Case Studies in Construction Materials (Dec 2024)
Innovative hybrid machine learning models for estimating the compressive strength of copper mine tailings concrete
Abstract
The growing demand for copper and related materials in various industries is driving increased copper mining globally. This surge presents a substantial challenge in managing and responsibly disposing of large volumes of copper mine tailings (CMT). Incorporating CMT as supplementary cementitious materials (SCMs) in concrete addresses two significant environmental challenges simultaneously: reducing the accumulation of CMT waste in landfills and lowering the carbon footprint by reducing cement usage. The investigation into recycling CMT as a cement substitute involves a thorough assessment of its impact on the compressive strength (CS) of concrete. This research introduces innovative hybrid machine learning (ML) models for estimating the CS of CMT concrete, aiming to streamline strength assessment processes and save valuable resources. The method involves integrating features from large public datasets with the limited available data on the CS of CMT concrete. Support vector regression (SVR) was combined with advanced optimization techniques: firefly algorithm (FFA), grey wolf optimization (GWO) and particle swarm optimization (PSO) to create new hybrid models for forecasting the CS of CMT concrete. Additionally, traditional ML techniques like decision tree (DT) and random forest (RF) were used to compare with these SVR-based hybrids. All three hybrid models demonstrated strong performance, with SVR-FFA emerging as the most effective among them. Notably, SVR-FFA achieved the greatest R² score of 0.96, indicating superior predictive accuracy compared to SVR-PSO (0.92) and SVR-GWO (0.90). Additionally, the DT model attained an R² score of 0.88, while the RF model achieved an R² score of 0.84. Moreover, the SHapley additive exPlanations (SHAP) and partial dependence plots (PDP) analyses underscore the positive effects of curing age, cement, blast furnace slag, and superplasticizer on the CS of CMT concrete. A graphical user interface was developed for predicting the CS of CMT concrete, allowing for instant predictions without the need for conducting experiments.
