Predicting the Probability of Failure in Truss Structures Using Artificial Neural Networks
Reliability and safety evaluation is a significant topic in structural engineering. The main issues in structural reliability assessment are the excessive computational cost as well as the accuracy. Artificial neural network (ANN) can be used for structural reliability assessment. The ANN used in th...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Pouyan Press
2025-10-01
|
| Series: | Journal of Soft Computing in Civil Engineering |
| Subjects: | |
| Online Access: | https://www.jsoftcivil.com/article_209137_162f915a6a14d834ad81685fbe80e90c.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Reliability and safety evaluation is a significant topic in structural engineering. The main issues in structural reliability assessment are the excessive computational cost as well as the accuracy. Artificial neural network (ANN) can be used for structural reliability assessment. The ANN used in this article is a multilayer perceptron network (MLP) type. This study aims to evaluate the reliability of truss structures using MLP. In order to train and test the neural network, a database is created for the problem. Truss samples are generated based on a uniform distribution of optimal truss sections. The probability of failure in each truss sample is calculated using the Monte Carlo simulation, taking into account the normal distribution of random variables such as the cross-sectional area of the bars and the applied load. The limitation of node displacement is considered as a limit state function. The data was split as 60% for training and 40% was used for testing and validation. The optimal number of neurons in each layer is determined through a trial-and-error process, based on the lowest error of the predicted data and the highest regression coefficient of responses. Finally, the probability of failure of three benchmark truss structures is calculated as numerical examples using the MLP and compared with the values obtained from simulation. It has been shown that after training and preparing the MLP neural network, the accuracy of the MLP prediction process is proportional to 106 and 103 interactions for MCS and LHS, respectively. |
|---|---|
| ISSN: | 2588-2872 |