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

职业生涯：迈向采矿岩土工程中的下一代数据驱动和基于性能的多尺度程序

• 批准号: 2145092
• 负责人: Jorge Macedo
• 金额: $ 58.19万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145092&HistoricalAwards=false
• 关键词: CAREER; Towards; Next; Generation; Data

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.

该学院早期职业发展（CAREER）奖将为在采矿岩土工程中创建数据驱动，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，重塑采矿岩土工程领域，并改变高风险基础设施评估的范式，重点关注尾矿储存设施。在过去十年中，尾矿储存设施的故障在全世界造成了前所未有的环境后果和人命损失。美国的失败，类似于最近的其他失败，可能会对环境、州经济和当地社区造成巨大破坏。因此，尾矿储存设施的弹性设计和状况评估对于亚利桑那州、内华达州、科罗拉多、犹他州等采矿活跃地区至关重要，考虑到新的全球尾矿标准，这一点比以往任何时候都更加重要。在此背景下，该项目将提供基本见解，以提高尾矿储存设施的复原力，并制定新的标准，以提高采矿基础设施的总体复原力。综合教育计划将有助于创造下一代岩土尾矿工程师与数据科学素养，通过参与本科生，研究生和K-12学生（i）格鲁吉亚科技STEM中心和促进采矿基础设施恢复力的美国机构;（ii）格鲁吉亚科技垂直整合项目;和（iii）教育增强机会。外展计划将为西班牙裔/拉丁裔学生建立一个同行试点指导计划，以补充PI目前的外展活动。该项目的研究目标是：（i）调查尾矿的多尺度机械响应，并通过数据科学发现高维相互作用;（ii）调查评估原位状态的基本问题;（iii）调查尾矿的物理特性。以及（iii）探索采矿岩土工程中新的数据驱动和基于性能的程序的制定。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）固有的和诱导的各向异性和循环载荷的作用;（iii）现场尺度系统效应;（iii）微观结构特征。以及（iv）用于状态反转的数据驱动方法。从这项研究中获得的见解将用于探索数据科学的价值，并将其用于揭示尾矿机械性能的高维相互作用，并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。该项目将使PI能够为数据科学，采矿地质技术，和基于性能的工程，可以向岩土工程和危险社区展示拥抱数据的机会-该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查进行评估来支持的搜索.