Supporting Data for "Intelligent prediction of steel corrosion in cementitious materials via machine learning"
<p dir="ltr">This dataset is associated with the PhD thesis "Intelligent prediction of steel corrosion in cementitious materials via machine learning", which focuses on the development of data-driven and physics-informed machine learning models for predicting the corrosion...
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2025
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| Summary: | <p dir="ltr">This dataset is associated with the PhD thesis "Intelligent prediction of steel corrosion in cementitious materials via machine learning", which focuses on the development of data-driven and physics-informed machine learning models for predicting the corrosion behavior of steel in cementitious environments. The dataset is structured according to the thesis chapters (Chapter 3 to Chapter 7), with each part containing the original experimental data and relevant code used in the corresponding analyses.</p><ul><li><b>Chapter </b><b>3</b> contains laboratory corrosion data of steel under carbonation conditions. The dataset includes 16 variables and 180 groups, along with code implementing relevant regression algorithms.</li><li><b>Chapter </b><b>4</b> contains laboratory corrosion data of steel under chloride ingress conditions, comprising 15 variables and 95 groups. It also includes literature-sourced corrosion data with 5 variables and 81 groups. The folder provides code for both regression and transfer learning models.</li><li><b>Chapter </b><b>5</b> provides data for corrosion probability prediction, including 4 variables and 535 groups. It also contains code for probabilistic classification and corrosion mapping.</li><li><b>Chapter </b><b>6</b> includes corrosion data of steel under drying-wetting cycling conditions, with 10 variables and 284 groups. The folder also contains code for regression analysis.</li><li><b>Chapter </b><b>7</b> provides code related to symbolic learning for interpretable corrosion modeling, based on the data compiled from previous chapters.</li></ul><p></p> |
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