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.

土壤是一种全球资源，支撑着我们大部分的城市基础设施，充当地下水的管道，并且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）对于人类如何与土壤沉积物相互作用做出有效且高效的决策至关重要。 目前大多数岩土测量仪器都需要将探头垂直插入土壤中，深度较浅（最多几百米）。 通常，探测器一次仅在非常局部的区域记录一种类型的数据（例如位移、水分含量或热梯度）。 因此，决策中使用的地面模型依赖于相对分散的数据点之间的插值，并分别考虑相关参数（机械、液压、热）。 具有集成传感系统的挖洞机器人 (BRISS) 建立在设计圆锥穿透测试修改和佐治亚理工学院 (GT) 小型原型挖洞机器人的最新开发中获得的见解之上。研究目标是：（i）设计、构建和部署穴居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，并使用机器学习来调整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 的研究小组将与英国伦敦帝国理工学院 (ICL) 的研究小组合作，共同实现研究目标，并共同为一批研究生和博士后研究人员提供建议。 BRISS 将实现土壤勘探和场地特征的范式转变。 BRISS 的传感器模块和推进部分都是可堆叠的，因此可以使用不同的模块组合或相同的模块但采用不同的配置来构建探测器。 BRISS 将尽量减少布线，因此该项目的研究结果将为无线、远程控制、多向地下传感铺平道路。 此类技术最终将实现深层沉积物表征和外星探索。 多传感探头的长期部署可用于检测独立于局部探头刺激的土壤特性变化，例如采矿活动引起的pH变化或反复干旱引起的孔隙压力变化。 PI 将利用 GT 和 ICL 机构组织招收代表性不足的少数族裔的学生。他们将与 ALERT Geomaterials 网络和 GT 女工程师协会合作，以吸引女学生。首席 PI 将参与促进 LGBTQ 群体融入工程的外展活动，并将促进所有项目团队成员的安全空间培训。与获取未受干扰的样本相关的挑战意味着能够在原位测量这些属性的探针非常有用。 根据 GT 最近的原型设计工作，该团队将开发一种新型多传感器系统 BRISS，该系统将包含几项重大进展：(a) 使用软机器人和微控制器，使探测器能够在地下的任何方向导航； (b) 这些探针利用蠕动运动自行穿过土壤的能力； (c) 在这些半自主探测器中加入多个微型传感器； (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完善和优化推进机制；其中包括新颖的纹理、仿生锚、土壤烧蚀机制和自润滑过程。 将设计和评估创新的传感器系统，以优化测量集，特别关注应力、应力波速度和声发射。 新型深度强化机器学习算法将用于细化挖掘轨迹并调整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型数据集。 该项目还将提供对自走式 BRISS 穿透过程中干燥和水饱和沙子的物理和机械响应的基本了解。 多尺度数值模型将首次通过结合离散元模型（包括蠕动边界条件、颗粒相互作用和多尺度摩擦学模型）和大变形有限元流体机械弹塑性模型来解释挖掘力学，其中离散元模型将揭示机器人/土壤界面流变学，而大变形有限元流体机械弹塑性模型将适用于预测更大尺度的土壤行为。该项目通过“Signals in 土壤（SitS）”机会，这是一项合作招标，涉及美国农业部国家食品和农业研究所（USDA NIFA）和以下英国研究与创新（UKRI）研究委员会：1）自然环境研究委员会（NERC），2）生物技术和生物科学研究委员会（BBSRC），3）工程和物理科学研究委员会 (EPSRC) 和科学技术设施委员会 (STFC)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.