Academic Journal
Comparative Analysis of Machine Learning Models for Predicting Crack Propagation under Coupled Load and Temperature
| العنوان: | Comparative Analysis of Machine Learning Models for Predicting Crack Propagation under Coupled Load and Temperature |
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| المؤلفون: | Intisar Omar, Muhammad Khan, Andrew Starr |
| المصدر: | Applied Sciences, Vol 13, Iss 12, p 7212 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| مصطلحات موضوعية: | crack propagation, machine learning, dynamic load, Random Forest Regressor, Support Vector Regression, Gradient Boosting Regressor, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| الوصف: | Crack propagation in materials is a complex phenomenon that is influenced by various factors, including dynamic load and temperature. In this study, we investigated the performance of different machine learning models for predicting crack propagation in three types of materials: composite, metal, and polymer. For composite materials, we used Random Forest Regressor, Support Vector Regression, and Gradient Boosting Regressor models, while for polymer and metal materials, we used Ridge, Lasso, and K-Nearest Neighbors models. We trained and tested these models using experimental data obtained from crack propagation tests performed under varying load and temperature conditions. We evaluated the performance of each model using the mean squared error (MSE) metric. Our results showed that the best-performing model for composite materials was Gradient Boosting Regressor, while for polymer and metal materials, Ridge and K-Nearest Neighbors models outperformed the other models. We also validated the models using additional experimental data and found that they could accurately predict crack propagation in all three materials with high accuracy. The study’s findings provide valuable insights into crack propagation behavior in different materials and offer practical applications in the design, construction, maintenance, and inspection of structures. By leveraging this knowledge, engineers and designers can make informed decisions to enhance the strength, reliability, and durability of structures, ensuring their long-term performance and safety. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/13/12/7212; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app13127212 |
| URL الوصول: | https://doaj.org/article/947c6e2d2afa44939075e132ad5fa8c8 |
| رقم الانضمام: | edsdoj.947c6e2d2afa44939075e132ad5fa8c8 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20763417 |
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| DOI: | 10.3390/app13127212 |