Rock Slope Instability Characterization and Progressive Failure Monitoring from in situ Ambient Resonance Data

根据原位环境共振数据进行岩石边坡失稳表征和渐进破坏监测

• 批准号: 2150896
• 负责人: Jeffrey Moore
• 金额: $ 33.22万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2150896&HistoricalAwards=false
• 关键词: Rock; Slope; Instability; Characterization; Progressive

Landslides cause significant damage to infrastructure and loss of life worldwide, and thus improved approaches are needed to better mitigate landslide hazards and risk. Slope stability characterization and monitoring form the core of hazard analysis, yet many commonly used techniques are costly and invasive, or only provide information at a single point. The investigators will use ambient vibration measurements at rock slope instability sites to create new conceptual and practical approaches for landslide structural characterization and progressive failure monitoring. These approaches are based on concepts of structural dynamics, where the resonance properties of slope instabilities are computed and monitored over time, revealing unique information on landslide structural composition and providing new metrics to anticipate the time of failure. The investigators will also use numerical simulations to test the role of landslide failure type on the predicted resonance response, generating new theoretical understanding aiding interpretation of field data and providing guidance for future application of the technique at other sites.The resonance properties of an unstable rock slope vary with geometry, material properties, and boundary conditions, creating the foundation for non-invasive slope stability characterization and monitoring based on in situ ambient vibration measurements. The investigators will conduct a suite of experiments designed to explore how in situ ambient vibration data can be used to interpret the structural conditions of rock slope instabilities, including boundary conditions at depth, and monitor and interpret progressive failure over time. They hypothesize that in situ ambient vibration data interpreted together with 3D eigenfrequency models allows improved characterization of the material, dynamic and structural properties of rock slope instabilities, and that ambient resonance monitoring provide unique information aiding slope stability hazard assessment and time-of-failure prediction. The investigators will test these hypotheses using a combination of field experiments and numerical models. Field measurements from ambient vibration array deployments, interpreted together with numerical simulations, are used to develop a new approach for characterizing the structural properties and boundary conditions of rock slope instabilities, supporting improved hazard assessment. Meanwhile, ambient vibration monitoring data generated at two field sites are analyzed to develop refined analyses able to distinguish subtle frequency changes associated with progressive failure amidst environmentally-driven drifts. Finally, a series of numerical experiments are used to develop new conceptual understanding of how slope kinematics influence resonant frequency evolution during progressive failure, and interpret failure metrics in tandem with resonance monitoring to demonstrate how ambient vibration data may be used for time-of-failure forecasting.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.

山体滑坡在世界范围内对基础设施造成重大破坏和生命损失，因此需要改进方法，以更好地减轻山体滑坡灾害和风险。边坡稳定性表征和监测是灾害分析的核心，然而许多常用的技术成本高且具有侵入性，或者只能提供单点信息。研究人员将在岩石边坡不稳定地点使用环境振动测量，为滑坡结构表征和渐进破坏监测创造新的概念和实用方法。这些方法是基于结构动力学的概念，其中斜坡不稳定性的共振特性随时间的推移进行计算和监测，揭示滑坡结构组成的独特信息，并提供新的指标来预测故障的时间。研究人员还将使用数值模拟来测试滑坡破坏类型对预测共振响应的作用，产生新的理论理解，帮助解释现场数据，并为该技术在其他地点的未来应用提供指导。不稳定岩石边坡的共振特性随几何形状、材料特性和边界条件而变化，为基于现场环境振动测量的非侵入性边坡稳定性表征和监测奠定了基础。研究人员将进行一系列实验，旨在探索如何使用现场环境振动数据来解释岩石边坡不稳定性的结构条件，包括深度边界条件，并监测和解释随时间推移的渐进破坏。他们假设，现场环境振动数据与三维特征频率模型一起解释，可以改善岩石边坡不稳定性的材料，动态和结构特性的表征，环境共振监测提供独特的信息，帮助边坡稳定性危险评估和故障时间预测。研究人员将使用现场实验和数值模型相结合的方法来测试这些假设。现场测量环境振动阵列部署，解释与数值模拟，用于开发一种新的方法来表征岩石边坡不稳定性的结构特性和边界条件，支持改进的灾害评估。同时，环境振动监测数据产生的两个现场进行了分析，以开发精细的分析，能够区分与环境驱动的漂移中的渐进式故障相关的细微频率变化。最后，一系列的数值实验是用来发展新的概念理解如何斜坡运动学影响共振频率演变过程中逐步失败，并解释故障指标与共振监测串联，以演示环境振动数据如何用于该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

项目成果

Rotational Components of Normal Modes Measured at a Natural Sandstone Tower (Kane Springs Canyon, Utah, U.S.A.)

在天然砂岩塔（美国犹他州凯恩斯普林斯峡谷）测量的简正模态旋转分量

• DOI: 10.1785/0320220035
• 发表时间: 2022
• 期刊: The Seismic Record
• 作者: Dzubay, Alex;Moore, Jeffrey R.;Finnegan, Riley;Jensen, Erin K.;Geimer, Paul R.;Koper, Keith D.
• 通讯作者: Koper, Keith D.

Structural Characterization of a Toppling Rock Slab From Array‐Based Ambient Vibration Measurements and Numerical Modal Analysis

基于阵列的环境振动测量和数值模态分析对倾倒岩板的结构表征

• DOI: 10.1029/2022jf006679
• 发表时间: 2022
• 期刊: Journal of Geophysical Research: Earth Surface
• 作者: Bessette‐Kirton, E. K.;Moore, J. R.;Geimer, P. R.;Finnegan, R.;Häusler, M.;Dzubay, A.
• 通讯作者: Dzubay, A.