Journal of Geotechnical and Geoenvironmental Engineering · 2013 · 427 citations · 53 references
EngineeringCivil EngineeringEarth SciencesEarthquake EngineeringGeotechnical EngineeringSoil MechanicsGeotechnical Earthquake EngineeringGround Motion CharacterizationPerformance-based Earthquake EngineeringSeismic MonitoringShear Strength Of SoilsStructural ReliabilityLiquefaction AssessmentProbabilistic Seismic AssessmentSeismic DemandSeismically Induced Soil LiquefactionShear VelocitySoil LiquefactionSoil Liquefaction AssessmentStructural Reliability Assessment
Shear‑wave velocity (Vs) is a fundamental soil property used to assess seismic liquefaction resistance. The study reports an 11‑year international effort to collect Vs data and develop probabilistic correlations for seismic soil liquefaction. The authors measured Vs at 301 new liquefaction sites worldwide, merged the data with existing cases to create a 422‑entry catalog, and applied Bayesian regression and structural reliability analysis to produce probabilistic liquefaction correlations. The new catalog confirms that most cases overlap prior penetration tests, and the probabilistic analysis reduces overall uncertainty while addressing adjustments for soil fines and magnitude scaling.
Shear-wave velocity (Vs) offers a means to determine the seismic resistance of soil to liquefaction by a fundamental soil property. This paper presents the results of an 11-year international project to gather new Vs site data and develop probabilistic correlations for seismic soil liquefaction occurrence. Toward that objective, shear-wave velocity test sites were identified, and measurements made for 301 new liquefaction field case histories in China, Japan, Taiwan, Greece, and the United States over a decade. The majority of these new case histories reoccupy those previously investigated by penetration testing. These new data are combined with previously published case histories to build a global catalog of 422 case histories of Vs liquefaction performance. Bayesian regression and structural reliability methods facilitate a probabilistic treatment of the Vs catalog for performance-based engineering applications. Where possible, uncertainties of the variables comprising both the seismic demand and the soil capacity were estimated and included in the analysis, resulting in greatly reduced overall model uncertainty relative to previous studies. The presented data set and probabilistic analysis also help resolve the ancillary issues of adjustment for soil fines content and magnitude scaling factors.
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