Strength and durability prediction of sustainable concrete incorporating rubber aggregate and micro silica

Abstract

The ability to predict concrete compressive strength is important in early-stage mix design screening. Thus, the predictions made from these models must match the actual data that was used to train and validate them. Therefore, this study assesses machine learning models using the publicly available UCI concrete compressive strength data set that has 1030 tabular entries. The tabular entries are defined by the following variables; cement, blast furnace slag, fly ash, water, superplasticiser, coarse aggregate, fine aggregate, curing time and measured compressive strength. As such, the study is framed as a transparent tabular prediction benchmark rather than as an experimental evaluation of microsilica and rubber aggregate based concretes. It does not claim to have evaluated any new test pieces, nor does it make any claims regarding SEM imaging or microstructural measurement of those test pieces. Further, there is no claim related to the use of any durability testing procedures. Several machine learning algorithms including support vector regression (SVR), random forest, XGBoost, artificial neural network (ANN) and an optimised tabular ensemble (TE), were each developed under the same leakage-controlled validation methodology. Performance metrics were provided based on both the native test set(s) and a common subset of the test set(s). Metrics included R 2 , RMSE, MAE, MAPE along with residual diagnostic and graphical error analyses. The new framework also emphasizes reproducibility, fairness of comparison and transparency of data domain limitations. In addition to supporting computer-based screening of conventional concrete strength databases, its results indicate what will be required for future studies that contain micro silica, rubber aggregates, microstructural measurements and/or durability measurements in their respective databases.