Supporting Data for "Intelligent prediction of steel corrosion in cementitious materials via machine learning"

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.Chapter 3 contains laboratory corrosion data of steel under carbonation conditions. The dataset includes 16 variables and 180 groups, along with code implementing relevant regression algorithms.Chapter 4 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.Chapter 5 provides data for corrosion probability prediction, including 4 variables and 535 groups. It also contains code for probabilistic classification and corrosion mapping.Chapter 6 includes corrosion data of steel under drying-wetting cycling conditions, with 10 variables and 284 groups. The folder also contains code for regression analysis.Chapter 7 provides code related to symbolic learning for interpretable corrosion modeling, based on the data compiled from previous chapters.

本数据集配套于博士学位论文《基于机器学习的胶凝材料中钢筋锈蚀智能预测》，该论文聚焦于开发数据驱动与物理信息增强机器学习（physics-informed machine learning）模型，用于预测胶凝材料环境中钢筋的锈蚀行为。
本数据集按照论文章节（第3章至第7章）进行组织，各模块均包含对应分析环节所用的原始实验数据与相关代码。
第3章收录碳化环境下钢筋的实验室锈蚀数据，该数据集包含16个变量与180组样本，同时附带实现相关回归算法的代码。
第4章收录氯离子侵蚀环境下钢筋的实验室锈蚀数据，包含15个变量与95组样本；此外还收录了来自文献的锈蚀数据，涵盖5个变量与81组样本。该章节文件夹提供了回归模型与迁移学习模型的实现代码。
第5章提供锈蚀概率预测相关数据集，包含4个变量与535组样本，同时附带概率分类与锈蚀映射的实现代码。
第6章收录干湿循环环境下钢筋的锈蚀数据，包含10个变量与284组样本，该章节文件夹同时附带回归分析的实现代码。
第7章提供基于前文各章节整理的数据、用于可解释锈蚀建模的符号学习相关代码。