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The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
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Updated: Dec 28, 2025

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Data on a coupled ENN / t-SNE model for soil liquefaction evaluation.

Pierre Guy Atangana Njock1, Shui-Long Shen2,3, Annan Zhou3

  • 1Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang District, Shanghai, 200240, China.

Data in Brief
|February 15, 2020
PubMed
Summary

This study analyzes 253 cone penetration test (CPT) records from earthquake-prone regions to evaluate soil liquefaction potential. An AI model accurately predicted liquefaction based on field data and key soil variables.

Keywords:
CPTDatabaseLiquefactionNeural network

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Area of Science:

  • Geotechnical Engineering
  • Earthquake Engineering
  • Artificial Intelligence in Geosciences

Background:

  • Soil liquefaction is a critical hazard in earthquake-prone areas, impacting infrastructure stability.
  • Accurate assessment of liquefaction potential is crucial for seismic risk mitigation and urban planning.
  • Existing methods for liquefaction assessment often require extensive data and complex analysis.

Purpose of the Study:

  • To compile and present a comprehensive database of field case records for soil liquefaction potential.
  • To evaluate the performance of an evolutionary neural network (ENN) model for predicting soil liquefaction.
  • To compare AI-based predictions with actual field observations of liquefaction.

Main Methods:

  • Compilation of 253 cone penetration test (CPT) records from 219 sites globally.
  • Inclusion of 10 principal variables: earthquake magnitude, ground acceleration, depth, water depth, stresses, CPT tip resistance, friction ratio, fines content, and shear stress ratio.
  • Utilizing an evolutionary neural network (ENN) coupled with t-distributed Stochastic Neighbor Embedding (t-SNE) for predictive modeling, with data split into training (200 cases) and testing (53 cases) sets.

Main Results:

  • The developed ENN model demonstrated strong predictive capabilities for soil liquefaction potential.
  • Field observations were compared against model predictions, showing good agreement.
  • The study successfully validated the effectiveness of AI technology in evaluating soil liquefaction.

Conclusions:

  • The integrated ENN/t-SNE model offers a robust and efficient approach for assessing soil liquefaction potential.
  • The compiled database serves as a valuable resource for future research in geotechnical earthquake engineering.
  • AI-driven methods show significant promise for improving the accuracy and reliability of seismic hazard assessments.