Machine learning for predicting compressive strength of sustainable cement paste incorporating copper mine tailings as supplementary cementitious materials

Abstract

Copper mining produces significant amounts of copper mine tailings (CMT), necessitating appropriate waste handling and disposal practices. By substituting a portion of cement with CMT as supplementary cementitious materials (SCMs), we aim to address two environmental issues simultaneously: reducing copper mine waste in landfills and decreasing embodied carbon by using less cement. The exploration of CMT recycling as a cement replacement requires evaluation of its impact on material performance, such as compressive strength. In this paper, we address this by machine learning that features data fusion of large public data with our own small data on compressive strength of CMT-incorporated cement. We developed and critically evaluated three machine learning models: a simple linear model, Gaussian process, and random forest that predict the compressive strength of CMT-incorporated cement pastes with different mix designs (e.g., varying amounts of CMT and water-binder ratios) and curing ages. Hyperparameters in the random forest model were tuned using Bayesian optimization. Following a comprehensive evaluation of the models, we find that the random forest model can accurately estimate the compressive strength of cement paste across the mix designs. Furthermore, results from SHapley Additive exPlanation (SHAP), Individual Conditional Expectation (ICE), and Partial Dependence Plots (PDP) revealed that cement, ground-granulated blast furnace slag, superplasticizers, and curing ages positively influence compressive strength. This study contributes to acceleration of sustainable material technology to obtain the best mix design and desired compressive strength.