Current assessments of the water-ice in the lunar PSRs come from the orbital measurements from instruments aboard Lunar Reconnaissance Orbiter and Chandrayaan spacecrafts. The variance in distribution and form of water-ice from orbital assessments emphasizes the need to obtain ground truth data from the Lunar PSRs by drilling. The goal of this early stage innovation project was to develop algorithms to analyze the patterns in the drilling data to estimate the geotechnical properties and characteristics of subsurface water-ice in the lunar regolith. These algorithms use drilling data to estimate the uniaxial compressive strength and subsurface stratigraphy. These algorithms can also be used to estimate the quantity and distribution of water-ice on the lunar subsurface. The papers presented on this topic earlier assume a low-porosity, compacted form of water-ice. This paper discusses the steps taken to update the algorithm to a high-porosity system. Several papers published earlier by the used the drilling data from the low-porosity analog and cryogenic tests to develop pattern recognition algorithm to estimate uniaxial compressive strength (UCS), water-content, stratigraphy, and drilling dysfunctions like auger choking. This paper deploys the algorithms developed for the low-porosity form of water-ice to high-porosity analogs. Five analog cellular concrete blocks with varying porosity and strengths were cast and drilling data was recorded from each of the blocks. The algorithms developed earlier were directly deployed on this data first to evaluate the accuracy of the algorithm without any modifications. The drilling data was then used to update the algorithms and improve their accuracy. The updated algorithms were able to differentiate between high- and low-porosity systems and predict the UCS and stratigraphy. Such algorithms will be essential in planning the manned and robotic Lunar missions in the coming years. They can be used to optimize drilling operations, estimate UCS, water-content, and stratigraphy. 
 
DOI: https://doi.org/10.1061/9780784483374.041

Using a Pattern Recognition Algorithm to Characterize Cryogenic Lunar Regolith

Due to challenges with COVID-19, this 17th Biennial has been moved to April 2021 and will now be offered in a virtual format. This conference aims to bring the experience and knowledge of experts in the aerospace industry together to share and discuss the latest research and engineering techniques that effect the exploration and settlement of space. Presentations will be made in multiple concurrent symposia over three days. Topics will include: Granular materials in space exploration, Exploration in utilization of extra-terrestrial bodies, advanced materials and designs for aerospace and terrestrial structures under extreme environment, Structures in challenging environments, and Space engineering, construction, and architecture for Moon, Mars, and beyond.

Paper Proceedings: https://ascelibrary.org/doi/book/10.1061/earthandspace2021