A simulation-based method for reliability based design optimization problems with highly nonlinear constraints

Rashki, Mohsen; Miri, Mahmoud; Moghaddam, Mehdi Azhdary

doi:10.1016/j.autcon.2014.07.004

Cited by 70 publications

(21 citation statements)

References 42 publications

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“…Results corresponding to β Tar = 2.0 and β Tar = 3.0 are shown. Compared to the benchmark solution obtained from [63], both TSM and dynamic chaos control (DCC) Finally, we compare the advantages gained by each of the modifications (Table 13). Since, we have only one probabilistic constraint, Modifications (a) and (c) have no effect.…”

Section: Methodsmentioning

confidence: 99%

Threshold shift method for reliability-based design optimization

Goswami

Chakraborty

Chowdhury

et al. 2019

Struct Multidisc Optim

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We present a novel approach, referred to as the threshold shift method (TSM), for reliability based design optimization (RBDO). The proposed approach is similar in spirit with the sequential optimization and reliability analysis (SORA) method where the RBDO problem is decoupled into an optimization and a reliability analysis problem. However, unlike SORA that utilizes shift-vector to shift the design variables within a constraint (independently), in TSM we propose to shift the threshold of the constraints. We argue that modifying a constraint, either by shifting the design variables (SORA) or by shifting the threshold of the constraints (TSM), influences the other constraints of the system. Therefore, we propose to determine the thresholds for all the constraints by solving a single optimization problem. Additionally, the proposed TSM is equipped with an active-constraint determination scheme. To make the method scalable, a practical algorithm for TSM that utilizes two surrogate models is proposed. Unlike the conventional RBDO methods, the proposed approach has the ability to handle highly non-linear probabilistic constraints. The performance of the proposed approach is examined on six benchmark problems selected from the literature. The proposed approach yields excellent results outperforming other popular methods in literature. As for the computational efficiency, the proposed approach is found to be highly efficient, indicating it's future application to other real-life problems.Keywords RBDO · threshold shift method · PC-Kriging · uncertainty IntroductionUncertainty is an eternal companion of all structural systems. In a system, uncertainty can originate from both, uncertain parameters within the model as well as the limitations in the model itself. These uncertainties, if ignored, may result in to catastrophic failures. Therefore, it is necessary to consider the effect of uncertainty in an optimization process. In literature, there exist two methodologies to incorporate uncertainty into the framework of optimization, namely the robust design optimization (RDO) [1-3] and the reliability-based design optimization (RBDO) [4][5][6][7][8][9]. In this study, we focus on RBDO. * These authors have equal contributions

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Section: Methodsmentioning

confidence: 99%

Threshold shift method for reliability-based design optimization

Goswami

Chakraborty

Chowdhury

et al. 2019

Struct Multidisc Optim

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“…The performance of RBDO solution schemes has been analysed by Rashki et al [38]. In their study, the authors proposed an interesting strategy for incorporating the reliability analysis into the optimisation step.…”

Section: Optimisation Problem Formulation Problem Statementmentioning

confidence: 99%

The cover thickness design of concrete structures subjected to chloride ingress from RBDO solution technique

Pellizzer

Leonel

2020

Rev. IBRACON Estrut. Mater.

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Diffusion is the principal transport mechanism of chloride ions into concrete pores. The chlorides trigger the reinforcements’ depassivation when its concentration at the concrete/reinforcement interface reaches the threshold level. Thus, the depassivation defines the initiation stage end and the propagation stage start. The structural safety reduces widely during the propagation stage because of the various deleterious mechanisms triggered by reinforcement’s corrosion. Therefore, the engineers should accurately predict and prevent the propagation stage start. The literature describes several models for evaluating the end of the initiation stage. However, few of them applies the Boundary Element Method (BEM) for this purpose, despite its known accuracy. Besides, enormous randomness affect the phenomenon. Thus, it is adequately handled solely in the probabilistic context. Optimisation techniques may be coupled in the problem modelling to propose adequate cover thickness values accounting for probabilities of failure. This study presents a Reliability-Based Design Optimisation (RBDO) approach for designing accurately the cover thickness of concrete structures subjected to chloride ingress. The BEM handles the diffusion modelling whereas the Monte Carlo simulation assesses the probabilities of failure. The RBDO is formulated in the context of Weighted Average Simulation Method (WASM), which requires only one assessment of the reliability analysis. It leads to a reliable and computationally efficient solution technique. The problem formulation and the implemented solution scheme are described herein. Moreover, one application is presented, in which the design results are interpreted properly.

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“…With utilization of the obtained failure probability and reprocessing the results of index function, parameter sensitivity is achievable without additional performance function valuation. The idea of proposed approach is using the weight flexibility feature of samples that is suggested in another study . To meet the mentioned purpose, after performing the above‐mentioned steps, small perturbation should apply in the desired statistical parameters of the random variable.…”

Section: Proposed Perturbation‐based Reliability Sensitivity Analysismentioning

confidence: 99%

“…Reliability sensitivity of road‐holding safety of a passive vehicle suspension investigated. The mechanical system is originally modeled in a previous study and its reliability investigated in other studies . The model is presented in Figure .…”

Section: Numerical Examplesmentioning

confidence: 99%

System‐level reliability sensitivity analysis by using weighted average simulation method

Rashki

Moghaddam

Miri

2019

Quality & Reliability Eng

Self Cite

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In addition to reliability analysis, investigation of the uncertainties' effect on the safety of structures can be regarded as one of the great concerns in engineering fields. The present study provides an efficient perturbation-based reliability sensitivity analysis approach based on weighted average simulation method (WASM) to attain uncertainties effects on the structures safety. Without asking additional samplings and/or requiring to function derivation (that is necessary in score function method), the proposed approach simultaneously uses the finite difference and weight flexibility feature of WASM to estimate the parameter sensitivities of a reliability problem. The proposed method has also been successfully applied to the improved version of WASM to obtain reliability based sensitivity results with very few samples. The accuracy and efficiency of the proposed method is examined by solving five analytical and engineering examples with highly nonlinear performance functions and system-level reliability problems. For each example, results are compared with those obtained by Monte Carlo simulation and common reliability methods. It is shown that the method is capable of solving real world system-level engineering problems efficiently and accurately.

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A simulation-based method for reliability based design optimization problems with highly nonlinear constraints

Cited by 70 publications

References 42 publications

Threshold shift method for reliability-based design optimization

Threshold shift method for reliability-based design optimization

The cover thickness design of concrete structures subjected to chloride ingress from RBDO solution technique

System‐level reliability sensitivity analysis by using weighted average simulation method

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