Reliability Evaluation of Bridges Based on Nonprobabilistic Response Surface Limit Method

Chen, Xuyong; Chen, Qian; Bian, Xiaoya; Fan, Jiawei

doi:10.1155/2017/1964165

Cited by 34 publications

(8 citation statements)

References 19 publications

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“…Substituting the results expressed by Equations (18) and (19) into the original limit state functions as shown in Equation (15) would make the simplified limit state functions available as follows:…”

Section: Advances In Civil Engineeringmentioning

confidence: 99%

“…Chen et al [6] and Fan and Chen [18] proposed the global optimal solution method and pointed out that the probable failure points only existed on extreme points and root points of the limit state function, avoiding the loss of probable failure points. Chen et al [19] proposed a nonprobabilistic response surface limit method based on the interval model, which effectively avoided the tedious and oscillating iteration process and enhanced computational efficiency and precision.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Advances In Civil Engineeringmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, scholars have carried out extensive research on reliability in their respective fields. Many models and methods are devised to predict and evaluate the reliability of different products and systems (see [1][2][3][4][5][6]). On the reliability of ammunition storage, the traditional methods are based on natural storage condition data and accelerated testing data, respectively.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ammunition Storage Reliability Based on Improved Ant Colony Algorithm and BP Neural Network

et al. 2019

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Storage reliability is an important index of ammunition product quality. It is the core guarantee for the safe use of ammunition and the completion of tasks. In this paper, we develop a prediction model of ammunition storage reliability in the natural storage state where the main affecting factors of ammunition reliability include temperature, humidity, and storage period. A new improved algorithm based on three-stage ant colony optimization (IACO) and BP neural network algorithm is proposed to predict ammunition failure numbers. The reliability of ammunition storage is obtained indirectly by failure numbers. The improved three-stage pheromone updating strategies solve two problems of ant colony algorithm: local minimum and slow convergence. Aiming at the incompleteness of field data, “zero failure” data pretreatment, “inverted hanging” data pretreatment, normalization of data, and small sample data augmentation are carried out. A homogenization sampling method is proposed to extract training and testing samples. Experimental results show that IACO-BP algorithm has better accuracy and stability in ammunition storage reliability prediction than BP network, PSO-BP, and ACO-BP algorithm.

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“…There are many inevitable uncertainties in practical structure engineering problems [1]. At the same time, with the intensive requirement of high product quality and reliability and therefore quantifying, controlling and managing the effects of uncertainty are important, sometimes even imperative [2].…”

Section: Introductionmentioning

confidence: 99%

“…Guo et al [14] proposed a measure system and analysis method of "nonprobabilistic reliability" by quantifying the uncertain structural parameters as interval variables. Chen et al [1] proposed a nonprobabilistic response surface limit method to perform nonprobabilistic reliability analysis for the structures based on the interval model. The possibility theory is suitable for dealing with epistemic uncertainty of conflict-free information, because the evidence from different experts is always consistent in possibility theory.…”

Section: Introductionmentioning

confidence: 99%

Discretization Analysis Method of Hybrid Reliability Based on Evidence Theory

Tang

2018

Mathematical Problems in Engineering

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Aiming at the problem that various types of uncertainties, such as randomness, fuzziness, and interval, coexist in structure reliability analysis, a discretization analysis method of hybrid reliability for uncertain structures is proposed based on evidence theory (ET) in this article. Firstly, in order to establish a hybrid reliability model based on ET, a generalized density method (GDM) is developed to transform the fuzzy variables into equivalent random variables on the basis of the entropy equivalent method (EEM). Based on the discrete property of the basic probability assignment (BPA) in evidence theory, the random variables and fuzzy variables (equivalent random variables) are both discretized into subintervals according to six-sigma rule. Then, the BPA of each subinterval is solved and all focal elements are assigned BPA, so the evidence structure characterization of random and fuzzy variables is realized. Secondly, using Fmincon function based on the sequential quadratic programming (SQP) algorithm in MATLAB, the minimum and maximum values of performance function over each focal element can be acquired directly. Meanwhile, the production rules are used to judge the belonging of focal elements and classify them, so the numerical calculation of belief measure and plausibility measure is also realized. Finally, combined with the Monte Carlo Simulation (MCS) method, an engineering example is provided to demonstrate the feasibility and accuracy of the proposed method.

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Reliability Evaluation of Bridges Based on Nonprobabilistic Response Surface Limit Method

Cited by 34 publications

References 19 publications

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

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

Prediction of Ammunition Storage Reliability Based on Improved Ant Colony Algorithm and BP Neural Network

Discretization Analysis Method of Hybrid Reliability Based on Evidence Theory

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