Reliability analysis of strength models for short-concrete columns under concentric loading with FRP rebars through Artificial Neural Network

2021 ◽  
Vol 42 ◽  
pp. 102497
Author(s):  
Afaq Ahmad ◽  
Mohamed Elchalakani ◽  
Nouran Elmesalami ◽  
Ahmed El Refai ◽  
Farid Abed
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reliability Analysis ◽  
Concrete Columns ◽  
Artificial Neural ◽  
Strength Models ◽  
Concentric Loading

scholarly journals Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhou Yang ◽  
Unsong Pak ◽  
Cholu Kwon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reliability Analysis ◽  
Sampling Method ◽  
Back Propagation ◽  
Simulation Method ◽  
Back Propagation Neural Network ◽  
Element Analysis ◽  
Analysis Methods ◽  
Artificial Neural

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.

scholarly journals A Multi-State Model for Reliability Analysis of Metal Sheet Manufacturing Process Using Artificial Neural Network Technique

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Anil Chandra ◽  
Surbhi Gupta ◽  
Chandra Kant Jaggi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reliability Analysis ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Metal Sheet ◽  
Time Period ◽  
Artificial Neural ◽  
Over Time

A manufacturing system is governed by its various processes upon which its efficiency is dependent. Since failure results in considerable losses, many manufacturing systems have certain redundancies for some processes. These redundancies cause the system to work under different efficiency states called multi-state elements. In this paper, various processes of metal sheet manufacturing unit have been categorized as subsystems to determine the multi-state probabilities of its different efficiency states. Artificial Neural Network Technique (ANN) has been used to estimate the change in these multi-state probabilities over time. The ANN has also been used to estimate variation in upstate and downstate probabilities of the system for a particular-time period. The results have been used to determine variation in profit over time for the system.

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