Development of an Embedded In-Mass Inertial Device for Landslide and Rockfall Monitoring
Source abstract
Abstract
Early-stage landslides and rockfalls are often characterized by very small internal accelerations associated with creep and progressive deformation, which are difficult to capture using conventional surface-based displacement monitoring techniques. To address this, the study presents the design and laboratory validation of a prototype in-mass inertial monitoring device, referred to as a Smart Rock, intended for embedded monitoring of rock mass motion. The developed device integrates low-noise inertial measurements with on-board processing to enable real-time characterization of motion signatures within a moving mass. Two sensing configurations, including a low-noise accelerometer-only configuration and a full inertial measurement unit (IMU) configuration, were implemented to evaluate their relative performance for in-mass motion monitoring. Embedded signal processing approaches suitable for landslide motions were developed to identify quasi-static, step-change, and impact-related motion regimes. Laboratory experiments using a controlled robotic testbed generated repeatable motion scenarios representative of creep-like movement, abrupt displacement changes, and impact events. Results showed that Smart Rock resolved very low-magnitude acceleration signatures on the order of 10−5 g and distinguished these from higher-energy motion and impact events, with improved signal stability observed for IMU-based configurations. These findings demonstrated the feasibility of in-mass inertial devices for characterizing landslide and rockfall motion in geotechnical applications. These results should be interpreted as proof-of-concept laboratory validation under controlled conditions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Landslides and related hazards · Robotic Locomotion and Control · Seismology and Earthquake Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mining and Metallurgical Engineering, University of Nevada, Reno, NV 89557, USA
Funding
Center for Disease Control and Prevention · National Institute for Occupational Health and Safety
References (47)
Show source-provided references
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