Reliability Analysis Based on Efficient Simulation

Assessing small failure probabilities by AK–SS: An active learning method combining Kriging and Subset Simulation

Modified subset simulation method for reliability analysis of structural systems

An adaptive parallel learning dependent Kriging model for small failure probability problems

Structural reliability is an engineering method that focuses on the calculation and prediction of the probability of failure for a structural system. Structural reliability is used by many pipeline operators to assess the probability of failure, and these probability of failure estimates are then used to manage safety, environmental, and operational risks for threats such as corrosion, cracking, and third-party damage. In the structural reliability approach, the probability of failure is obtained from a multi-dimensional integral, for which the solution is commonly estimated numerically using Direct Monte Carlo (DMC) simulation. DMC is straight-forward and robust but requires a significant amount of computational effort to estimate small probabilities, which are typical of threats to pipeline integrity. Subset Simulation is an approach that improves on the efficiency issues of DMC by representing the rare event probability as the product of a number of more frequent events, which are each estimated separately. Previous work published at IPC in 2020 by Bandstra and Fraser [1] showed very close agreement between DMC and Subset Simulation for a single defect reference case with a single limit state. This study extends that initial work by applying Subset Simulation to various failure models for a variety of pipeline defects. Subset Simulation is applied to the CSA corrosion model, PRCI MAT-8 crack model, and the EPRG dent failure model, and the performance is evaluated by comparing the results to DMC. For each of these comparisons, simulations are performed across a large-scale grid of validation cases that consider a range of pipeline and defect sizes. An approach that utilizes Subset Simulation to handle multiple limit states is also presented and applied for relevant failure models and the results are evaluated against DMC.

Assessing small failure probabilities by combined subset simulation and Support Vector Machines

Estimation of the failure probability function plays a crucial role in reliability-based optimization design. However, efficiently and accurately estimating the failure probability function remains a challenge. Compared to the traditional double-loop Monte Carlo simulation(DMCS) method, the single-loop probabilistic reanalysis (PRA) method can alleviate the computational dependence of the failure probability function on the distribution parameters of input variables. Nevertheless, for problems involving small failure probabilities, the computational cost of PRA is still impractical for engineering applications. Therefore, to further improve the computational efficiency of the failure probability function, this paper combines importance sampling-based subset simulation(SS-IS) with the PRA method, and proposes a novel SS-IS-based PRA(SS-IS-PRA) method. The proposed method transforms the final small failure probability function into a product of a series of larger conditional failure probability functions and then estimates all conditional failure probability functions simultaneously with a set of input-output samples by virtue of the advantages of the PAR method. Furthermore, considering that the design points under different distribution parameters are normally different, a new strategy for constructing the importance sampling density function in the SS-IS-PAR method is proposed based on the idea of mixed importance sampling. Finally, two examples verify the effectiveness and efficiency of the proposed SS-IS-PRA method.

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation.The efficiency, superiority and practicability of AGPC-IS are verified by three examples

Importance analysis on the failure probability of the fuzzy and random system and its state dependent parameter solution

Passive systems, which completely depend on natural phenomena such as gravity, conduction and convection to accomplish the safety functions, are increasingly being used in new generation nuclear reactor designs. However, since the driving forces of passive systems are weak, they are more vulnerable to associated uncertainties and there may be a non-zero probability for the system to deviate from the intended behavior and leads to functional failure. Methods for quantification of the functional failure include Monte-Carlo simulation of the system uncertainties using a validated mechanistic code. Generally, the mechanistic codes used for complex system modeling are computationally expensive and Monte- Carlo simulation for estimating small failure probabilities requires more time and often become prohibitive. In this respect, recently, functional reliability methodologies including advanced simulation techniques such as subset simulation, Markov chain Monte-Carlo, importance sampling, and response conditioning method are reported in open literature. Unlike in the case of direct Monte Carlo simulation, for the probability estimates obtained using these advanced simulations, analytical formulas are not available to estimate standard error and confidence interval. In this paper, the estimation of standard error and confidence interval of functional reliability estimates using computationally efficient re-sampling methods based on bootstrap technique are described. Numerical application of these methods, to quantify the variability of functional reliability estimates, is also explained.

Heliostats represent the most important maintenance cost in the solar power tower plant. The aim of this work is to provide a failure probability analysis for heliostat design in order to minimize this maintenance cost. Based on mechanics of material study and wind aerodynamic analysis, a performance function, with five random variables, has been developed wherein the random variables are: wind speed, inside and outside pedestal diameters, pedestal yield stress and mirror mass. Four main methods have been proposed: first order reliability method, second order reliability method, Monte Carlo (MC) method and subset simulation (SS) method. The variation of failure probability with the variation of pedestal wall thickness and wind speed, for different outside diameters and heliostat azimuth and elevation angles, has been simulated. The results show that SS is more efficient and accurate for small failure probabilities; however, MC is more accurate for high failure probabilities.

Estimation of the lower and upper bounds on the probability of failure using subset simulation and random set theory

An enhanced method for improving the accuracy of small failure probability of structures

The aim of this article is to study the reliability analysis, parametric reliability sensitivity analysis and global reliability sensitivity analysis of structures with extremely rare failure events. First, the global reliability sensitivity indices are restudied, and we show that the total effect index can also be interpreted as the effect of randomly copying each individual input variable on the failure surface. Second, a new method, denoted as Active learning Kriging Markov Chain Monte Carlo (AK-MCMC), is developed for adaptively approximating the failure surface with active learning Kriging surrogate model as well as dynamically updated Monte Carlo or Markov chain Monte Carlo populations. Third, the AK-MCMC procedure combined with the quasi-optimal importance sampling procedure is extended for estimating the failure probability and the parametric reliability sensitivity and global reliability sensitivity indices. For estimating the global reliability sensitivity indices, two new importance sampling estimators are derived. The AK-MCMC procedure can be regarded as a combination of the classical Monte Carlo Simulation (AK-MCS) and subset simulation procedures, but it is much more effective when applied to extremely rare failure events. Results of test examples show that the proposed method can accurately and robustly estimate the extremely small failure probability (e.g. 1e–9) as well as the related parametric reliability sensitivity and global reliability sensitivity indices with several dozens of function calls.

SummaryPolynomial chaos expansions (PCEs) have been widely employed to estimate failure probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies: (a) PCE coefficients are solved by the least‐square minimization method which easily causes overfitting issues; (b) building a high order PCE is often computationally expensive. In order to overcome the aforementioned drawbacks, the Bayesian regression technique is employed to evaluate PCE coefficients, which not only provides a sparse solution but also avoids overfitting. With the aid of the predictive means and variances given by Bayesian analysis, a learning function is proposed to sequentially select the most informative samples that are critical to build a PCE. This sequential learning scheme can highly enhance the computational efficiency of PCEs. Besides, importance sampling (IS) is incorporated into the sequential learning (SL)‐PCEs to deal with geotechnical problems with small failure probabilities. The proposed method of SL‐PCE‐IS is applied to three illustrative examples, which shows that the improved PCE method is more effective and efficient than the common PCEs method, leading to accurate estimations of small failure probabilities using fewer training samples.

Assessing small failure probability by importance splitting method and its application to wind turbine extreme response prediction