Structural robust optimization design based on convex model

Chen, Xuyong; Fan, Jiawei; Bian, Xiaoya

doi:10.1016/j.rinp.2017.08.013

Cited by 13 publications

(4 citation statements)

References 25 publications

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“…The fault tree analysis method based on the fuzzy reliability model is relatively mature. However, in engineering practice, it is often difficult to obtain sufficient data to determine the probability density function or fuzzy affiliation function of parameters [30]. As a mathematical method to deal with uncertainty inference problems, D-S evidence theory quantifies the degree of confidence and likelihood of propositions.…”

Section: D-s Evidence Theorymentioning

confidence: 99%

System Reliability Analysis Method Based on T-S FTA and HE-BN

Xia,

Li,

Zhang

et al. 2024

CMES

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For high-reliability systems in military, aerospace, and railway fields, the challenges of reliability analysis lie in dealing with unclear failure mechanisms, complex fault relationships, lack of fault data, and uncertainty of fault states. To overcome these problems, this paper proposes a reliability analysis method based on T-S fault tree analysis (T-S FTA) and Hyper-ellipsoidal Bayesian network (HE-BN). The method describes the connection between the various system fault events by T-S fuzzy gates and translates them into a Bayesian network (BN) model. Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation, a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems. Experts describe the degree of failure of the event in the form of interval numbers. The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node. Then, the Hyper-ellipsoidal model (HM) constrains the initial failure probability interval and constructs a HE-BN for the system. A reliability analysis method is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure. The failure probability of the system is further calculated and the key components that affect the system's reliability are identified. The proposed method accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses. The feasibility and accuracy of the method are further verified by conducting case studies.

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Section: D-s Evidence Theorymentioning

confidence: 99%

System Reliability Analysis Method Based on T-S FTA and HE-BN

Xia,

Li,

Zhang

et al. 2024

CMES

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“…In addition, Chen et al [25] presented the method and procedure of robust optimization design by dividing the optimization procedure into two stages based on the interval model, namely main optimization and suboptimization. Wang et al [26] proposed a nonprobabilistic reliability-based topology optimization method for detailed design of continuum structures, in which the unknown but bounded uncertainties existing in material and external loads were considered simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Improved Nonprobabilistic Global Optimal Solution Method and Its Application in Bridge Reliability Assessment

Bian

Chen

Yang

et al. 2019

Advances in Civil Engineering

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Utilizing the improved one-dimensional optimization algorithm conveniently solved the nonprobabilistic reliability index, however, only searching the part of probable failure points. Utilizing the global optimal solution method produced heavy computation, although it was capable of searching all probable failure points. This paper, based on these two methods, proposed an improved nonprobabilistic global optimal solution method. The presented method possessed the advantages of searching all probable failure points and generating less computation, by means of which the values of interval variables were determined based on the monotonicity of performance function to the corresponding variables. Without losing any probable failure points, this method was contributive to reducing root value point equations, lowering the computational complexity, and improving the computational efficiency. The effectiveness and feasibility of the presented method were verified by two examples. The proposed method was also introduced to build the nonprobabilistic reliability evaluation process of existing bridges. Taking the Longganhu bridge for example, the nonprobabilistic reliability index of it was calculated using the presented nonprobabilistic reliability evaluation process. The computed nonprobabilistic reliability index η = 0.737 < 1 indicated that the Longganhu bridge was unreliable and needed to be reinforced. Reinforcement measures were carried out for a hollow slab and bridge deck of the Longganhu bridge, respectively. The reinforced bridge was reevaluated as η = 2.159 > 1. The results showed that the bridge was reliable after reinforcement. The study illustrated the application and feasibility of the improved nonprobabilistic global optimal solution method and nonprobabilistic reliability evaluation process in reliability assessment and reinforcement of existing bridges.

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“…Wu et al [33] investigated the robust topology optimization of structures under interval uncertainty and developed a new sensitivity analysis method. Chen et al [34] proposed the target-performance-based analytical scheme for the robust optimization of uncertain structures based on hyper-ellipsoidal and interval models. Hot et al [35] investigated the robust design of a pre-stressed space structure under epistemic uncertainties based on info-gap model.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous-Objective Robust Optimization of Complex Mechatronic Components Considering Interval Uncertainties

et al. 2019

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Structural optimization of complex mechatronic components may involve heterogeneous competing performance indices, including the cost, fixation, benefit, and deviation ones. However, such optimization problems with heterogeneous objectives have not been investigated so far. In this paper, a novel interval heterogeneous-objective robust optimization approach is proposed for complex mechatronic components. First, a unified interval heterogeneous-objective robust optimization model is constructed for mechatronic components with the uncertainties described as interval variables. Subsequently, a new interval robust equilibrium optimization algorithm is proposed to solve the interval heterogeneous-objective robust optimization model. Specifically, the unified formulas for assessing the robustness of interval heterogeneous-objective performance indices and the robust equilibrium among them are derived at first. Then, the preferential guidelines considering the robust equilibrium among all the objective and constraint performance indices are proposed for the direct ranking of various design vectors, and finally, the heterogeneous-objective robust equilibrium optimization of complex mechatronic components is realized by integrating the Kriging technique and the nested genetic algorithm. The feasibility and effectiveness of the proposed heterogeneous-objective robust optimization approach are verified by a numerical example and a case study. INDEX TERMS Heterogeneous-objective robust optimization, complex mechatronic component, interval, robust equilibrium, closeness coefficient, group ranking.

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Structural robust optimization design based on convex model

Cited by 13 publications

References 25 publications

System Reliability Analysis Method Based on T-S FTA and HE-BN

System Reliability Analysis Method Based on T-S FTA and HE-BN

Improved Nonprobabilistic Global Optimal Solution Method and Its Application in Bridge Reliability Assessment

Heterogeneous-Objective Robust Optimization of Complex Mechatronic Components Considering Interval Uncertainties

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