The Experts below are selected from a list of 54 Experts worldwide ranked by ideXlab platform

Michel Ghosn - One of the best experts on this subject based on the ideXlab platform.

  • modified subset simulation method for reliability analysis of structural systems
    Structural Safety, 2011
    Co-Authors: Feng Miao, Michel Ghosn
    Abstract:

    Abstract A “Regenerative Adaptive Subset Simulation” (RASS) method is proposed for performing the reliability analysis of complex structural systems. Proposed modifications to the classic subset simulation method include the implementation of advanced Markov Chain processes to combine the benefits of a Markov Chain regeneration process, a Delayed Rejection and Adaptive sample selection Algorithms and a Componentwise sampling model. The proposed modifications help to overcome the limitations of the original Metropolis–Hasting Algorithm used in the subset simulation which include the “burn-in problem” and the difficulty of the selection of the proposal probability function. Several illustrative examples are presented to demonstrate the efficiency of the proposed simulation and compare its results to those of other methods. The results show that RASS is robust and efficient in estimating the probability of failure of structural systems with complex failure regions, large numbers of random variables, and small probabilities of failure.

Feng Miao - One of the best experts on this subject based on the ideXlab platform.

  • modified subset simulation method for reliability analysis of structural systems
    Structural Safety, 2011
    Co-Authors: Feng Miao, Michel Ghosn
    Abstract:

    Abstract A “Regenerative Adaptive Subset Simulation” (RASS) method is proposed for performing the reliability analysis of complex structural systems. Proposed modifications to the classic subset simulation method include the implementation of advanced Markov Chain processes to combine the benefits of a Markov Chain regeneration process, a Delayed Rejection and Adaptive sample selection Algorithms and a Componentwise sampling model. The proposed modifications help to overcome the limitations of the original Metropolis–Hasting Algorithm used in the subset simulation which include the “burn-in problem” and the difficulty of the selection of the proposal probability function. Several illustrative examples are presented to demonstrate the efficiency of the proposed simulation and compare its results to those of other methods. The results show that RASS is robust and efficient in estimating the probability of failure of structural systems with complex failure regions, large numbers of random variables, and small probabilities of failure.

Herve Le Nagard - One of the best experts on this subject based on the ideXlab platform.

  • benefits of a new metropolis hasting based Algorithm in non linear regression for estimation of ex vivo antimalarial sensitivity in patients infected with two strains
    Computers in Biology and Medicine, 2014
    Co-Authors: Rebecca Bauer, Halima Kaddouri, Jacques Le Bras, Herve Le Nagard
    Abstract:

    Malaria is one of the world's most widespread parasitic diseases. The parasitic protozoans of the genus Plasmodium have developed resistance to several antimalarial drugs. Some patients are therefore infected by two or more strains with different levels of antimalarial drug sensitivity. We previously developed a model to estimate the drug concentration ( IC 50 ) that inhibits 50% of the growth of the parasite isolated from a patient infected with one strain. We propose here a new Two-Slopes model for patients infected by two strains. This model involves four parameters: the proportion of each strain and their IC50, and the sigmoidicity parameter. To estimate the parameters of this model, we have developed a new Algorithm called PGBO (Population Genetics-Based Optimizer). It is based on the Metropolis-Hasting Algorithm and is implemented in the statistical software R. We performed a simulation study and defined three evaluation criteria to evaluate its properties and compare it with three other Algorithms (Gauss-Newton, Levenberg-Marquardt, and a simulated annealing). We also evaluated it using in vitro data and three ex vivo datasets from the French Malaria Reference Center.Our evaluation criteria in the simulation show that PGBO gives good estimates of the parameters even if the concentration design is poor. Moreover, our Algorithm is less sensitive than Gauss-Newton Algorithms to initial values. Although parameter estimation is good, interpretation of the results can be difficult if the proportion of the second strain is close to 0 or 1. For these reasons, this approach cannot yet be implemented routinely. HighlightsWe model the antimalarial sensitivity in a blood sample with two strains of parasite.We develop a Metropolis-Hasting Algorithm to estimate parameters of the model.We compare our estimation results with three other Algorithms.We evaluate our Algorithm on a simulation study, on in vitro and ex vivo data.For poor concentration design, our Algorithm gives accurate results.

Tang W.h. - One of the best experts on this subject based on the ideXlab platform.

  • Back analysis of slope failure with Markov chain Monte Carlo simulation
    2010
    Co-Authors: Zhang L.l., Zhang J., Zhang L.m., Tang W.h.
    Abstract:

    Field observed performance of slopes can be used to back calculate input parameters of soil properties and evaluate uncertainty of a slope stability analysis model. In this paper, a new probabilistic method is proposed for back analysis of slope failure. The proposed back analysis method is formulated based on Bayes' theorem and solved using the Markov chain Monte Carlo simulation method with a Metropolis-Hasting Algorithm. The method is very flexible as any type of prior distribution can be used. The method is also computationally efficient when a response surface method is employed to approximate the slope stability model. An illustrative example of back analysis of a hypothetical slope failure is presented. Effects of jumping distribution functions and number of samples on the efficiency of Markov chains are studied. It is found that the covariance matrix of the jumping function can be set to be one half of the covariance of the prior distribution to achieve a reasonable acceptance rate and that 80,000 samples seem to be sufficient to obtain robust posterior statistics for the example. It is also found that the correlation of cohesion and friction angle of soil does not affect the posterior statistics and the remediation design of the slope significantly, while the type of the prior distribution seems to have much influence on the remediation design. © 2010 Elsevier Ltd

Salusti A. - One of the best experts on this subject based on the ideXlab platform.

  • Markov chain Monte Carlo Algorithms for target-oriented and interval-oriented amplitude versus angle inversions with non-parametric priors and non-linear forward modellings
    'Wiley', 2019
    Co-Authors: Aleardi M., Salusti A.
    Abstract:

    In geophysical inverse problems, the posterior model can be analytically assessed only in case of linear forward operators, Gaussian, Gaussian mixture, or generalized Gaussian prior models, continuous model properties, and Gaussian-distributed noise contaminating the observed data. For this reason, one of the major challenges of seismic inversion is to derive reliable uncertainty appraisals in cases of complex prior models, non-linear forward operators and mixed discrete-continuous model parameters. We present two amplitude versus angle inversion strategies for the joint estimation of elastic properties and litho-fluid facies from pre-stack seismic data in case of non-parametric mixture prior distributions and non-linear forward modellings. The first strategy is a two-dimensional target-oriented inversion that inverts the amplitude versus angle responses of the target reflections by adopting the single-interface full Zoeppritz equations. The second is an interval-oriented approach that inverts the pre-stack seismic responses along a given time interval using a one-dimensional convolutional forward modelling still based on the Zoeppritz equations. In both approaches, the model vector includes the facies sequence and the elastic properties of P-wave velocity, S-wave velocity and density. The distribution of the elastic properties at each common-mid-point location (for the target-oriented approach) or at each time-sample position (for the time-interval approach) is assumed to be multimodal with as many modes as the number of litho-fluid facies considered. In this context, an analytical expression of the posterior model is no more available. For this reason, we adopt a Markov chain Monte Carlo Algorithm to numerically evaluate the posterior uncertainties. With the aim of speeding up the convergence of the probabilistic sampling, we adopt a specific recipe that includes multiple chains, a parallel tempering strategy, a delayed rejection updating scheme and hybridizes the standard Metropolis–Hasting Algorithm with the more advanced differential evolution Markov chain method. For the lack of available field seismic data, we validate the two implemented Algorithms by inverting synthetic seismic data derived on the basis of realistic subsurface models and actual well log data. The two approaches are also benchmarked against two analytical inversion approaches that assume Gaussian-mixture-distributed elastic parameters. The final predictions and the convergence analysis of the two implemented methods proved that our approaches retrieve reliable estimations and accurate uncertainties quantifications with a reasonable computational effort