Stochastic multi-scale finite element based reliability analysis for laminated composite structures

This paper proposes a novel multi-scale approach for the reliability analysis of composite structures that accounts for both microscopic and macroscopic uncertainties, such as constituent material properties and ply angle. The stochastic structural responses, which establish the relationship between structural responses and random variables, are achieved using a stochastic multi-scale finite element method, which integrates computational homogenisation with the stochastic finite element method. This is further combined with the first- and second-order reliability methods to create a unique reliability analysis framework. To assess this approach, the deterministic computational homogenisation method is combined with the Monte Carlo method as an alternative reliability method. Numerical examples are used to demonstrate the capability of the proposed method in measuring the safety of composite structures. The paper shows that it provides estimates very close to those from Monte Carlo method, but is significantly more efficient in terms of computational time. It is advocated that this new method can be a fundamental element in the development of stochastic multi-scale design methods for composite structures.     

随机结构系统基于可靠性的优化设计

提出了以梁板（薄板）为基体的随机结构系统（即结构元件的面积、长度、弹性模量和强度等均为随机变量）在随机载荷作用下,基于可靠性的优化设计方法．给出了随机结构系统安全余量和系统可靠性指标的敏度表达式;给出最佳矢量型算法．首先是用改进的一次二阶矩和随机有限元法求出安全余量的可靠性指标的表达式,然后用概率网络估算(PNET)法求出系统失效概率的公式,对该式两边求导得出了系统可靠性指标的敏度表达式,进而用最佳矢量型算法进行优化设计．在优化迭代过程中,采用梯度步和最佳矢量步相结合的方法进行计算．最后给出了一个算例,说明该方法计算效率高,收敛稳定,适合工程应用．     

Stochasticity and safety factors: Part 1. Random actual stress and deterministic yield stress

This paper makes a bridge between the classical concept of the safety factor and the structural reliability. It concentrates on a special case in which the material possesses deterministic yield stress, and the element is subjected to the random actual stress. Various probability distribution functions are treated to describe the probabilistic behavior of the stress; reliabilities are evaluated, and the connection with the safety factors is emphasized.     

Structural reliability analysis with imprecise random and interval fields

This paper investigates the issue of reliability assessment for engineering structures involving mixture of stochastic and non-stochastic uncertain parameters through the Finite Element Method (FEM). Non-deterministic system inputs modelled by both imprecise random and interval fields have been incorporated, so the applicability of the structural reliability analysis scheme can be further promoted to satisfy the intricate demand of modern engineering application. The concept of robust structural reliability profile for systems involving hybrid uncertainties is discussed, and then a new computational scheme, namely the unified interval stochastic reliability sampling (UISRS) approach, is proposed for assessing the safety of engineering structures. The proposed method provides a robust semi-sampling scheme for assessing the safety of engineering structures involving multiple imprecise random fields with various distribution types and interval fields simultaneously. Various aspects of structural reliability analysis with multiple imprecise random and interval fields are explored, and some theoretically instructive remarks are also reported herein.     

The analyses of dynamic response and reliability for failure-dependent stochastic micro-resonator with thermoelastic coupling effects

A crucial measure for the design of high-performance micro-resonators is to consider the randomness of structural parameters when analyzing the structural system reliability. In this work, the stochastic dynamic response analysis and subsequently, a dynamic reliability assessment of the random micro-resonators are originally presented, where the thermoelastic coupling effects are freshly incorporated in the models proposed. The dynamic characteristics equation of the deterministic micro-resonator is firstly established based on the finite element method. The random dynamic characteristics of the resonator are then solved by implementing the left and right eigenvectors and the block Lanczos algorithm, and the random temperature field and structural random dynamic stress are also tackled. Afterwards, the overall structural reliability is investigated with a comprehensive consideration of the strength failure and frequency resonance failure, in which the Copula function is used for describing the dynamic correlation between two failure modes. Finally, the feasibility and rationality of the method put forward are demonstrated via a practically motivated example.     

Análise da confiabilidade de estruturas de concreto armado com uma metodologia para inclusão de efeitos estocásticos de propriedades dos materiais

In this paper, special emphasis is given to the inclusion of uncertainties in the evaluation of structural behaviour aiming at a better representation of the system characteristics and the quantification of the importance of these uncertainties in the project. It deals with the structural reliability analysis problem accounting the effect of spatial variability of material properties. To this end it is proposed a finite element model to represent the behaviour of reinforced concrete for short and long-term loads, which includes the main features observed in this material. It was developed a model for the generation of multidimensional non-Gaussian stochastic fields for the material properties that is independent of the finite element mesh. First, an example of a two-dimensional non-Gaussian stochastic field generation in a square steel plate is presented. Latter, the reliability analysis is performed to a limit state function based on prescribed values of mid-span displacements on a simply-supported reinforced beam. Finally, the influence of long-term effects on the reliability of a reinforced concrete beam is studied considering the effect of steel reinforcement corrosion.     

多态关联系统结构重要度分析

多态关联系统重要度是可靠性分析的重要研究内容之一，它可用于可靠度分配，系统的优化设计和指导系统运行管理等。本文定义了５类物理意义明确的结构重要度，由实例看出，这些重要度能较好反映系统状态的性质及部件对系统状态的影响。     

Reliability function of a class of time-dependent systems with standby redundancy

By applying shortest path analysis in stochastic networks, we introduce a new approach to obtain the reliability function of time-dependent systems with standby redundancy. We assume that not all elements of the system are set to function from the beginning. Upon the failure of each element of the active path in the reliability graph, the system switches to the next path. Then, the corresponding elements are activated and consequently the connection between the input and the output is established. It is also assumed each element exhibits a constant hazard rate and its lifetime is a random variable with exponential distribution. To evaluate the system reliability, we construct a directed stochastic network called E-network, in which each path corresponds with a minimal cut of the reliability graph. We also prove that the system failure function is equal to the density function of the shortest path of E-network. The shortest path distribution of this new constructed network is determined analytically using continuous-time Markov processes.     

Integrated decision support for aviation safety inspectors

This paper presents a systems viewpoint for developing an advanced decision support system for aircraft safety inspectors. Research results from a Federal Aviation Administration (FAA) sponsored project to use neural network and expert systems technology to analyze aircraft maintenance databases are summarized. One of the main objectives of this research is to define more refined “alert” indicators for national comparison purposes that can signal potential problem areas by aircraft type for safety inspector consideration.

Integration aspects are addressed on two levels: (1) integration of the various technical components of the decision support system, and (2) integration of the decision support system with individual behavior, management systems and organizational structure, as well as corporate culture across both formal and informal dimensions. The paper summarizes the creation of strategic “inspection profiles” for aging aircraft and reliability curve fitting for structural components both based upon using neural network technology. Also, the potential use of a model-based expert system to facilitate field inspection diagnostics is presented. Finally, a framework for developing an intelligent decision system to support aircraft safety inspections is proposed that links expert systems, neural networks, as well as a paradigm of the decision making process typically used in unstructured situations.     

The reliability analysis of probabilistic and interval hybrid structural system

The aim of this paper is to evaluate the reliability of probabilistic and interval hybrid structural system. The hybrid structural system includes two kinds of uncertain parameters—probabilistic parameters and interval parameters. Based on the interval reliability model and probabilistic operation, a new probabilistic and interval hybrid reliability model is proposed. Firstly, we use the interval reliability model to analyze the performance function, and then sum up reliability of all regions divided by the failure plane. Based on the presented optimal criterion enumerating the main failure modes of hybrid structural system and the relationship of failure modes, the reliability of structure system can be obtained. By means of the numerical examples, the hybrid reliability model and the traditional probabilistic reliability model are critically contrasted. The results indicate the presented reliability model is more suitable for analysis and design of these structural systems and it can ensure the security of system well, and it only needs less uncertain information.     

Classification of Structural Complexity for Mine Ventilation Networks

The mine ventilation system is most important and technical measure for ensuring safety production in mines. The structural complexity of a mine ventilation network can directly affect the safety and reliability of the underground mining system. Quantitatively justifying the degree of complexity can contribute to providing a deeper understanding of the essential characteristics of a network. However, so far, there is no such a model which is able to simply, practically, reasonably, and quantitatively determine or compare the structural complexity of different ventilation networks. In this article, by analyzing some typical parameters of a mine ventilation network, we conclude that there is a linear functional relationship among five key parameters (number of ventilation network branches, number of nodes, number of independent circuits, number of independent paths, and number of diagonal branches). Correlation analyses for the main parameters of ventilation networks are conducted based on SPSS. Based on these findings, a new evaluation model for the structural complexity of ventilation network (which is represented by C) has been proposed. By combining SPSS classification analyses results with the characteristics of mine ventilation networks, standards for the complexity classification of mine ventilation systems are put forward. Using the developed model, we carried out analyses and comparisons for the structural complexity of ventilation networks for typical mines. Case demonstrations show that the classification results correspond to the actual situations. © 2014 Wiley Periodicals, Inc. Complexity 21: 21–34, 2015     

Asymptotic sampling – a tool for efficient reliability computation in high dimensions

Monte Carlo methods are most versatile regarding applications to the reliability analysis of high-dimensional nonlinear structural systems. In addition to its versatility, the computational efficacy of Monte Carlo method is not adversely affected by the dimensionality of the problem. Crude Monte Carlo techniques, however, are very inefficient for extremely small failure probabilities such as typically required for sensitive structural systems. Therefore methods to increase the efficacy for small failure probability while keeping the adverse influence of dimensionality small are desirable. On such method is the asymptotic sampling method. Within the framework of this method, well-known asymptotic properties of the reliability index regarding the scaling of the basic variables are exploited to construct a regression model which allows to determine the reliability index for extremely small failure probabilities with high precision using a moderate number of Monte Carlo samples. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Machine learning aided static structural reliability analysis for functionally graded frame structures

A novel machine learning aided structural reliability analysis for functionally graded frame structures against static loading is proposed. The uncertain system parameters, which include the material properties, dimensions of structural members, applied loads, as well as the degree of gradation of the functionally graded material (FGM), can be incorporated within a unified structural reliability analysis framework. A 3D finite element method (FEM) for static analysis of bar-type engineering structures involving FGM is presented. By extending the traditional support vector regression (SVR) method, a new kernel-based machine learning technique, namely the extended support vector regression (X-SVR), is proposed for modelling the underpinned relationship between the structural behaviours and the uncertain system inputs. The proposed structural reliability analysis inherits the advantages of the traditional sampling method (i.e., Monte-Carlo Simulation) on providing the information regarding the statistical characteristics (i.e., mean, standard deviations, probability density functions and cumulative distribution functions etc.) of any concerned structural outputs, but with significantly reduced computational efforts. Five numerical examples are investigated to illustrate the accuracy, applicability, and computational efficiency of the proposed computational scheme.     

Dependency analysis in reliability studies

The reliability of engineering systems, especially safety systems,is acknowledged to be significantly affected by the dependencyof the components and subsystems. The factors that cause thedependency range from the design of the system to the maintenanceprocedures employed. Assessing the effect of dependency hasbeen a concern in both reliability theory and statistics. Mostinterest has centred on wmmoncause, or common-mode, failurebut other forms, such as time between failures, can play a majorrole. The engineering and statistical approaches to dependency havetended to diverge, and misconceptions in both assessment andmodelling have arisen. This paper constructively reviews thevarying approaches employed, and indicates future benefits thatmight accrue by the proper use of statistical methodologies.In this exploration, the pitfalls asscciated with some of thetechniques are highlighted. One of the major problems encounteredin assessing dependency is that the information d for assessmentwmes from maintenance records or incident reports which arcnot collected specifically for dependency studies. Other aspectsare the naivety of some of the models employed, which includecut-off assessment, the beta-factor, and multiple-parametermodels. Another feature, which seems to be overlooked by practitioners,is failures that are timedependent. Complementary to these practical approaches, there are a numberof statistical m e t h d that could be applied. In the recentliterature, there has been considerable work on developing multivariatemodels. There are also a variety of stochastic processes thatmight be employed. The paper examines how such models can bepractically applied to the data available, to produce a morecomprehensive understanding of dependency in the field of reliability.Particular emphasis is given to methods for identification ofdependency between components and systems, especially thosecapable of detecting time-dependent failures.     

电大教学效果的系统可靠性初析

本文运用概率论和系统的可靠性理论分析了电大教学系统的可靠性,揭示了电大教学系统及其各子系统之间的可靠性的关系     

Uncertainty Quantification of Stochastic Simulation for Black-box Computer Experiments

Stochastic simulations applied to black-box computer experiments are becoming more widely used to evaluate the reliability of systems. Yet, the reliability evaluation or computer experiments involving many replications of simulations can take significant computational resources as simulators become more realistic. To speed up, importance sampling coupled with near-optimal sampling allocation for these experiments is recently proposed to efficiently estimate the probability associated with the stochastic system output. In this study, we establish the central limit theorem for the probability estimator from such procedure and construct an asymptotically valid confidence interval to quantify estimation uncertainty. We apply the proposed approach to a numerical example and present a case study for evaluating the structural reliability of a wind turbine.     

Hybrid control variates-based simulation method for structural reliability analysis of some problems with low failure probability

The safety analysis of systems with nonlinear performance function and small probability of failure is a challenge in the field of reliability analysis. In this study, an efficient approach is presented for approximating small failure probabilities. To meet this aim, by introducing Probability Density Function (PDF) control variates, the original failure probability integral was reformulated based on the Control Variates Technique (CVT). Accordingly, using the adaptive cooperation of the subset simulation (SubSim) and the CVT, a new formulation was offered for the approximation of small failure probabilities. The proposed formulation involves a probability term (resulting from a fast-moving SubSim) and an adaptive weighting term that refines the obtained probability. Several numerical and engineering problems, involving nonlinear performance functions and system-level reliability problems, are solved by the proposed approach and common reliability methods. Results showed that the proposed simulation approach is not only more efficient, but is also robust than common reliability methods. It also presents a good potential for application in engineering reliability problems.     

Interval optimization based line sampling method for fuzzy and random reliability analysis

For structural system with fuzzy variables as well as random variables, a novel algorithm for obtaining membership function of fuzzy reliability is presented on interval optimization based Line Sampling (LS) method. In the presented algorithm, the value domain of the fuzzy variables under the given membership level is firstly obtained according to their membership functions. Then, in the value domain of the fuzzy variables, bounds of reliability of the structure are obtained by the nesting analysis of the interval optimization, which is performed by modern heuristic methods, and reliability analysis, which is achieved by the LS method in the reduced space of the random variables. In this way the uncertainties of the input variables are propagated to the safety measurement of the structure, and the membership function of the fuzzy reliability is obtained. The presented algorithm not only inherits the advantage of the direct Monte Carlo method in propagating and distinguishing the fuzzy and random uncertainties, but also can improve the computational efficiency tremendously in case of acceptable precision. Several examples are used to illustrate the advantages of the presented algorithm.     

A unified theory for coherent systems in reliability II. A unified viewpoint on multinary coherent systems through monotone coherence and relevance

Binary coherent system theory has played an important part in reliability. Its extension to (‘degradable’ or ‘multistate’ or) multinary systems has recently been considered in various papers, through various definitions. This paper studies the most general model for multinary systems, proposes a unified viewpoint on multinary coherent systems and gives unified arguments to apply and to investigate further the binary and multinary cases. In a more detailed way, the ‘helpful bridge’ lately proposed by Block and Savits¹ between the binary and multinary cases is completed and multinary systems then can be studied in terms of monotone binary coherent systems, introduced in a companion paper.² Through various results, multinary systems are examined in terms of structure functions and of life functions; fundamental relations for their analysis are obtained with their set characterizations; the main axis that can be retained among the numerous types of coherence is emphasized, in a unified way, through relevance; reliability models are examined through performance processes, life lengths and performance functions; and Birnbaum's factors of importance are thoroughly extended to the multinary case. Fundamental results proposed in previous studies are thus completed with a shorter unified approach.     

A new perspective to stress–strength models

The stress–strength models have been widely used for reliability design of systems. In these models the reliability is defined as the probability that the strength is larger than the stress. The analysis is then based on the binary reliability theory since there are two possible states for the system. In this paper, we study the stress–strength reliability in a different framework assigning more than two states to the system depending on the difference between strength and stress values. In other words, the stress–strength reliability is studied under multi-state system modeling. System state probabilities are evaluated and estimated under various assumptions on the system. The multicomponent form is also studied and some results are provided for large systems.