A Machine Learning-Based Approach to Recycling Mine Waste for Developing Sustainable Construction Materials
- Principal Investigator (PI): Hee-Jeong Kim, Civil and Architectural Engineering and Mechanics
- Co-PI: Angelina Anani, Mining and Geological Engineering
The goal of this research project is to maximize utilization of copper mine wastes as supplementary cementitious materials (SCM) or aggregate to achieve sustainability by using a machine learning-based approach to understanding the relationship between chemical and physical properties. Data from this trial will serve as preliminary results for additional grant proposals.
Research activities include collection of mine wastes and XRF, XRD data for mine tailings, fabricating concrete and measuring mechanical properties, and applying the machine learning-based approach. Principal outcomes from this effort will be the determination of the effect of copper tailings on compressive strength of concrete at different curing ages, and the determination of the effect of copper slag on compressive strength of concrete at different curing ages.