serial URL: http://link.springer.com/journal/40515

Subject/Index Terms

• TRT Terms: Deep learning; Geotechnical engineering; Liquefaction; Predictive models; Soft computing; Soils
• Subject Areas: Data and Information Technology; Geotechnology; Transportation (General);

Filing Info

• Accession Number: 01979851
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 18 2026 12:00PM