Special Issue "Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures"

Submission Deadline: 31 July 2020 (closed)
Guest Editors
Prof. Yangjun Luo, Dalian University of Technology, China
Dr. Feng Zhang, Northwestern Polytechnical University, China

Summary

Reliability of mechanical structures is a very important issue in the engineering field, especially in the field of aerospace and other high-tech sophisticated fields. Up to now, reliability-based evaluation and optimization methods have been widely applied in the safety analysis and design of mechanical structures. Many advanced algorithms for greatly improving the calculation efficiency and accuracy of solutions have been developed and gained successful applications in real engineering problems. Recently, with the advantages of strong versatility and well global-searching ability, intelligent algorithms for reliability optimization design of mechanical structures have also attracted ever-increasing interest. For the failure of complex mechanical structures under multi-physical coupling field, the reliability optimization method based on intelligent algorithms is applicable to solving design failure problems.

In this special issue, "Novel methods for reliability evaluation and optimization of complex mechanical structures", we thus invite researchers and practitioners to present their original ideas and novel algorithms in the reliability-based evaluation and reliability-based optimization. Suggested topics include, but are not limited to:
• Reliability Evaluation Index of Complex Mechanical Structures

• Innovation and Improvement of Intelligent Algorithms

• Fault Analysis of Complex Mechanical Structures

• Reliability Evaluation Based on Intelligent Algorithms

• Reliability Optimization Design in Intelligent Algorithms



Keywords
Structure Reliability; Intelligent Algorithms; Reliability Evaluation; Reliability Optimization

Published Papers


  • Sensitivity of Sample for Simulation-Based Reliability Analysis Methods
  • Abstract In structural reliability analysis, simulation methods are widely used. The statistical characteristics of failure probability estimate of these methods have been well investigated. In this study, the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample, called ‘contribution indexes’, are proposed to measure the contribution of sample. The contribution indexes in four widely simulation methods, i.e., Monte Carlo simulation (MCS), importance sampling (IS), line sampling (LS) and subset simulation (SS) are derived and analyzed. The proposed contribution indexes of sample can provide valuable information understanding the methods deeply, and enlighten potential improvement of methods. It… More
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  • A Fast Product of Conditional Reduction Method for System Failure Probability Sensitivity Evaluation
  • Abstract System reliability sensitivity analysis becomes difficult due to involving the issues of the correlation between failure modes whether using analytic method or numerical simulation methods. A fast conditional reduction method based on conditional probability theory is proposed to solve the sensitivity analysis based on the approximate analytic method. The relevant concepts are introduced to characterize the correlation between failure modes by the reliability index and correlation coefficient, and conditional normal fractile the for the multi-dimensional conditional failure analysis is proposed based on the two-dimensional normal distribution function. Thus the calculation of system failure probability can be represented as a summation… More
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  • Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames
  • Abstract Steel frames equipped with buckling restrained braces (BRBs) have been increasingly applied in earthquake-prone areas given their excellent capacity for resisting lateral forces. Therefore, special attention has been paid to the seismic risk assessment (SRA) of such structures, e.g., seismic fragility analysis. Conventional approaches, e.g., nonlinear finite element simulation (NFES), are computationally inefficient for SRA analysis particularly for large-scale steel BRB frame structures. In this study, a machine learning (ML)- based seismic fragility analysis framework is established to effectively assess the risk to structures under seismic loading conditions. An optimal artificial neural network model can be trained using calculated damage… More
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  • Robust Design Optimization and Improvement by Metamodel
  • Abstract The robust design optimization (RDO) is an effective method to improve product performance with uncertainty factors. The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables. There are some important issues in RDO, such as how to judge robustness, deal with multi-objective problem and black-box situation. In this paper, two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment. The robustness measure based on maximum entropy is proposed. Weighted sum method is improved to deal with the objective function, and the basic framework of… More
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  • Subinterval Decomposition-Based Interval Importance Analysis Method
  • Abstract The importance analysis method represents a powerful tool for quantifying the impact of input uncertainty on the output uncertainty. When an input variable is described by a specific interval rather than a certain probability distribution, the interval importance measure of input interval variable can be calculated by the traditional non-probabilistic importance analysis methods. Generally, the non-probabilistic importance analysis methods involve the Monte Carlo simulation (MCS) and the optimization-based methods, which both have high computational cost. In order to overcome this problem, this study proposes an interval important analytical method avoids the time-consuming optimization process. First, the original performance function is… More
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  • A Local Sparse Screening Identification Algorithm with Applications
  • Abstract Extracting nonlinear governing equations from noisy data is a central challenge in the analysis of complicated nonlinear behaviors. Despite researchers follow the sparse identification nonlinear dynamics algorithm (SINDy) rule to restore nonlinear equations, there also exist obstacles. One is the excessive dependence on empirical parameters, which increases the difficulty of data pre-processing. Another one is the coexistence of multiple coefficient vectors, which causes the optimal solution to be drowned in multiple solutions. The third one is the composition of basic function, which is exclusively applicable to specific equations. In this article, a local sparse screening identification algorithm (LSSI) is proposed… More
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