SitS NSF-UKRI: Rapid Deployment of Multi-Functional Modular Sensing Systems in the Soil

SitS NSF-UKRI：在土壤中快速部署多功能模块化传感系统

• 批准号: 1935548
• 负责人: David Frost
• 金额: $ 80万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935548&HistoricalAwards=false
• 关键词: SitS; NSF; UKRI; Rapid; Deployment

Soil is a global resource that supports most of our urban infrastructure, acts as a conduit for groundwater and is the dominant material in many of the world's geohazards. Understanding the in-situ state of soil (stress level, stiffness, strength, permeability) is essential to inform effective and efficient decisions about how humans should interact with soil deposits. Most current geotechnical measuring instruments involve vertical penetration of a probe into the soil to a shallow depth (up to a few hundred meters). Usually, the probe records only one type of data at a time (e.g. a displacement, a moisture content or a thermal gradient), in a very localized area. Consequently, the ground models used in decision-making rely on interpolation between relatively sporadic data points and consider relevant parameters (mechanical, hydraulic, thermal) separately. The Burrowing Robot with Integrated Sensing System (BRISS) builds on insight gained in designing Cone Penetration Test modifications and the more recent development of small prototype burrowing robots at the Georgia Institute of Technology (GT). The research objectives are to: (i) design, build and deploy a burrowing robotized sensor delivery system; (ii) sense mechanical and physical signals during the burrowing process and use machine-learning to adapt the burrowing process and the sensing strategy; (iii) interpret soil signals using particulate mechanics, tribology, large deformation continuum mechanics models and feature selection algorithms. The research group at GT, in collaboration with the research group at Imperial College London (ICL) in the UK, will collaborate to achieve the research objectives and to co-advise a cohort of graduate students and post-doctoral researchers. The BRISS will achieve a paradigm shift in soil exploration and site characterization. Both the sensor modules and the propulsion sections of the BRISS will be stackable, so that probes can be built up with different combinations of modules or the same modules but in different configurations. The BRISS will be minimally wired, thus the project findings will pave the way towards wireless, remotely controlled, multi-directional subsurface sensing. Such technologies will ultimately enable deep sediment characterization and extra-terrestrial exploration. The long-term deployment of multi-sensing probes could be used to detect variations of soil properties that are independent from localized probe stimuli, such as pH change consequent to mining activities or pore pressure change consequent to repeated droughts. The PIs will use GT and ICL institutional organizations to recruit students from under-represented minorities. They will engage with the ALERT Geomaterials network and GT Society of Women Engineers to attract female students. The lead-PI will participate in outreach activities for promoting the inclusion of the LGBTQ community in engineering and will facilitate Safe Space training for all the project team members.Challenges associated with obtaining undisturbed samples mean that probes that can measure these properties in-situ are incredibly useful. Informed by recent prototyping work at GT, the team will develop a novel multi-sensor system, BRISS, which will incorporate several major advances: (a) the use of soft robot and micro-controls to enable probes to navigate in any orientation in the subsurface; (b) the ability of these probes to self-propel through the soil using peristaltic motion; (c) the incorporation of multiple micro-sensors in these semi-autonomous probes; and (d) the leveraging of machine learning algorithms into the data analysis and soil model development. Recently developed experimental techniques will be used to refine and optimize the propulsion mechanism; these include novel textures, bio-inspired anchors, soil ablation mechanisms and self-lubrication processes. Innovative sensor systems will be designed and evaluated to optimize the set of measurements, with a particular focus on stress, stress wave velocity and acoustic emissions. Novel deep reinforcement machine learning algorithms will be used to refine the burrowing trajectory and adapt the frequency at which in situ measures are taken. Feature selection algorithms will be enhanced to handle large data sets for interpreting stress, pore pressure, geophysical and acoustic signals. This project will also provide fundamental understanding of the physical and mechanical response of dry and water-saturated sand during the penetration of the self-propelled BRISS. For the first time, multi-scale numerical models will decipher burrowing mechanics, by combining discrete element models that will include peristaltic boundary conditions, particle interaction and multi-scale tribological models that will shed light on the robot/soil interface rheology, and large-deformation finite element hydro-mechanical elasto-plastic models that will be applicable to predict soil behavior at larger scales.This project was awarded through the "Signals in the Soil (SitS)" opportunity, a collaborative solicitation that involves the United States Department of Agriculture National Institute of Food and Agriculture (USDA NIFA) and the following United Kingdom Research and Innovation (UKRI) research councils: 1) The Natural Environment Research Council (NERC), 2) the Biotechnology and Biological Sciences Research Council (BBSRC), 3) the Engineering and Physical Sciences Research Council (EPSRC), and the Science and Technology Facilities Council (STFC).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.

土壤是一种支持我们大多数城市基础设施的全球资源，是地下水的渠道，并且是世界许多地质灾害中的主要材料。 了解土壤的原位状态（应力水平，刚度，强度，渗透性）对于告知人类应如何与土壤沉积相互作用的有效决策至关重要。 当前的大多数岩土测量仪器涉及将探针垂直渗透到土壤中的浅深度（最高几百米）。 通常，探针一次仅记录一种类型的数据（例如位移，水分含量或热梯度）。 因此，在决策中使用的地面模型依赖于相对零星的数据点之间的插值，并分别考虑相关参数（机械，液压，热力）。 具有集成感应系统（BRISS）的挖洞机器人建立在设计锥体渗透测试修饰和乔治亚技术学院（GT）的小型原型挖洞机器人的最新开发中。研究目标是：（i）设计，构建和部署挖洞的机器人传感器输送系统； （ii）在挖洞过程中有理由机械和物理信号，并使用机器学习来适应挖洞过程和感应策略； （iii）使用颗粒力学，摩擦学，大变形连续性力学模型和特征选择算法来解释土壤信号。 GT的研究小组与英国帝国伦敦帝国学院（ICL）的研究小组合作，将合作以实现研究目标，并共同审议研究生和博士后研究人员的群体。 布里斯将在土壤探索和现场表征上实现范式转移。 传感器模块和Briss的推进部分都是可堆叠的，因此可以使用模块或相同模块的不同组合来构建探针，但使用不同的配置。 Briss将是最小的接线，因此项目发现将为无线，遥控，多向地下传感铺平道路。 这样的技术最终将使深层沉积物表征和外物探索。 多种感应探针的长期部署可用于检测独立于局部探针刺激的土壤特性的变化，例如导致采矿活动导致的pH变化或导致反复干旱造成的孔隙压力变化。 PI将使用GT和ICL机构组织来招募来自代表性不足的少数民族的学生。他们将与警报地材料网络和GT女士工程师协会互动，以吸引女学生。 Lead-PI将参加推广LGBTQ社区在工程中的推广活动，并将促进所有项目团队成员的安全空间培训。与获取不受干扰的样品相关的核能意味着可以衡量这些属性的探针非常有用。 在GT的最新原型工作中，该团队将开发一个新型的多传感器系统Briss，该系统将结合几个主要进步：（a）使用软机器人和微控制能力来启用探测器在地下中的任何方向导航； （b）这些探针使用蠕动运动通过土壤自行旋转的能力； （c）在这些半自主探针中掺入多个微传感器； （d）将机器学习算法利用到数据分析和土壤模型开发中。 最近开发的实验技术将用于完善和优化推进机制。这些包括新颖的纹理，生物启发的锚，土壤消融机制和自润滑过程。 创新的传感器系统将经过设计和评估，以优化一组测量值，特别关注应力，应力波速度和声学排放。 新型的深钢筋学习算法将用于完善挖洞轨迹并适应采取原位措施的频率。 特征选择算法将得到增强，以处理用于解释应力，孔隙压力，地球物理和声学信号的大数据集。 该项目还将提供对自前卫briss渗透过程中干和水饱和沙的物理和机械反应的基本理解。 多尺度数值模型首次通过结合离散的元素模型来解密挖掘力学，该模型将包括蠕动边界条件，粒子的相互作用和多尺度摩擦学模型，这些模型将揭示机器人/土壤界面流变学，以及大型元素的大型机器化模型，以预测较大的启发型号，以预测较大的质量。土壤（SITS）“机会，是一种合作招标，涉及美国农业部国家食品和农业研究所（USDA NIFA）以及以下英国研究与创新委员会（UKRI）研究委员会：1）自然环境研究委员会（NERC），2），生物学科学研究委员会（NERC），生物科学研究委员会（BBBSRC），3） （EPSRC）和科学技术设施委员会（STFC）。该奖项反映了NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准，被视为值得通过评估来获得支持。

项目成果

Machine learning algorithms applied to the blowout susceptibility estimation around pressurized cavities in drained soil

机器学习算法应用于排水土壤加压空腔周围井喷敏感性估计

• 发表时间: 2021
• 作者: Patino-Ramirez, L. Fernando;Arson, Chloe
• 通讯作者: Arson, Chloe

Acoustic Emission Enabled Particle Size Estimation via Low Stress-Varied Axial Interface Shearing

通过低应力变化轴向界面剪切进行声发射颗粒尺寸估算

• DOI: 10.1109/tim.2022.3156175
• 发表时间: 2022
• 期刊: IEEE Transactions on Instrumentation and Measurement
• 影响因子: 5.6
• 作者: Yu, Min;Reddyhoff, Tom;Dini, Daniele;Holmes, Andrew;O'Sullivan, Catherine
• 通讯作者: O'Sullivan, Catherine

Using Ultrasonic Reflection Resonance to Probe Stress Wave Velocity in Assemblies of Spherical Particles

利用超声波反射共振探测球形颗粒集合体中的应力波速度

• DOI: 10.1109/jsen.2021.3106806
• 发表时间: 2021
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Yu, Min;Reddyhoff, Tom;Dini, Daniele;Holmes, Andrew;O'Sullivan, Catherine
• 通讯作者: O'Sullivan, Catherine

Controlling subterranean forces enables a fast, steerable, burrowing soft robot

• DOI: 10.1126/scirobotics.abe2922
• 发表时间: 2021-06-16
• 期刊: SCIENCE ROBOTICS
• 影响因子: 25
• 作者: Naclerio, Nicholas D.;Karsai, Andras;Hawkes, Elliot W.
• 通讯作者: Hawkes, Elliot W.