Brief Communication: Measuring rock decelerations and rotation changes during short-duration ground impacts

Rockfall trajectories are primarily influenced by ground contacts, causing changes in acceleration and rock rotation. The duration of contacts and its influence on the rock kinematics are highly variable and generally unknown. The lack of knowledge hinders the development and calibration of physics-based rockfall trajectory models needed for hazard mitigation. To address this problem we placed three-axis gyroscopes and accelerometers in rocks of various sizes and shapes with the goal of quantifying rock deceleration in natural terrain. Short ground contacts range between 8 and 15&thinsp;ms, longer contacts between 50 and 70&thinsp;ms, totalling to only 6&thinsp;% of the runtime. Our results underscore the highly nonlinear character of rock–ground interactions.</p

Use of Smart Rocks to Improve Rock Slope Design

For many states, rockfall presents risks of irreversible damage to motorists on highways and roads across the country. Assessing these hazards is difficult as it relies on highly empirical methods based on assumed and/or measured slope and terrain surfaces and rock parameters, which can predict unrealistic trajectories due to unreliable modeling inputs. Research undertaken at the University of New Hampshire over the last decade includes the development of Smart Rock (SR) sensors used to evaluate these events from the perspective of the falling rock. The latest SRs consist of 3D printed capsules 50.8 mm in length and 25.4 mm in diameter, equipped with a ±400 g and a ±16 g 3-axis accelerometer, a ±4000 dps high-rate gyroscope, an altimeter, and a temperature sensor. Approximately 80 field experiments conducted in New Hampshire and Vermont provided SR data on rockfall at 10 different sites with a wide range of topographies and geological conditions. Preliminary laboratory and modeling assessments were also undertaken to compare experimental trajectories with rockfall simulations using different coefficients of restitution. It was concluded that acceleration and rotational velocity data from the rock perspective present a high potential to expand rockfall understanding and modeling. Such broader description of rockfall movements can enhance input parameters in computer rockfall modeling, which often disregards rotational data in kinetic energy estimates and tends to predict overly conservative trajectories

Development of an acoustic emission waveguide-based system for monitoring of rock slope deformation mechanisms

Hundreds of thousands of landslides occur every year around the world impacting on people's lives. Monitoring techniques able to foresee imminent collapse and provide a warning in time useful for action to be taken are essential for risk reduction and disaster prevention. Acoustic emission (AE) is generated in soil and rock materials by rearrangement of particles during displacement or increasing damage in the microstructure preceding a collapse; therefore AE is appropriate for estimation of slope deformation. To overcome the high attenuation that characterise geological materials and thus to be able to monitor AE activity, a system called Slope ALARMS that makes use of a waveguide to transmit AE waves from a deforming zone to a piezoelectric transducer was developed. The system quantifies acoustic activity as Ring Down Count (RDC) rates. In soil applications RDC rates have been correlated with the rate of deformation, however, the application to rock slopes poses new challenges over the significance of the measured AE trends, requiring new interpretation strategies. In order to develop new approaches to interpret acoustic emission rates measured within rock slopes, the system was installed at two trial sites in Italy and Austria. RDC rates from these sites, which have been measured over 6 and 2.5 years respectively, are analysed and clear and recurring trends were identified. The comparison of AE trends with response from a series of traditional instruments available at the sites allowed correlation with changes in external slope loading and internal stress changes. AE signatures from the limestone slope at the Italian site have been identified as generated in response to variations in the groundwater level and snow loading. At the conglomerate slope in Austria, AE signatures include the detachment of small boulders from the slope surface caused by the succession of freeze-thaw cycles during winter time. Consideration was also given to laboratory testing of specific system elements and field experiments. A framework towards strategies to interpret measured acoustic emission trends is provided for the use of the system within rock slopes

Debris-flow monitoring and warning: review and examples

Debris flows represent one of the most dangerous types of mass movements, because of their high velocities, large impact forces and long runout distances. This review describes the available debris-flow monitoring techniques and proposes recommendations to inform the design of future monitoring and warning/alarm systems. The selection and application of these techniques is highly dependent on site and hazard characterization, which is illustrated through detailed descriptions of nine monitoring sites: five in Europe, three in Asia and one in the USA. Most of these monitored catchments cover less than ~10 km2 and are topographically rugged with Melton Indices greater than 0.5. Hourly rainfall intensities between 5 and 15 mm/h are sufficient to trigger debris flows at many of the sites, and observed debris-flow volumes range from a few hundred up to almost one million cubic meters. The sensors found in these monitoring systems can be separated into two classes: a class measuring the initiation mechanisms, and another class measuring the flow dynamics. The first class principally includes rain gauges, but also contains of soil moisture and pore-water pressure sensors. The second class involves a large variety of sensors focusing on flow stage or ground vibrations and commonly includes video cameras to validate and aid in the data interpretation. Given the sporadic nature of debris flows, an essential characteristic of the monitoring systems is the differentiation between a continuous mode that samples at low frequency (“non-event mode”) and another mode that records the measurements at high frequency (“event mode”). The event detection algorithm, used to switch into the “event mode” depends on a threshold that is typically based on rainfall or ground vibration. Identifying the correct definition of these thresholds is a fundamental task not only for monitoring purposes, but also for the implementation of warning and alarm systemsPeer ReviewedPostprint (author's final draft

Optimizing IoT-Based Asset and Utilization Tracking: Efficient Activity Classification with MiniRocket on Resource-Constrained Devices

This paper introduces an effective solution for retrofitting construction power tools with low-power IoT to enable accurate activity classification. We address the challenge of distinguishing between when a power tool is being moved and when it is actually being used. To achieve classification accuracy and power consumption preservation a newly released algorithm called MiniRocket was employed. Known for its accuracy, scalability, and fast training for time-series classification, in this paper, it is proposed as a TinyML algorithm for inference on resource-constrained IoT devices. The paper demonstrates the portability and performance of MiniRocket on a resource-constrained, ultra-low power sensor node for floating-point and fixed-point arithmetic, matching up to 1% of the floating-point accuracy. The hyperparameters of the algorithm have been optimized for the task at hand to find a Pareto point that balances memory usage, accuracy and energy consumption. For the classification problem, we rely on an accelerometer as the sole sensor source, and BLE for data transmission. Extensive real-world construction data, using 16 different power tools, were collected, labeled, and used to validate the algorithm's performance directly embedded in the IoT device. Experimental results demonstrate that the proposed solution achieves an accuracy of 96.9% in distinguishing between real usage status and other motion statuses while consuming only 7kB of flash and 3kB of RAM. The final application exhibits an average current consumption of less than 15{\mu}W for the whole system, resulting in battery life performance ranging from 3 to 9 years depending on the battery capacity (250-500mAh) and the number of power tool usage hours (100-1500h)