Abstract
This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method (MCS) than those of other methods (including BP-IS). In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems.
Highlights
Since brakes serve the essential purpose of slowing down and stopping vehicles, the reliability of brakes significantly impacts vehicle safety
finite element analysis (FEA) is used to obtain training sample of the ANN, the random variables of the structure are used for ANN input, the structure response is used for ANN output, and explicit form of the function relationship between the structure response and random variables can be established. e back propagation neural network (BP)-based MCS method is used to predict the structure reliability, and the result is compared with three reliability analysis methods including the traditional MCS, polynomial-FOSM, and BP-based advanced first-order second moment method (AFOSM) [27]. e combination of an adaptive radial basis function neural network (RBF) metamodeling technique and a first-order reliability method (FORM) proposes a new reliability analysis method for the practical tunnel engineering problems [28]
Vibration Reliability Analysis Method of the Drum Brake Based on artificial neural network-based important sampling method (ANN-IS). e ANN-IS method is used to conduct the drum brake vibration reliability analysis based on the theory in Section 2.1 using ANN samples of different sizes
Summary
Since brakes serve the essential purpose of slowing down and stopping vehicles, the reliability of brakes significantly impacts vehicle safety. Because the MCS method is very simple and easy to program, it is widely used for the reliability analysis of the engineering structures This method requires a significant computation effort for low probability of failure (POF) problem [9]. E ANN is widely used to solve reliability problems related to practical engineering structure In this setup, FEA is used to obtain training sample of the ANN, the random variables of the structure are used for ANN input, the structure response is used for ANN output, and explicit form of the function relationship between the structure response and random variables can be established. E combination of an adaptive RBF metamodeling technique and a FORM proposes a new reliability analysis method for the practical tunnel engineering problems [28]. Is study is structured as follows: Section 2 presents a vibration reliability analysis based on the ANN-IS method, Section 3 provides the numerical analyses and results, and Section 4 presents the discussion and conclusions
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