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

该学院早期职业发展（Career）奖将为在采矿岩土技术中创建数据驱动、物理指导和基于性能的多尺度程序奠定基础。该项目利用新兴数据科学领域提供的前所未有的机会，重塑采矿岩土技术领域，改变高风险基础设施评估的范式，重点是尾矿储存设施。在过去的十年中，尾矿储存设施的故障在世界范围内造成了前所未有的环境后果和生命损失。在美国，类似于最近其他国家的失败，可能会对环境、州经济和当地社区造成巨大破坏。因此，考虑到新的全球尾矿标准，尾矿储存设施的弹性设计和状态评估对于亚利桑那州、内华达州、科罗拉多州、犹他州等采矿活跃地区至关重要，并且比以往任何时候都更加重要。在此背景下，该项目将为提高尾矿储存设施的复原力提供根本性的见解，并为提高总体采矿基础设施的复原力设定新的标准。该综合教育计划将通过以下方式培养具有数据科学素养的下一代岩土工程尾矿工程师：(1)佐治亚理工学院STEM中心和促进采矿基础设施恢复能力的美国机构的本科生、研究生和K-12学生；佐治亚理工学院垂直一体化项目；（三）提升教育的机会。外展计划将为西班牙裔/拉丁裔学生建立一项同伴试点辅导计划，以补充私人学院目前的外展活动。本项目的研究目标是：(i)通过数据科学研究尾矿的多尺度力学响应，发现高维相互作用；（ii）调查评估原地状态的基本问题；（iii）探索在采矿岩土技术中制定新的数据驱动和基于性能的程序。为了实现这些目标，该项目采用综合方法，考虑数据科学、实验室测试、微观结构测量、现场测试和数值模拟，为中间材料（如矿山尾矿）的多尺度响应带来新的见解，考虑(i)微观结构特征；（ii）固有和诱导的各向异性和循环荷载的作用；（iii）现场规模的系统效应；（四）数据驱动的状态反演方法。从这项研究中获得的见解将用于探索数据科学的价值，并形式化其在揭示矿山尾矿力学特性的高维相互作用以及使用机器和主动学习制定新的数据驱动和基于性能的程序方面的应用。该项目将使PI能够为数据科学、采矿岩土技术和基于性能的工程的跨学科领域奠定基础，从而向岩土技术和灾害社区展示采用数据驱动方法的机会。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.