GOALI/Collaborative Research: Future Underground Landscape - Learning from Large Excavations in a Complex Urban Environment

GOALI/合作研究：未来地下景观 - 从复杂城市环境中的大型挖掘中学习

• 批准号: 1917036
• 负责人: Youssef Hashash
• 金额: $ 44.39万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917036&HistoricalAwards=false
• 关键词: GOALI; Collaborative; Research; Future; Underground

In megacities such as Los Angeles, San Francisco, and New York, urban sprawl is no longer a viable or desirable option, so demand for underground space - a non-renewable resource - continues to increase. A challenge in many urban areas is in constructing and developing below surface space while limiting impacts to existing surface and subsurface development in heavily-built environments and varying soil conditions. New and improved engineering tools are needed to meet ever more stringent design goals. Despite their capabilities, the reliability of our available predictive models is limited by the sparse data from construction projects. Major expansions of the Los Angeles Metro system approved by Los Angeles voters as part of Measure R and Measure M provide a unique opportunity to collect and utilize data needed to improve our understanding of excavation performance. This Grant Opportunities for Academic Liaison with Industry (GOALI) collaborative research project brings together personnel from academia, public agencies and private industry to leverage over-$11-billion-dollar public investments in large scale excavations to improve the understanding of the influence of ground conditions and construction processes on excavations. This project will provide tools and techniques, including modeling, data sharing, and investigation methods, to enhance the cost-effectiveness of urban underground construction. The successful completion of this project and the acquired knowledge provide valuable information that can be applied to large upcoming underground infrastructure investments throughout the US.The targeted excavations, completed or under construction, are geographically distributed and variable in size and settings, and provide ideal testbeds for research activities aimed at learning from the observed excavation performance. We will access 13 large excavations in the LA Metro system and more importantly, deploy advanced and emerging technologies to acquire and curate unique time sensitive and perishable data on excavation and soil response beyond what is currently available in our empirical databases and numerical models. A novel framework for data sharing, storage and authentication based on the rapidly evolving block chain technology will be developed for the first time for geotechnical and construction use. The project will significantly advance state-of-the-art excavation modeling, through tools that employ conventional and deep learning-based soil modeling techniques and three-dimensional city block scale models, as well as the development of empirical models with application to urban excavations nationally and globally. In addition, this project will engage undergraduate students, with a special focus on low-income, first-generation college students from CSULB, to work with graduate research assistants from UCI and UIUC. There are currently very few students graduating from US higher education programs with training in underground construction. Students working on this project will gain invaluable real-world experience on a major construction project, and will interact with both researchers and consultants.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.

在洛杉矶、弗朗西斯科和纽约等特大城市，城市扩张不再是一个可行或可取的选择，因此对地下空间--一种不可再生资源--的需求继续增加。许多城市地区面临的一个挑战是，在建筑密集的环境和不同的土壤条件下，在地表以下空间进行建设和开发，同时限制对现有地表和地下开发的影响。需要新的和改进的工程工具来满足越来越严格的设计目标。尽管它们的能力，我们可用的预测模型的可靠性是有限的，从建设项目的稀疏数据。作为措施R和措施M的一部分，洛杉矶选民批准的洛杉矶地铁系统的重大扩建为收集和利用提高我们对挖掘性能的理解所需的数据提供了一个独特的机会。这个学术界与工业界联络的赠款机会（GOALI）合作研究项目汇集了来自学术界，公共机构和私营企业的人员，以利用超过110亿美元的大型挖掘公共投资，以提高对地面条件和施工过程对挖掘影响的理解。该项目将提供工具和技术，包括建模，数据共享和调查方法，以提高城市地下建设的成本效益。该项目的成功完成和所获得的知识提供了有价值的信息，可以应用于整个美国即将进行的大型地下基础设施投资。有针对性的挖掘，已完成或正在建设中，在地理上分布，大小和设置可变，并为研究活动提供理想的试验台，旨在从观察到的挖掘性能中学习。我们将访问洛杉矶地铁系统中的13个大型挖掘，更重要的是，部署先进的新兴技术，以获取和管理关于挖掘和土壤响应的独特的时间敏感和易腐数据，这些数据超出了我们的经验数据库和数值模型中目前可用的数据。基于快速发展的区块链技术的数据共享，存储和认证的新框架将首次用于岩土工程和建筑用途。该项目将通过采用传统和基于深度学习的土壤建模技术和三维城市街区规模模型的工具，以及应用于全国和全球城市挖掘的经验模型的开发，显着推进最先进的挖掘建模。此外，该项目将吸引本科生，特别关注CSULB的低收入第一代大学生，与UCI和UIUC的研究生研究助理合作。目前，从美国高等教育项目毕业的学生很少接受地下建筑培训。参与该项目的学生将获得重大建设项目的宝贵现实经验，并将与研究人员和顾问进行互动。该奖项反映了NSF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。

项目成果

Evaluation of Empirical Methods for Estimating Tunneling-Induced Ground Movements—Los Angeles Metro K Line Crenshaw/LAX Transit Project

估算隧道引起的地面运动的经验方法评估——洛杉矶地铁 K 线克伦肖/洛杉矶国际机场交通项目

• DOI: 10.1061/9780784484708.047
• 发表时间: 2023
• 期刊: ASCE Geo-Congress 2023 : Geotechnical Systems from Pore-Scale to City-Scale . 2023
• 作者: Zhao, Wendi;Hashash, Youssef M.;Jasiak, Maksymilian;Lawrence, Jack;Hutter, Abby;Bernard, Timothy;Josephina Szewczyk, Alicia;Beaino, Charbel;Pearce, Michael;Lemnitzer, Anne
• 通讯作者: Lemnitzer, Anne

Effects of Tar on CPT and Shear Wave Velocity Correlations for the LA Metro Purple Line (D-Line)

焦油对洛杉矶地铁紫线（D 线）CPT 和剪切波速度相关性的影响

• DOI: 10.1061/9780784484708.019
• 发表时间: 2023
• 期刊: ASCE Geo-Congress 2023
• 作者: Chrysovergis, Taki;Lemnitzer, Anne;Star, Lisa M.;Rodriguez, Joseph;Hashash, Youssef;Sathialingam, Namasivayam;Cording, Edward;O’Rourke, Thomas D.
• 通讯作者: O’Rourke, Thomas D.

Semi-Empirical Method for Excavation-Induced Surface Displacements—Los Angeles Metro K Line Crenshaw/LAX Transit Project

开挖引起的表面位移的半经验方法 - 洛杉矶地铁 K 线克伦肖/洛杉矶国际机场交通项目

• DOI: 10.1061/9780784484708.046
• 发表时间: 2023
• 期刊: ASCE Geo-Congress 2023
• 作者: Beaino, Charbel;Hashash, Youssef M.;Bernard, Timothy;Hutter, Abby;Jasiak, Maksymilian;Lawrence, Jack;Szewczyk, Alicia;Zhao, Wendi;Pearce, Michael;Lemnitzer, Anne
• 通讯作者: Lemnitzer, Anne