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

在洛杉矶、旧金山和纽约等特大城市，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，如何在建造密集的环境和不同的土壤条件下建造和开发地下空间，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。尽管我们的预测模型功能强大，但我们现有的预测模型的可靠性受到建筑项目稀疏数据的限制。作为措施R和措施M的一部分，洛杉矶选民批准对洛杉矶地铁系统进行重大扩建，这为收集和利用改善我们对挖掘性能的了解所需的数据提供了一个独特的机会。GOALI学术联络机会(GOALI)合作研究项目汇集了学术界、公共机构和私营行业的人员，利用超过110亿美元的公共投资进行大规模挖掘，以提高对地面条件和施工过程对挖掘影响的了解。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宝贵的信息，可以应用于美国各地即将进行的大型地下基础设施投资。目标挖掘，无论是已完成的还是正在建设的，都是地理上分布的，大小和环境都不同，为旨在从观察到的挖掘性能中学习的研究活动提供了理想的试验台。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来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烂的数据，而不是我们现有的经验数据库和数值模型中提供的数据。基于快速发展的区块链技术的数据共享、存储和身份验证的新框架将首次开发用于岩土和建筑用途。该项目将通过使用基于传统和深度学习的土壤建模技术和三维城市街区比例模型的工具，以及开发应用于全国和全球城市挖掘的经验模型，显著推动最先进的挖掘建模。此外，该项目将吸引本科生，特别是来自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并接受地下建筑培训的学生寥寥无几。参与该项目的学生将在重大建设项目中获得宝贵的现实世界经验，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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.