Experimental investigation of early strain heterogeneities and localizations in polycrystalline α-Fe during monotonic loading

Machine Learning-Assisted Tensile Modulus Prediction for Flax Fiber/Shape Memory Epoxy Hygromorph Composites

“…According to Chicco et al, R 2 is often the most informative stat in many cases compared to other measures such as mean absolute error (MAE) and percentage variation (MAPE), symmetric mean absolute percentage error (SMAPE), m square error (MSE), and the square root of mean square error (RMSE) [33]. MAE calcul the average absolute difference between the actual and expected values of a dat Therefore, R 2 is recommended as the standard measure for evaluating regression anal across various scientific disciplines [48]. These results emphasize the significant influence of wax fiber orientation and environmental conditions on the mechanical properties of hygromorph composites.…”

“…To compare the performance of the two machine learning models, a statistical measure called the coefficient of determination, R 2 , was employed. R 2 is commonly used to evaluate the effectiveness of machine learning models [48]. According to Chicco et al, R 2 is often the most informative statistic in many cases compared to other measures such as mean absolute error (MAE) and its percentage variation (MAPE), symmetric mean absolute percentage error (SMAPE), mean square error (MSE), and the square root of mean square error (RMSE) [33].…”

“…MAE calculates the average absolute difference between the actual and expected values of a dataset. Therefore, R 2 is recommended as the standard measure for evaluating regression analyses across various scientific disciplines [48]. An R 2 value of 0.94 was obtained for the decision tree model, while the random forest model achieved a value of 0.95.…”

Prediction of Concrete Peak Load and Compressive Failure Strength Using Machine Learning

“…Therefore, the fact that the machine learning models are highly affected by this parameter is very relevant. Machine learning is a very pertinent method that should be considered to predict output values from several input variables that are not linearly dependent in other cases such as the study of mechanical properties of alloys [24][25][26][27][28][29][30], composites [31][32][33][34][35] or ceramics materials [36][37][38].…”

Microplasticity and macroplasticity behavior of additively manufactured Al-Mg-Sc-Zr alloys: In-situ experiment and modeling

“…Fundamentally, microplasticity accounts for the transition from elastic to plastic activity. It is known that before macroscopic yielding, microplasticity deformation has already occurred (Berger et al, 2022). However, there are still many unanswered questions regarding microplasticity deformation (Arechabaleta et al, 2016;Li and Wagoner, 2021;Maaß and Derlet, 2018).…”