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.