Regression Models for Estimating the Stress Concentration Factor of Rectangular Plates
Estimating Stress Concentration Factors (SCF) guarantees resistance and durability criteria in structures and design components. Failure to correctly identify the SCFs could lead to premature material failure. In this chapter, eight regression models were used to predict the SCF. The regression mode...
| Autores: | , |
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| Tipo de documento: | capítulo de livro |
| Estado: | Versão publicada |
| Data de publicação: | 2024 |
| País: | México |
| Recursos: | Universidad Autónoma de Ciudad Juárez |
| Repositório: | Repositorio Institucional de la Universidad Autónoma de Ciudad Juárez |
| OAI Identifier: | oai:uacj.mx:oai:cathi.uacj.mx:20.500.11961ir-30442 |
| Acesso em linha: | https://doi.org/10.1007/978-3-031-66731-2_17 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Stress concentration factor Rectangular plates Polynomial curve fitting Artificial intelligence Regression models Random sample consensus Ridge regression LASSO regression Elastic Net Random forest regression Support vector regression Polynomial regression info:eu-repo/classification/cti/7 |
| Resumo: | Estimating Stress Concentration Factors (SCF) guarantees resistance and durability criteria in structures and design components. Failure to correctly identify the SCFs could lead to premature material failure. In this chapter, eight regression models were used to predict the SCF. The regression models were multiple linear regression, random sample consensus, ridge regression, LASSO regression, elastic net, random forest regression, support vector regression, and polynomial regression. The models were trained on a dataset resulting from a two-dimensional Finite Ele ment Analysis from the Finite Element Method for different values of the parameters: large, width, and circular hole radius in a tensile plate. Least squares polynomial equations were fitted to these design points. The performance of the models was compared using the MSE, RMSE, MAE, MAPE, and R2 metrics. The random forest regression performed the best. |
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