Subset Simulation Algorithm Research Articles

An effective implementation of reliability methods for Bayesian model updating of structural dynamic models with multiple uncertain parameters

The implementation of reliability methods in the framework of Bayesian model updating of structural dynamic models using measured responses is explored for high-dimensional model parameter spaces. The formulation relies on a recently established analogy between Bayesian updating problems and reliability problems. Under this framework, samples following the posterior distribution of the Bayesian model updating problem can be obtained as failure samples in an especially devised reliability problem. An approach that requires only minimal modifications to the standard subset simulation algorithm is proposed and implemented. The scheme uses an adaptive strategy to select the threshold value that determines the last subset level. Due to the basis of the formulation, the approach does not make use of any problem-specific information and, therefore, any type of structural model can be considered. The approach is combined with an efficient parametric model reduction technique for an effective numerical implementation. The performance of the proposed implementation is assessed numerically for a linear building model and a nonlinear three-dimensional bridge structural model. The results indicate that the proposed implementation represents an effective numerical technique to address high-dimensional Bayesian model updating problems involving complex structural dynamic models.

An adaptive subset simulation algorithm for system reliability analysis with discontinuous limit states

Many system reliability problems involve performance functions with a discontinuous distribution. Such situations occur in both connectivity- and flow-based network reliability problems, due to binary or multi-state random variables entering the definition of the system performance or due to the discontinuous nature of the system model. When solving this kind of problems, the standard subset simulation algorithm with fixed intermediate conditional probability and fixed number of samples per level can lead to substantial errors, since the discontinuity of the output can result in an ambiguous definition of the sought percentile of the samples and, hence, of the intermediate domains. In this paper, we propose an adaptive subset simulation algorithm to determine the reliability of systems whose performance function is a discontinuous random variable. The proposed algorithm chooses the number of samples and the intermediate conditional probabilities adaptively. We discuss two MCMC algorithms for generation of the samples in the intermediate domains, the adaptive conditional sampling method and a novel independent Metropolis–Hastings algorithm that efficiently samples in discrete input spaces. The accuracy and efficiency of the proposed algorithm are demonstrated by a set of numerical examples.

Pricing discretely-monitored double barrier options with small probabilities of execution

In this paper, we propose a new stochastic simulation-based methodology for pricing discretely-monitored double barrier options and estimating the corresponding probabilities of execution. We develop our framework by employing a versatile tool for the estimation of rare event probabilities known as subset simulation algorithm. In this regard, considering plausible dynamics for the price evolution of the underlying asset, we are able to compare and demonstrate clearly that our treatment always outperforms the standard Monte Carlo approach and becomes substantially more efficient (measured in terms of the sample coefficient of variation) when the underlying asset has high volatility and the barriers are set close to the spot price of the underlying asset. In addition, we test and report that our approach performs better when it is compared to the multilevel Monte Carlo method for special cases of barrier options and underlying assets that make the pricing problem a rare event estimation. These theoretical findings are confirmed by numerous simulation results.

Efficient Pricing of Barrier Options on High Volatility Assets Using Subset Simulation

In this paper, we propose a new stochastic simulation-based methodology for pricing discretely-monitored double barrier options and estimating the corresponding probabilities of execution. We develop our framework by employing a versatile tool for the estimation of rare event probabilities known as subset simulation algorithm. In this regard, considering plausible dynamics for the price evolution of the underlying asset, we are able to compare and demonstrate clearly that our treatment always outperforms the standard Monte Carlo approach and becomes substantially more efficient (measured in terms of the sample coefficient of variation) when the underlying asset has high volatility and the barriers are set close to the spot price of the underlying asset. In addition, we test and report that our approach performs better when it is compared to the multilevel Monte Carlo method for special cases of barrier options and underlying assets that make the pricing problem a rare event estimation. These theoretical findings are confirmed by numerous simulation results.

Robustness of Subset Simulation to Functional Forms of Limit State Functions in System Reliability Analysis: Revisiting and Improvement

With the development of computing technologies, computer-based simulation methods have gained increasing attention in reliability analysis of engineering systems, among which subset simulation (SS) provides a robust yet efficient tool for exploring system rare failure events and evaluating system reliability. However, the component limit state functions (LSFs) of a system can be formulated in different forms (e.g., linear, exponential, and scaled), depending on mathematical modeling of the engineering systems concerned. This affects the system LSF and the performance of SS, and may lead to inconsistent system reliability analysis results. This study systematically explores effects of the functional form of component LSFs on the performance of SS in system reliability analysis and accounts for such effects from the perspective of sampling procedures. It is found that the efficient generation of conditional samples during SS, which is pivotal to the success of SS, is affected by the functional form of component LSFs in the system concerned. The performance of SS can be sensitive to the functional form of component LSFs. Normalizing component LSFs eliminate the effects of scaled LSFs on the performance of SS, but it does not improve the robustness (insensitivity) of SS in system reliability analysis involving exponential LSFs that are nonlinear. Understanding the effects of component LSFs on the performance of SS, a generalized SS (GSS) algorithm is proposed for system reliability analysis, which is robust to different functional forms of component LSFs provided that the functional transformation of component LSFs does not change their monotonicity and failure domains. Numerical examples and a real engineering example showed that the proposed algorithm is more robust to the functional form of component LSFs in system reliability analysis than standard SS.

Bayesian Subset Simulation

We consider the problem of estimating a probability of failure $\alpha$, defined as the volume of the excursion set of a function $f:\mathbb{X} \subseteq \mathbb{R}^{d} \to \mathbb{R}$ above a given threshold, under a given probability measure on $\mathbb{X}$. In this article, we combine the popular subset simulation algorithm (Au and Beck, Probab. Eng. Mech. 2001) and our sequential Bayesian approach for the estimation of a probability of failure (Bect, Ginsbourger, Li, Picheny and Vazquez, Stat. Comput. 2012). This makes it possible to estimate $\alpha$ when the number of evaluations of $f$ is very limited and $\alpha$ is very small. The resulting algorithm is called Bayesian subset simulation (BSS). A key idea, as in the subset simulation algorithm, is to estimate the probabilities of a sequence of excursion sets of $f$ above intermediate thresholds, using a sequential Monte Carlo (SMC) approach. A Gaussian process prior on $f$ is used to define the sequence of densities targeted by the SMC algorithm, and drive the selection of evaluation points of $f$ to estimate the intermediate probabilities. Adaptive procedures are proposed to determine the intermediate thresholds and the number of evaluations to be carried out at each stage of the algorithm. Numerical experiments illustrate that BSS achieves significant savings in the number of function evaluations with respect to other Monte Carlo approaches.

Discrete Optimum Design for Truss Structures by Subset Simulation Algorithm

AbstractThis article deals with the design optimization of truss structures with discrete design variables, which remains quite a challenging task in structural design. A new discrete search strategy based on the recently developed subset simulation optimization algorithm is proposed in detail for this type of structural optimization. The discrete design variables are transformed into standard normal variable space to implement the sampling procedure in subset simulation optimization, while the optimization is processed in the discrete design space in the mean time. The performance of the proposed method is illustrated by four representative benchmark optimization problems. Comparisons are made with other well known stochastic optimization algorithms. It is found that the proposed method can produce optimum designs as good as or better than those of other stochastic optimization algorithms.

Evaluating small failure probabilities of multiple limit states by parallel subset simulation

A novel subset simulation algorithm, called the parallel subset simulation, is proposed to estimate small failure probabilities of multiple limit states with only a single subset simulation analysis. As well known, crude Monte Carlo simulation is inefficient in estimating small probabilities but is applicable to multiple limit states, while the ordinary subset simulation is efficient in estimating small probabilities but can only handle a single limit state. The proposed novel stochastic simulation approach combines the advantages of the two simulation methods: it is not only efficient in estimating small probabilities but also applicable to multiple limit states. The key idea is to introduce a “principal variable” which is correlated with all performance functions. The failure probabilities of all limit states therefore could be evaluated simultaneously when subset simulation algorithm generates the principal variable samples. The statistical properties of the failure probability estimators are also derived. Two examples are presented to demonstrate the effectiveness of the new approach and to compare with crude Monte Carlo and ordinary subset simulation methods.

Parallel processing in structural reliability

The present contribution addresses the parallelization of advanced simulation methods for structural reliability analysis, which have recently been developed for large-scale structures with a high number of uncertain parameters. In particular, the Line Sampling method and the Subset Simulation method are considered. The proposed parallel algorithms exploit the parallelism associated with the possibility to simultaneously perform independent FE analyses. For the Line Sampling method a parallelization scheme is proposed both for the actual sampling process, and for the statistical gradient estimation method used to identify the so-called important direction of the Line Sampling scheme. Two parallelization strategies are investigated for the Subset Simulation method: the first one consists in the embarrassingly parallel advancement of distinct Markov chains; in this case the speedup is bounded by the number of chains advanced simultaneously. The second parallel Subset Simulation algorithm utilizes the concept of speculative computing. Speedup measurements in context with the FE model of a multistory building (24,000 DOFs) show the reduction of the wall-clock time to a very viable amount (<10 minutes for Line Sampling and <TEX>${\approx}$</TEX> 1 hour for Subset Simulation). The measurements, conducted on clusters of multi-core nodes, also indicate a strong sensitivity of the parallel performance to the load level of the nodes, in terms of the number of simultaneously used cores. This performance degradation is related to memory bottlenecks during the modal analysis required during each FE analysis.