Artificial Intelligence Approach to Predict Elevated Temperature Cyclic Oxidation of Fe-Cr and Fe-Cr-Ni Alloys

Article Metallurgy & Metallurgical Engineering

Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

R. Pillai et al.

Summary: Machine learning offers many advantages in predicting material properties, but limited efforts have been made to predict alloy oxidation kinetics and spallation behavior due to the lack of consistently measured and sufficient experimental data and the complexity in oxidation behavior of multicomponent high-temperature alloys.

OXIDATION OF METALS (2022)

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Article Engineering, Civil

Strength prediction of concrete-filled steel tubular columns using Categorical Gradient Boosting algorithm

Seunghye Lee et al.

Summary: The CatBoost algorithm is used to predict the ultimate axial strength of CFST columns with high accuracy based on training and testing of 3103 tests. The results show high prediction accuracy with R2 values ranging from 0.964 to 0.996. The algorithm provides nearly similar experiment results with unity mean values, making it a reliable tool for predicting the strength of CFST columns.

ENGINEERING STRUCTURES (2021)

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Article Materials Science, Multidisciplinary

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Jian Peng et al.

Summary: This study introduces a data analytics approach to predict the oxidation rate constant of NiCr-based alloys and has successfully trained machine learning models to accurately predict experimental parabolic rate constants. The research demonstrates the potential of machine learning approaches in predicting the oxidation kinetics of alloys.

NPJ MATERIALS DEGRADATION (2021)

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Article Computer Science, Theory & Methods