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

Nima Pirhadi, Xiaowei Tang, Qing Yang and Fei Kang
Additional contact information
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

Sustainability, 2018, vol. 11, issue 1, 1-24

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.

Keywords: liquefaction; response surface method; artificial neural network; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/1/112/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/1/112/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2018:i:1:p:112-:d:193218

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().