CAREER: Towards the Next Generation of Data-Driven and Performance-Based Multiscale Procedures in Mining Geotechnics

This Faculty Early Career Development (CAREER) award will set the stage for creating data-driven, physics-guided, and performance-based multiscale procedures in mining geotechnics. This project capitalizes on the unprecedented opportunities provided by the emerging field of data science to reshape the field of mining geotechnics and shift the paradigms for the assessment of high-risk infrastructure, focusing on tailings storage facilities.

During the last decade, tailings storage facility failures have caused unprecedented environmental consequences and loss of human lives worldwide. A failure in the United States, similar to other recent ones, may cause dramatic damage to the environment, state economies, and local communities. Therefore, the resilient design and condition assessment of tailings storage facilities are vitally important for regions where mining is active, such as Arizona, Nevada, Colorado, Utah, amongst others, and have become more relevant than ever, considering the new global tailings standards.

In this context, this project will provide fundamental insights to improve the resilience of tailings storage facilities and set new standards to enhance the resilience of mining infrastructure in general. The integrated educational plan will contribute to creating the next generation of geotechnical tailings engineers with literacy in data science by engaging undergraduate, graduate, and K-12 students in (i) Georgia Tech STEM centers and US institutions that promote the resilience of mining infrastructure; (ii) Georgia Tech vertically integrated projects; and (iii) education enhancement opportunities.

The outreach plan will establish a peer pilot mentoring program for Hispanic/Latinx students that complements the PI’s current outreach activities.

The research objectives of this project are to (i) investigate the multiscale mechanical response of mine tailings and discover high-dimensional interactions through data science; (ii) investigate the fundamental problem of assessing in situ states; and (iii) explore the formulation of novel data-driven and performance-based procedures in mining geotechnics. Towards these objectives, this project uses an integrated approach that considers data science, laboratory tests, microstructural measurements, field tests, and numerical simulations to bring novel insights to the multiscale response of intermediate materials such as mine tailings considering (i) microstructure signatures; (ii) the role of inherent and induced anisotropy and cyclic loadings; (iii) field-scale system effects; and (iv) data-driven approaches for state inversion.

Insights gained from this research will be used to probe the value of data science and formalize its use in unraveling high dimensional interactions of mechanical properties of mine tailings and formulating novel data-driven and performance-based procedures using machine and active learning. This project will allow the PI to establish the foundation for an interdisciplinary field at the convergence of data science, mining geotechnics, and performance-based engineering that can demonstrate to the geotechnical and hazard communities the opportunities of embracing data-driven approaches.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.