Comparative Analysis of Machine Learning Models for Predicting Crack Propagation under Coupled Load and Temperature

Abstract

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.

Fracture Behaviour of Aluminium Alloys under Coastal Environmental Conditions: A Review

“…Investigating how temperature and humidity interact to affect fracture toughness and FCGR behaviour is critical to ensure aluminium alloy components' reliable and safe performance in coastal environments. The effects of humidity [192,193] and temperature [48,[194][195][196] on the fatigue properties of materials [197][198][199][200] were independently studied in earlier research. For the purpose of forecasting fatigue life and creating empirical equations for predicting and associating FCGR with fracture toughness, a thorough understanding of the combined effects of these components is essential.…”

Investigation of the Combined Influence of Temperature and Humidity on Fatigue Crack Growth Rate in Al6082 Alloy in a Coastal Environment

“…Investigating the combined impact of temperature and humidity on FCG behaviour is still critical for ensuring the reliable and safe performance of components made from the Al6082 alloy in coastal environments. Previous studies have separately examined the influence of temperature [24][25][26][27] and humidity [28][29][30] on the fatigue properties of materials [31][32][33][34]. However, a comprehensive understanding of the combined impact of these factors is critical for accurately predicting fatigue life [35] and developing empirical equations for predicting and correlating FCGR with fracture toughness.…”