Latin hypercube sampling normal distribution

Earthq Spectra 30(3):973вЂ“987Д«radley AB (2010) A generalized conditional intensity measure approach and holistic ground-motion selection. Earthq Spectra 24(1):99вЂ“138Д«ozorgnia Y, Abrahamson AA, Atik AL et al (2014) NGA-West2 research project. Bull Seismol Soc Am 99(6):3217вЂ“3233Д«oore MD, Atkinson AG (2008) Ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5%-damped PSA at spectral periods between 0.01 and 10.0вЂ‰s. Earthq Eng Struct Dyn 42:297вЂ“310Д«ommer JJ, Stafford JP, AlarcГіn EJ (2009) Empirical equations for the prediction of the significant, bracketed, and uniform duration of earthquake ground motion. Earthq Spectra 31(1):579вЂ“599Д«aradaran Shoraka M, Yang TY, Elwood KJ (2013) Seismic loss estimation of non-ductile reinforced concrete buildings. The outcomesВ of these latter studies demonstrated that the ground motionВ suite selected based on an average-intensity-measure conditioning criterionВ performed better than those selectedВ through single- and two-intensity-measure conditioning criteria.Д«aker JW (2015) Efficient analytical fragility function fitting using dynamic structural analysis. Using the selected suites, response history and subsequent damage/loss analyses were conducted on aВ generic 4-story non-ductile reinforced concrete building. Accordingly, it was utilized as an underlying peace of a broader ground motion selection framework to facilitite the selection of a number of ground motion suites based on different methods of conditioning. LHS was then utilized for sampling purposesВ from the conditional multivariate distributions, which in turn demonstrated superiority over MC given the same number of realization samples. To examine theВ efficiency of these twoВ distinct sampling methods, a set of conditional multivariate distributions was fit to an intensity measure vector based onВ a single, two, orВ average of more-than-two (average)В conditioning intensity measure. Latin Hypercube Sampling ( LHS) works by first stratifying a probability distribution domain into multiple equally spaced and non-overlapping stripes and then byВ permutationally drawing samples from those stripes. TheВ application of Latin Hypercube and Monte Carlo ( MC) sampling techniquesВ for ground motion selectionВ purposes is investigated.

Latin hypercube sampling normal distribution