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
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Open Access
EDITORIAL
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Open Access
ARTICLE
Stress Relaxation and Sensitivity Weight for Bi-Directional Evolutionary Structural Optimization to Improve the Computational Efficiency and Stabilization on Stress-Based Topology Optimization
Chao Ma, Yunkai Gao, Yuexing Duan, Zhe Liu
CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 715-738, 2021, DOI:10.32604/cmes.2021.011187
（This article belongs to this Special Issue:
Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
Abstract Stress-based topology optimization is one of the most concerns of structural optimization and receives much attention in a wide range of engineering designs. To solve the inherent issues of stress-based topology optimization, many schemes are added to the conventional bi-directional evolutionary structural optimization (BESO) method in the previous studies. However, these schemes degrade the generality of BESO and increase the computational cost. This study proposes an improved topology optimization method for the continuum structures considering stress minimization in the framework of the conventional BESO method. A global stress measure constructed by
p-norm function is treated as the objective function. To…
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Open Access
ARTICLE
Sensitivity of Sample for Simulation-Based Reliability Analysis Methods
Xiukai Yuan, Jian Gu, Shaolong Liu
CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.1, pp. 331-357, 2021, DOI:10.32604/cmes.2021.010482
（This article belongs to this Special Issue:
Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
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…
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Open Access
ARTICLE
A Fast Product of Conditional Reduction Method for System Failure Probability Sensitivity Evaluation
Jie Yang, Changping Chen, Ao Ma
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 1159-1171, 2020, DOI:10.32604/cmes.2020.09640
（This article belongs to this Special Issue:
Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
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…
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Open Access
ARTICLE
Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames
Baoyin Sun, Yantai Zhang, Caigui Huang
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 755-776, 2020, DOI:10.32604/cmes.2020.09632
（This article belongs to this Special Issue:
Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
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…
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Open Access
ARTICLE
A Bayesian Updating Method for Non-Probabilistic Reliability Assessment of Structures with Performance Test Data
Jiaqi He, Yangjun Luo
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 777-800, 2020, DOI:10.32604/cmes.2020.010688
（This article belongs to this Special Issue:
Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
Abstract For structures that only the predicted bounds of uncertainties are
available, this study proposes a Bayesian method to logically evaluate the nonprobabilistic reliability of structures based on multi-ellipsoid convex model
and performance test data. According to the given interval ranges of uncertainties, we determine the initial characteristic parameters of a multi-ellipsoid
convex set. Moreover, to update the plausibility of characteristic parameters,
a Bayesian network for the information fusion of prior uncertainty knowledge and subsequent performance test data is constructed. Then, an updated
multi-ellipsoid set with the maximum likelihood of the performance test data
can be achieved. The credible non-probabilistic reliability…
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Open Access
ARTICLE
Reliability Analysis Based on Optimization Random Forest Model and MCMC
Fan Yang, Jianwei Ren
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 801-814, 2020, DOI:10.32604/cmes.2020.08889
（This article belongs to this Special Issue:
Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
Abstract Based on the rapid simulation of Markov Chain on samples in
failure region, a novel method of reliability analysis combining Monte Carlo
Markov Chain (MCMC) with random forest algorithm was proposed. Firstly,
a series of samples distributing around limit state function are generated by
MCMC. Then, the samples were taken as training data to establish the random forest model. Afterwards, Monte Carlo simulation was used to evaluate
the failure probability. Finally, examples demonstrate the proposed method
possesses higher computational efficiency and accuracy.
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Open Access
ARTICLE
Robust Design Optimization and Improvement by Metamodel
Shufang Song, Lu Wang, Yuhua Yan
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 383-399, 2020, DOI:10.32604/cmes.2020.09588
（This article belongs to this Special Issue:
Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
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…
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Open Access
ARTICLE
Research on Trajectory Tracking Method of Redundant Manipulator Based on PSO Algorithm Optimization
Shifu Xu, Yanan Jiang
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 401-415, 2020, DOI:10.32604/cmes.2020.09608
（This article belongs to this Special Issue:
Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
Abstract Aiming at the problem that the trajectory tracking performance of
redundant manipulator corresponding to the target position is difficult to optimize,
the trajectory tracking method of redundant manipulator based on PSO algorithm
optimization is studied. The kinematic diagram of redundant manipulator is created, to derive the equation of motion trajectory of redundant manipulator end.
Pseudo inverse Jacobi matrix is used to solve the problem of manipulator redundancy. Based on the tracking ellipse of redundant manipulator, the tracking shape
of redundant manipulator is determined with the overall tracking index as the second index, and the optimization method of tracking index is…
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Open Access
ARTICLE
Subinterval Decomposition-Based Interval Importance Analysis Method
Wenxuan Wang, Xiaoyi Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 985-1000, 2020, DOI:10.32604/cmes.2020.09006
（This article belongs to this Special Issue:
Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
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…
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Open Access
ARTICLE
A Local Sparse Screening Identification Algorithm with Applications
Hao Li, Zhixia Wang, Wei Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 765-782, 2020, DOI:10.32604/cmes.2020.010061
（This article belongs to this Special Issue:
Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
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…
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Open Access
ARTICLE
Robust Remaining Useful Life Estimation Based on an Improved Unscented Kalman Filtering Method
Shenkun Zhao, Chao Jiang, Zhe Zhang, Xiangyun Long
CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1151-1173, 2020, DOI:10.32604/cmes.2020.08867
（This article belongs to this Special Issue:
Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
Abstract In the Prognostics and Health Management (PHM), remaining useful
life (RUL) is very important and utilized to ensure the reliability and safety of
the operation of complex mechanical systems. Recently, unscented Kalman filtering (UKF) has been applied widely in the RUL estimation. For a degradation system, the relationship between its monitored measurements and its degradation
states is assumed to be nonlinear in the conventional UKF. However, in some special degradation systems, their monitored measurements have a linear relation
with their degradation states. For these special problems, it may bring estimation
errors to use the UKF method directly. Besides, many uncertain…
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