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 (CAV5 and rrup) 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, CAV5 has the highest effect on PL. Also, the RSM and ANN models predict PL with considerable accuracy.
Highlights
Nowadays, it is crucially important for engineers and urban planners to take sustainability, livability, and social health into account when considering natural disasters
The liquefaction potential is expressed as a factor of safety (FS), which isTthhreouragthiodoefteCrRmRintiostCicSRm,ewthitohdosu, tthceonlisqidueerfiancgtiothnepuontecenrttiaailnitsyeaxspsroecsisaetdedaws aitfhatchtoerlooafdsainfegtyan(dFS), reswishtainchceisprtehdeicrtaitoinoso. fRCobReRrttsoonCaSnRd, wWirtihdoeu[t13c]ondsividideeridngthtehceounne cpeerntaeitnratytioansstoecsitarteesdulwt ritahngtheeinlotawdoing paratnsd, arnedsipstraensceentperdedtwicotioeqnus.aRtioobnesrftosronCRanRd: Wride [13] divided the cone penetration test result range in two parts, and presented two equations for cyclic shear strength (CRR): CRCRRCRR. 7R.5=7.=50=9.8303.8q31c3103N0q1,0cqs0c1100N30,0,+0CS0.+0+80f0o..00r555ff0oorr≤qqcq1c1NN,c,sc,s
They presented the following equation to estimate the potential for liquefaction (PL): ln[potential of liquefaction (PL)/1 − PL] = 13.6203 − 0.2820(N1)60,cs + 5.3265 ln(CSR7.5)
Summary
It is crucially important for engineers and urban planners to take sustainability, livability, and social health into account when considering natural disasters. Applying fuzzy neural network models to predict liquefaction, Rahman and Wang [40] considered nine parameters: magnitude of the earthquake (M), vertical total overburden pressure, vertical effective overburden pressure, qc1N value from CPT, acceleration ratio PGA/g, CSR, median grain diameter of the soil, critical depth of liquefaction, and water table depth. They combined fuzzy systems and NN to use both systems’ advantages: the learning and optimization ability of NN, and the human-like analysis of the fuzzy system to consider the meaning of high, very high, or low.
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