Rare event simulation in finite-infinite dimensional space

Modern engineering systems are becoming increasingly complex. Assessing their risk by simulation is intimately related to the efficient generation of rare failure events. Subset Simulation is an advanced Monte Carlo method for risk assessment and it has been applied in different disciplines. Pivotal to its success is the efficient generation of conditional failure samples, which is generally non-trivial. Conventionally an independent-component Markov Chain Monte Carlo (MCMC) algorithm is used, which is applicable to high dimensional problems (i.e., a large number of random variables) without suffering from ‘curse of dimension’. Experience suggests that the algorithm may perform even better for high dimensional problems. Motivated by this, for any given problem we construct an equivalent problem where each random variable is represented by an arbitrary (hence possibly infinite) number of ‘hidden’ variables. We study analytically the limiting behavior of the algorithm as the number of hidden variables increases indefinitely. This leads to a new algorithm that is more generic and offers greater flexibility and control. It coincides with an algorithm recently suggested by independent researchers, where a joint Gaussian distribution is imposed between the current sample and the candidate. The present work provides theoretical reasoning and insights into the algorithm

Assessment of optimal design methods of viscous dampers

Viscous dampers are often used for seismic protection and performance enhancement of building frames. The optimal design of such devices requires the modelling and propagation of the uncertainties related to the earthquake hazard. Different approaches are available for the seismic input characterisation and for the probabilistic response evaluation. This work analyzes the effect of different characterizations of the seismic input and of the response evaluation on the design of dampers for building frames. The seismic input is represented as a stochastic process and the optimal damper properties are found via a reliability-based design procedure aiming at controlling the frame performance while limiting the damper cost. Two simplified approaches are used to design the viscous damper of a multi-storey steel frame and the design results are compared with those obtained by considering a rigorous design approach resorting to advanced simulations for the response assessment. The first methodology evaluates the response through a prefixed probabilistic demand model, while the second approach considers the average response for a given hazard level only. The comparison allows to evaluate and quantify the effect of the seismic input uncertainty treatment on the system and damper performances

Dynamic safety assessment of a nonlinear pumped-storage generating system in a transient process

This paper focuses on a pumped-storage generating system with a reversible Francis turbine and presents an innovative framework for safety assessment in an attempt to overcome their limitations. Thus the aim is to analyze the dynamic safety process and risk probability of the above nonlinear generating system. This study is carried out based on an existing pumped-storage power station. In this paper we show the dynamic safety evaluation process and risk probability of the nonlinear generating system using Fisher discriminant method. A comparison analysis for the safety assessment is performed between two different closing laws, namely the separate mode only to include a guide vane and the linkage mode that includes a guide vane and a ball valve. We find that the most unfavorable condition of the generating system occurs in the final stage of the load rejection transient process. It is also demonstrated that there is no risk to the generating system with the linkage mode but the risk probability of the separate mode is 6 percent. The results obtained are in good agreement with the actual operation of hydropower stations. The developed framework may not only be adopted for the applications of the pumped-storage generating system with a reversible Francis turbine but serves as the basis for the safety assessment of various engineering applications.National Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesScientific research funds of Northwest A&F UniversityScience Fund for Excellent Young Scholars from Northwest A&F University and Shaanxi Nova progra

Robust artificial neural network for reliability analysis

Artificial Neural Networks (ANN) are used in place of expensive models to reduce the computational burden required for reliability analysis. Often, ANNs with selected architecture are trained with the back-propagation algorithm from few data representatives of the input/output relationship of the underlying model of interest. However, different performing ANNs might be obtained from the same training data, leading to an uncertainty in selecting the best performing ANN. On the other hand, using cross-validation to select the best performing ANN based on the highest R2 value can lead to a biassing in terms of the prediction made by the selected ANN. This is due to the fact that the use of R2 cannot determine if the prediction made by ANN is biased. Additionally, R2 does not indicate if a model is adequate, as it is possible to have a low R2 for a good model and a high R2 for a bad model. Hence we propose an approach to improve the prediction robustness of an ANN based on coupling Bayesian framework and model averaging technique into a unified framework. The model uncertainties propagated to the robust prediction is quantified in terms of confidence intervals. Two examples are used to demonstrate the applicability of the approach

An efficient method for estimating conditional failure probabilities

Conditional reliability measures provide a more detailed description of the performance of a system, being representative of different initial configurations. Commonly, since the failure region is characterized by a small probability of occurrence, advanced sampling techniques are required to reduce the computational effort of a simulation based approach. These techniques if on one hand decrease the number of samples needed to identify the failure domain, on the other hand do not generally allow a direct estimation of the conditional failure probability for different given inputs. This study aims at providing an efficient and simple methodology to evaluate the conditional failure probability in the case of a static reliability analysis. In particular, under the assumption of probability density functions (PDFs) with a finite support, the failure region mapping process is carried out using surrogate PDFs associated with Sobol’ sequences in order to reduce as much as possible the model evaluations. Finally, the integration of the failure region in the standard normal space employs probabilistic weights instead of a classic indicator function to account for the uncertainty associated with the failure region definition. The approach is verified by comparing the results against those obtained from a Latin Hypercube Sampling. The performance of the proposed method is evaluated in terms of computational costs and accurac

Reliability-based optimal design of nonlinear viscous dampers for the seismic protection of structural systems

Viscous dampers are widely employed for enhancing the seismic performance of structural systems, and their design is often carried out using simplified approaches to account for the uncertainty in the seismic input. This paper introduces a novel and rigorous approach that allows to explicitly consider the variability of the intensity and characteristics of the seismic input in designing the optimal viscous constant and velocity exponent of the dampers based on performance-based criteria. The optimal solution permits controlling the probability of structural failure, while minimizing the damper cost, related to the sum of the damper forces. The solution to the optimization problem is efficiently sought via the constrained optimization by linear approximation (COBYLA) method, while Subset simulation together with auxiliary response method are employed for the performance assessment at each iteration of the optimization process. A 3-storey steel moment-resisting building frame is considered to illustrate the application of the proposed design methodology and to evaluate and compare the performances that can be achieved with different damper nonlinearity levels. Comparisons are also made with the results obtained by applying simplifying approaches, often employed in design practice, as those aiming to minimize the sum of the viscous damping constant and/or considering a single hazard level for the performance assessment

Adaptive multilevel subset simulation with selective refinement

In this work we propose an adaptive multilevel version of subset simulation to estimate the probability of rare events for complex physical systems. Given a sequence of nested failure domains of increasing size, the rare event probability is expressed as a product of conditional probabilities. The proposed new estimator uses different model resolutions and varying numbers of samples across the hierarchy of nested failure sets. In order to dramatically reduce the computational cost, we construct the intermediate failure sets such that only a small number of expensive high-resolution model evaluations are needed, whilst the majority of samples can be taken from inexpensive low-resolution simulations. A key idea in our new estimator is the use of a posteriori error estimators combined with a selective mesh refinement strategy to guarantee the critical subset property that may be violated when changing model resolution from one failure set to the next. The efficiency gains and the statistical properties of the estimator are investigated both theoretically via shaking transformations, as well as numerically. On a model problem from subsurface flow, the new multilevel estimator achieves gains of more than a factor 200 over standard subset simulation for a practically relevant relative error of 25%

Non-intrusive stochastic analysis with parameterized imprecise probability models: II. Reliability and rare events analysis

© 2019 Elsevier Ltd Structural reliability analysis for rare failure events in the presence of hybrid uncertainties is a challenging task drawing increasing attentions in both academic and engineering fields. Based on the new imprecise stochastic simulation framework developed in the companion paper, this work aims at developing efficient methods to estimate the failure probability functions subjected to rare failure events with the hybrid uncertainties being characterized by imprecise probability models. The imprecise stochastic simulation methods are firstly improved by the active learning procedure so as to reduce the computational costs. For the more challenging rare failure events, two extended subset simulation based sampling methods are proposed to provide better performances in both local and global parameter spaces. The computational costs of both methods are the same with the classical subset simulation method. These two methods are also combined with the active learning procedure so as to further substantially reduce the computational costs. The estimation errors of all the methods are analyzed based on sensitivity indices and statistical properties of the developed estimators. All these new developments enrich the imprecise stochastic simulation framework. The feasibility and efficiency of the proposed methods are demonstrated with numerical and engineering test examples