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A New Equation to Evaluate Liquefaction Triggering Using the Response Surface Method and Parametric Sensitivity Analysis

Author

Listed:
  • Nima Pirhadi

    (State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China)

  • Xiaowei Tang

    (State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China)

  • Qing Yang

    (State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China)

  • Fei Kang

    (State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China)

Abstract

Liquefaction is one of the most damaging functions of earthquakes in saturated sandy soil. Therefore, clearly advancing the assessment of this phenomenon is one of the key points for the geotechnical profession for sustainable development. This study presents a new equation to evaluate the potential of liquefaction (PL) in sandy soil. It accounts for two new earthquake parameters: standardized cumulative absolute velocity and closest distance from the site to the rupture surface ( CAV 5 and r rup ) to the database. In the first step, an artificial neural network (ANN) model is developed. Additionally, a new response surface method (RSM) tool that shows the correlation between the input parameters and the target is applied to derive an equation. Then, the RSM equation and ANN model results are compared with those of the other available models to show their validity and capability. Finally, according the uncertainty in the considered parameters, sensitivity analysis is performed through Monte Carlo simulation (MCS) to show the effect of the parameters and their uncertainties on PL. The main advantage of this research is its consideration of the direct influence of the most important parameters, particularly earthquake characteristics, on liquefaction, thus making it possible to conduct parametric sensitivity analysis and show the direct impact of the parameters and their uncertainties on the PL. The results indicate that among the earthquake parameters, CAV 5 has the highest effect on PL. Also, the RSM and ANN models predict PL with considerable accuracy.

Suggested Citation

  • Nima Pirhadi & Xiaowei Tang & Qing Yang & Fei Kang, 2018. "A New Equation to Evaluate Liquefaction Triggering Using the Response Surface Method and Parametric Sensitivity Analysis," Sustainability, MDPI, vol. 11(1), pages 1-24, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2018:i:1:p:112-:d:193218
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