We believe that data science and artificial intelligence will substantially change the ways research is carried out. Therefore, we promote sharing of reliable databases with the science and engineering communities. See below representative databases we share. Please contact us for the latest versions or other databases generated from our own research projects and mined from the open literature.
Surface Chloride Concentration of Marine Concrete
This database describes how the surface chloride concentration of marine concrete is determined by the mixture proportions of concrete, the environmental conditions, and age. It includes 730 full datasets mined from the literature, and can be used for training and validation of relevant machine learning models. The database has been published on Cement and Concrete Research, and can be downloaded from the journal’s web page or the database link. Another option to download this database is to click here. To use this database, please cite:
Cai, R., Han, T., Liao, W., Huang, J., Li, D., Kumar, A., Ma, H.* (2020). Prediction of Surface Chloride Concentration of Marine Concrete using Ensemble Machine Learning. Cement and Concrete Research, 163, 106164. doi: 10.1016/j.cemconres.2020.106164
Reactivity/Cumulative Heat Release of (Alternative) Supplementary Cementitious Materials
This database includes the chemical composition, physical properties, processing conditions, age, and reactivity/cumulative heat measured using calorimetry (e.g., the R3 method). Using this database, one can analyze the effects of >60 parameters on the reactivity of (alternative) supplementary cementitious materials, as shown below.
The database is available here. To use it, please cite: