RAPID: Adaptive Management of Geotechnical Construction in Urban Areas

RAPID：城市地区岩土工程施工的适应性管理

• 批准号: 1603060
• 负责人: Richard Finno
• 金额: $ 16.67万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-01 至 2017-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1603060&HistoricalAwards=false
• 关键词: RAPID; Adaptive; Management; Geotechnical; Construction

Increased dense urbanization and traffic congestion in the US and many parts of the world are prompting a significant demand for underground space to make emerging mega-cities livable by lowering pollution and energy consumption. Underground construction provides sustainable development benefits in terms of creating mass transit and commercial space in areas with existing infrastructure, with the ability to capture emissions, and the opportunity to preserve green space by relocating transportation systems and other structures underground. However, planning and construction of underground space is a lengthy process that requires large budgets (over $100 billion annually in the U.S.). Cost and schedule delays are common (e.g., Boston Big Dig from initial estimate of $2 billion to over $13 billion). Most of underground construction is public, taxpayer-funded projects. Efficiencies that can be developed in design and construction of underground space thus will have a large financial benefit to the US. One such development is an adaptive management technique that provides a means to incorporate recent advances in sensor development, information technology, and numerical analyses to predict, monitor, and control ground movements during excavation. The purpose of this research is to employ for the first time adaptive management techniques for geotechnical construction to provide real time updates of performance predictions, in this case during the 50 foot deep excavation for a multi-story building in Chicago. The project will develop tools that will advance the state-of-art and practice in the underground construction industry so that underground space can be created in urban areas in such a way that the process will have minimal impact on adjacent structures and utilities, thereby minimizing construction costs and eliminating expensive construction claims and lawsuits. While this project focuses on adaptive management of deep excavations, its results are directly applicable to any geotechnical construction activity. Industrial collaboration with Hayward Baker, Inc., the excavation support contractor for the project and a worldwide leader in geotechnical specialty construction, will ensure that results will have immediate impact in the underground construction industry. This research builds upon the results of several of NSF-supported projects in which optimization techniques were developed, automated monitoring technologies were employed, full-scale field performance was quantified in detail, sophisticated models of soil behavior were employed, and parameter identification techniques were quantified at deep excavation sites. The test bed for this research is the excavation for the Simpson Querrey Biomedical Research Center building. The work will be conducted by Northwestern University in collaboration with Hayward Baker, Inc., the excavation support subcontractor for the project. The Principal Investigator conducted extensive research during and after construction of the adjacent Lurie Research Center which included a 40 ft deep excavation. At no cost to the research, Hayward Baker will install and maintain an automated monitoring system which will include MEMS-based shape arrays to measure lateral movements, robotic total stations to measure support system and adjacent building movements. Northwestern researchers will develop 2D and 3D finite element models of the excavation, based on constitutive models that account for small strain nonlinearity behavior of soils, implement an interface between the website, develop an analyses platform to allow automatic updates of performance predictions based on the finite element model, and deploy students as field personnel to record the detailed construction activities which will assure that the proper conditions have been considered in the update of the field performance. The research addresses fundamental issues regarding stress-strain behavior of natural clays at very small strain levels, and the relationships between detailed soil and structural responses due to construction activities. The tools will include integrated analyses and information platforms to facilitate communication among engineers, contractors, owners and the public, and will permit updated predictions of performance in near real time.

美国和世界许多地区日益密集的城市化和交通拥堵正在促使对地下空间的巨大需求，以通过降低污染和能源消耗使新兴特大城市变得宜居。 地下建筑提供了可持续发展效益，在现有基础设施的地区创造公共交通和商业空间，能够捕获排放，并有机会通过将交通系统和其他结构重新安置在地下来保护绿色空间。 然而，地下空间的规划和建设是一个漫长的过程，需要大量的预算（美国每年超过 1000 亿美元）。 成本和进度延误很常见（例如，Boston Big Dig 从最初估计的 20 亿美元增加到超过 130 亿美元）。 大多数地下建筑都是由纳税人资助的公共项目。 因此，提高地下空间设计和施工的效率将为美国带来巨大的经济利益。 其中一项发展是自适应管理技术，它提供了一种方法，将传感器开发、信息技术和数值分析的最新进展结合起来，以预测、监测和控制挖掘过程中的地面运动。 本研究的目的是首次在岩土工程施工中采用自适应管理技术，以提供性能预测的实时更新，本例是在芝加哥一座多层建筑的 50 英尺深挖掘过程中进行的。 该项目将开发工具来推进地下建筑行业的最先进和实践，以便在城市地区创建地下空间，该过程对邻近结构和公用设施的影响最小，从而最大限度地降低建筑成本并消除昂贵的建筑索赔和诉讼。 虽然该项目侧重于深基坑开挖的适应性管理，但其结果可直接适用于任何岩土施工活动。 与该项目的开挖支持承包商、岩土专业施工领域的全球领导者 Hayward Baker, Inc. 的工业合作将确保成果对地下建筑行业产生直接影响。这项研究建立在美国国家科学基金会支持的几个项目的成果基础上，其中开发了优化技术，采用了自动监测技术，详细量化了全面的现场性能，采用了复杂的土壤行为模型，并对深基坑开挖现场的参数识别技术进行了量化。 这项研究的试验台是对辛普森奎里生物医学研究中心大楼的挖掘。 这项工作将由西北大学与该项目的挖掘支持分包商 Hayward Baker, Inc. 合作进行。 首席研究员在邻近的卢里研究中心建设期间和之后进行了广泛的研究，其中包括 40 英尺深的挖掘。 Hayward Baker 将免费安装和维护一个自动监测系统，其中包括用于测量横向运动的基于 MEMS 的形状阵列、用于测量支撑系统和邻近建筑物运动的机器人全站仪。 西北大学的研究人员将基于考虑土壤小应变非线性行为的本构模型开发开挖的 2D 和 3D 有限元模型，实现网站之间的接口，开发一个分析平台以允许基于有限元模型自动更新性能预测，并部署学生作为现场人员记录详细的施工活动，这将确保在更新现场性能时考虑到适当的条件。 该研究解决了有关天然粘土在极小应变水平下的应力应变行为的基本问题，以及详细的土壤与施工活动引起的结构响应之间的关系。 这些工具将包括综合分析和信息平台，以促进工程师、承包商、业主和公众之间的沟通，并将允许近乎实时地更新性能预测。