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.

土壤是一种全球性资源，支撑着我们大部分的城市基础设施，是地下水的管道，也是世界上许多地质灾害的主要物质。 了解土壤的原位状态（应力水平，刚度，强度，渗透性）对于有效和高效地决定人类应如何与土壤沉积物相互作用至关重要。 大多数当前的岩土测量仪器涉及探针垂直穿透到土壤中的浅深度（高达几百米）。 通常情况下，探头在非常局部的区域内一次只记录一种类型的数据（例如位移、含水量或热梯度）。 因此，决策中使用的地面模型依赖于相对零散的数据点之间的插值，并分别考虑相关参数（机械、水力、热力）。 具有集成传感系统的挖掘机器人（BRISS）建立在设计圆锥贯入试验修改和格鲁吉亚理工学院（GT）小型原型挖掘机器人的最新发展中获得的洞察力的基础上。研究目标是：（i）设计，建造和部署一个挖掘机器人传感器传输系统;（ii）在挖掘过程中感测机械和物理信号，并使用机器学习来适应挖掘过程和感测策略;（iii）使用颗粒力学，摩擦学，大变形连续介质力学模型和特征选择算法来解释土壤信号。 GT的研究小组与英国伦敦帝国理工学院（ICL）的研究小组合作，将合作实现研究目标，并为一批研究生和博士后研究人员提供共同建议。 BRISS将实现土壤勘探和场地特性的范式转变。 BRISS的传感器模块和推进部分都是可堆叠的，因此探测器可以用不同的模块组合或相同的模块但不同的配置来构建。 BRISS将采用最低限度的有线连接，因此该项目的研究结果将为无线、遥控、多方向地下传感铺平道路。 这些技术最终将能够确定深层沉积物的特性和进行地外探索。 多传感探头的长期部署可用于检测土壤特性的变化，这些变化与局部探头刺激无关，例如采矿活动引起的pH值变化或反复干旱引起的孔隙压力变化。 PI将利用GT和ICL机构组织从代表性不足的少数民族中招收学生。他们将与ALERT Geomaterials网络和GT Society of Women Engineers合作，以吸引女学生。首席PI将参与推广活动，以促进LGBTQ社区在工程中的包容性，并将促进所有项目团队成员的安全空间培训。与获得未受干扰的样品相关的挑战意味着可以现场测量这些特性的探针非常有用。 根据GT最近的原型设计工作，该团队将开发一种新型的多传感器系统BRISS，该系统将包括几项主要进展：（a）使用软机器人和微控制器，使探头能够在地下的任何方向上导航;（B）这些探头能够利用蠕动运动在土壤中自推进;（c）在这些半自动探测器中纳入多个微型传感器;以及（d）利用机器学习算法进行数据分析和土壤模型开发。 最近开发的实验技术将用于改进和优化推进机制;其中包括新的纹理，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被设计和评估，以优化测量集，特别关注应力，应力波速度和声发射。 新的深度强化机器学习算法将用于改进挖掘轨迹，并调整采取现场措施的频率。 将加强特征选择算法，以处理用于解释应力、孔隙压力、地球物理和声学信号的大型数据集。 该项目还将提供基本的理解的物理和机械响应的干燥和水饱和的沙子在自推进BRISS的穿透。 这是第一次，多尺度数值模型将破译挖掘力学，通过结合离散元模型，将包括蠕动边界条件，颗粒相互作用和多尺度摩擦学模型，将揭示机器人/土壤界面流变学，和大变形有限元流体力学弹塑性模型，将适用于预测更大规模的土壤行为。该项目是通过"土壤中的信号（SitS）"机会，这是一项合作征集活动，涉及美国农业部国家粮食和农业研究所（USDA NIFA）和以下英国研究与创新（UKRI）研究委员会：1）自然环境研究理事会（NERC），2）生物技术和生物科学研究理事会（BBSRC），3）工程和物理科学研究理事会（EPSRC）和科学技术设施理事会（STFC）。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价值和更广泛的影响审查标准进行评估来支持。

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.