Interval analysis based robust truss optimization with continuous and discrete variables using mix-coded genetic algorithm

Zhou, Pingzhang; Du, Jiuyu; Li, Zhenhua

doi:10.1007/s00158-017-1668-6

Cited by 23 publications

(10 citation statements)

References 37 publications

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“…GA is preferred as the optimization solver in many papers (Li et al, 1999; Torregosa and Kanok-Nukulchai, 2002; Zhou et al, 2017) because of its inherent advantages: it has no preconditions for the continuity of the parameters and no sensitivity analysis of objective function is required. This adapts to the fact that sensitivity information has no explicit form and is difficult to obtain with respect to strong nonlinearity of the hysteresis curve of the bearing in this article.…”

Section: Parametric Optimization For Seismic Mitigationmentioning

confidence: 99%

Nonlinear dynamic analysis of the bridge bearing and genetic algorithm–based optimization for seismic mitigation

Liang

Cheng

Liu

et al. 2020

Advances in Structural Engineering

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Seismic mitigation for bridges by the specific bearing with highly elastoplastic dissipaters is very important to ensure safety of superstructures exposed to earthquakes. To study the lateral vibration of the bridge bearing, a nonlinear dynamic model is developed while thoroughly considering highly nonlinear mechanical properties of the bearing in this article. The generalized- α method is specifically adapted to solve the nonlinear equations. Moreover, nonlinear behavior of the bearing is fully incorporated through direct path-following of the practical experimental hysteresis curve. The proposed method is validated through careful comparison between the dynamic simulation and the quasi-static experimental results. Mechanical responses of the bridge-pier system under earthquake excitations can be calculated in a more reliable manner. On this basis, a parametric optimization model involving several key parameters of the bearing-pier system is developed. The optimization problem is solved by the genetic algorithm as the searching tool. Numerical examples show that mechanical responses of the bridge-pier system subjected to the earthquake excitations can be effectively mitigated after parametric optimization. The extensive applicability of the proposed method is validated through finding the optimized parameters for the bearing when multiple different earthquakes are considered. Moreover, the accumulative energy absorption of the bearing is also considered to enhance the seismic performance of the bearing. This work provides a reliable way of dynamic performance prediction and seismic mitigation study of nonlinear bridge bearing under earthquake excitations given any complex experimental hysteresis curve.

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Section: Parametric Optimization For Seismic Mitigationmentioning

confidence: 99%

Nonlinear dynamic analysis of the bridge bearing and genetic algorithm–based optimization for seismic mitigation

Liang

Cheng

Liu

et al. 2020

Advances in Structural Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The reliability range was compared with grey number theory and combination method, respectively. Zhou studied the optimization of truss structures with uncertain parameters considering continuous and discrete design variables, uncertain parameters were bounded in a specified interval, and a robust optimization method was proposed based on interval analysis. Soltania proposed a robust optimization method for solving redundancy allocation problems in series–parallel uncertain systems.…”

Section: Introductionmentioning

confidence: 99%

Reliability reallocation for cost uncertainty of fuel cell vehicles via improved differential evolution algorithm

Wang

Liu

et al. 2019

Quality & Reliability Eng

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A fuel cell vehicle (FCV) is composed of many subsystems; it is necessary to reallocate subsystem reliability to improve FCV system reliability. A comprehensive evaluation method considering uncertainty is proposed to obtain the interval value of feasibility factor. The FCV cost uncertainty model is established on the basic of parameters such as feasibility factor, initial reliability, limit reliability, and subsystem cost. To solve the optimization problem for cost uncertainty, the cost interval model is transformed into a deterministic model by interval order relation. An improved differential evolution (DE) algorithm is proposed to reallocate subsystem reliability for minimum cost.

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“…On the basis of global minimization, Gabriele et al developed a model updating detection method in the framework of interval analysis. Combining interval analysis with intelligent algorithm, interval optimization method becomes another means to solve interval problems . Qin and Yingming established an interval optimization method on the basis of particle swarm optimization and used it in damage detection.…”

Section: Introductionmentioning

confidence: 99%

A nonprobabilistic structural damage identification approach based on orthogonal polynomial expansion and interval mathematics

Wang

Qiu

et al. 2019

Struct Control Health Monit

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In this study, a nonprobabilistic structural damage identification approach based on orthogonal polynomial expansion and interval mathematics is proposed for uncertainty-oriented damage identification with insufficient parametric information. A deterministic damage identification method is firstly reviewed. By means of a nonnegative least squares algorithm that combines truncated singular value decomposition and the L-curve method, the illposed damage equation is then solved, and the damage index is simultaneously obtained. In terms of the uncertainty quantification issue, the material elastic modulus and density in structural models herein are described as unknownbut-bounded interval numbers. On the basis of orthogonal polynomial expansion and interval mathematics, the set collocation methodology is presented to determine the upper and lower bounds of the damage index. The interval bounds can provide supports for structural health diagnosis under uncertain conditions. A nonprobabilistic identification using the first-order Taylor series expansion is also proposed for comparison's purpose. Two numerical applications and one test are finally given under single damage and multidamage scenarios with different uncertainty degree. Results suggest that the presented nonprobabilistic structural damage identification approach can identify the damage location and degree with consideration of uncertainties. KEYWORDSinterval mathematics, interval-based set collocation methodology, nonprobabilistic structural damage identification, the first-order Taylor expansion | INTRODUCTIONVarious mechanical equipments and engineering structures like aircrafts and civil structures suffer from deterioration due to environmental erosion, overloading, accidental bumping, and other factors in service. 1 The deteriorations in structures accumulate as time goes on and are gradually reflected by performance degradation and structural damage. In order to avoid safety accidents, how to detect, locate, and characterize damage in structural and mechanical systems and estimate their residual life become critical problems. 2 Structural health monitoring and damage identification emerge in such demand and have attracted many researchers' attentions.

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Interval analysis based robust truss optimization with continuous and discrete variables using mix-coded genetic algorithm

Cited by 23 publications

References 37 publications

Nonlinear dynamic analysis of the bridge bearing and genetic algorithm–based optimization for seismic mitigation

Nonlinear dynamic analysis of the bridge bearing and genetic algorithm–based optimization for seismic mitigation

Reliability reallocation for cost uncertainty of fuel cell vehicles via improved differential evolution algorithm

A nonprobabilistic structural damage identification approach based on orthogonal polynomial expansion and interval mathematics

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