Evaluation of soil liquefaction potential based on the nonlinear energy dissipation principles

Yie Ruey Chen, Jing-Wen Chen, Shun Chieh Hsieh, Yi Teng Chang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This study presents the principle of nonlinear energy dissipation using an artificial neural network to assess liquefaction potential. The nonlinear relationship between an increase in pore water pressure and the dissipation of seismic energy observed in test data from triaxial shear testing of saturated sand was used to calculate the hysteresis loop energy required to trigger liquefaction. Data recorded during the 1999 Chi-Chi earthquake in Taiwan were utilized to validate the proposed ANN-based hysteresis loop energy model. Results show that the concept of using hysteresis loop energy and the proposed neural network are capable of effectively assessing liquefaction potential.

Original languageEnglish
Pages (from-to)54-72
Number of pages19
JournalJournal of Earthquake Engineering
Volume17
Issue number1
DOIs
Publication statusPublished - 2013 Feb 26

Fingerprint

Soil liquefaction
Liquefaction
Hysteresis loops
energy dissipation
liquefaction
Energy dissipation
hysteresis
Neural networks
energy
soil
Chi-Chi earthquake 1999
Earthquakes
Sand
artificial neural network
dissipation
porewater
Testing
sand
evaluation
Water

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Geotechnical Engineering and Engineering Geology

Cite this

Chen, Yie Ruey ; Chen, Jing-Wen ; Hsieh, Shun Chieh ; Chang, Yi Teng. / Evaluation of soil liquefaction potential based on the nonlinear energy dissipation principles. In: Journal of Earthquake Engineering. 2013 ; Vol. 17, No. 1. pp. 54-72.
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Evaluation of soil liquefaction potential based on the nonlinear energy dissipation principles. / Chen, Yie Ruey; Chen, Jing-Wen; Hsieh, Shun Chieh; Chang, Yi Teng.

In: Journal of Earthquake Engineering, Vol. 17, No. 1, 26.02.2013, p. 54-72.

Research output: Contribution to journalArticle

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