STTR Phase I: Underground Live - an innovative, advanced analytical tool for characterizing the subsurface and reducing underground construction risk

这项小型企业技术转让（STTR）第一阶段项目的更广泛影响/商业潜力是，通过开发基于计算软件的工具，使用数据驱动的高级分析技术实时创建和更新地面模型，降低地下民用基础设施建设的成本，并使新基础设施更具可持续性。民用基础设施越来越多地向地下移动，包括道路、交通、公用事业和设施。然而，地下建设的风险仍然是吸引投资的关键障碍。地下建筑的一个重大风险是影响设计和施工的地面条件和物理特性的高度不确定性，导致由于过度设计和/或施工期间的延误和失败而增加成本。本项目致力于提高对地面状况的了解，提供一个解决方案，在施工过程中以常规和自主的方式更新地面模型，充分利用在施工过程中收集的丰富数据。这项小型企业技术转移（STTR）第一阶段项目旨在开发一种技术解决方案，使背景分析地面属性和实时更新地面模型的过程自动化。几个技术挑战将得到解决。当前的反向分析实践在管理和整合地下基础设施项目的数据方面是非常劳动密集型和昂贵的。施工过程中的动力环境需要对数百种独特的隧道-结构相互作用进行4D反演分析。此外，反演在估计岩土参数方面的有效性，在技术的基本发展过程中仅在有限的情况下得到证实，需要进行验证。本研究的目标是：(1)开发自动整合岩土仪器监测、施工过程监测、现有基础设施、先验地质统计模型等数据的算法；(2)开发隧道施工与邻近结构、传感器和地面条件空间接近的岩土参数反演动态更新算法；(3)表征多种地基条件和隧道-结构相互作用下岩土参数反演效果；(4)了解传感布局和优化传感对反演效果的影响。第一阶段的工作将为开发“实时”地面建模工具奠定基础。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。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to reduce the cost of underground civil infrastructure construction and make new infrastructure more sustainable by developing a computational software-based tool to create and update ground models in real time using data-driven advanced analytics. Civil infrastructure is increasingly moving underground, including roadways, transit, utilities, and facilities. However, risk in underground construction remains a critical barrier to attracting investment. A significant risk in building underground is the high uncertainty in ground conditions and physical properties influencing design and construction, resulting in increased costs due to over-design and/or delays and failures during construction. This project strives to improve the understanding of ground conditions by providing a solution to update the ground models during construction in a routine and autonomous manner, making full use of the wealth of data collected during construction. This Small Business Technology Transfer (STTR) Phase I project aims to develop a technical solution that automates the process of back-analyzing ground properties and updating ground models in real time. Several technical challenges will be addressed. Current backanalyses practice is extraordinarily labor-intensive and expensive in managing and integrating data from underground infrastructure projects. The dynamic environment during construction requires 4D inversion analysis on potentially hundreds of unique tunnel-structure interactions. Furthermore, the efficacy of the inversion in estimating geotechnical parameters, which has been demonstrated for only limited situations during the fundamental development of the techniques, needs to be validated. The goals of the proposed research are to (1) develop algorithms to automatically integrate data from geotechnical instrumentation and monitoring, construction process monitoring, existing infrastructure, apriori geostatistical model, etc., (2) develop algorithms to dynamically update the geotechnical parameter inversion in spatial proximity of tunnel construction to adjacent structures, sensors, and ground conditions, (3) characterize the geotechnical parameter inversion efficacy across a broad variety of ground conditions and tunneling-structure interactions, and (4) learn the influence of sensing layout and optimized sensing on inversion efficacy. This Phase I work will lay the foundation for the development of a ‘live’ ground modelling tool.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.