First excursion probabilities for linear systems by very efficient importance sampling

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Bayesian system identification based on probability logic (2010, methodology): “Deﬁning failure as the maximum interstory drift of any story of the structure exceeding some threshold value, the posterior robust failure probability is calculated using a stochastic simulation algorithm similar to the extremely efﬁcient ISEE algorithm for linear dynamics [41].”
Structural model updating and prediction variability using Pareto optimal models (2008, methodology): “The failure probability is obtained using efficient stochastic simulation methods available for linear systems [ 38 ,39] for 2000 samples.”
Probability density evolution analysis of engineering structures via cubature points (2012, background): “the crude Monte Carlo [34] and various improved sampling methods [1].”
Uncertainty analysis of complex structural systems (2009, methodology): “First, an efficient Importance Sampling procedure to evaluate first excursion probabilities for deterministic linear systems subjected to Gaussian white noise excitation has been proposed in [2].”