This paper presents the first universal cloud-based database of recycled aggregate concrete (RAC) durability, named RACBase. The database collects experimental data on the durability of RAC from published literature all over the globe. Each set of data consists of the literature source, materials information, and the unified test result. The resistance to chloride ion attack, carbonation, sulfate attack, and freeze-thaw for RAC is quantified by the passed electric charge, carbonation depth, compressive strength loss, and durability factor, respectively. Based on the web interface, RACBase provides the functions of searching, adding, acquiring, and storing data, and gives the statistical distribution of data for each durability test. Users can freely access RACBase at https://www.racbase.com and browse and upload their data after online registration. One illustration of building a machine learning model for predicting the chloride resistance of RAC is presented to show the potential application of the database. The extreme gradient boosting model shows superiority to the artificial neural network and random forest models with the R2 of 0.975, RMSE of 246.719, and MAPE of 0.081. RACBase enriches the way for users to conduct in-depth research on the durability of RAC and construct performance prediction models.