(Received 18 October 2013; accepted 7 July 2014)
Published Online: 22 September 2014
| ||Format||Pages||Price|| |
|PDF (5.0M)||18||$25||  ADD TO CART|
Cite this document
This paper describes the development of a “smart rock,” an instrumented device for the study of debris flows, which is often triggered by earthquakes, heavy rain events, and rising groundwater conditions. Debris flows are very destructive forms of landslide consisting of a mixture of rocks, saturated soil, and debris typically flowing at high rates of speed and over long distances. In an effort to better understand the mechanics of debris flows, the smart rock was developed with a sensor package to be used in U.S. Geological Survey experiments at their flume facility in the Willamette National Forest, OR. The instrumented rock contains an inertial measurement unit (IMU) to measure acceleration and rotation rate about three body fixed axes, and two pressure sensors to measure pore water pressure. The sensors provide information about the movement of the rock and pore water pressures within a debris flow. One of the objectives of the sensor package is to use this information to track the position of a particle in the flow with an accuracy of 1 m over the course of 10 s. Calculation of position using the IMU requires the use of strapdown inertial navigation equations. Unfortunately, noise and bias in the rotation rate sensor introduce significant error in the position calculation. The results from one of the USGS debris flow experiments using the smart rock show that an ad hoc filtering method on the IMU data provides a rough estimate of the rock position in the flume, but far from the desired level of accuracy. Pressure and velocity recorded by the smart rock, while comparable to those measured by the USGS during the debris flow test, cannot be verified. Position accuracy can only be improved by using a better IMU and obtaining known rock positions versus time during the debris flow. Based on the results of this work, it is hoped that improved technology will result in a smart rock that can successfully provide useful and insightful debris flow data.
Harding, M. J.
Former Graduate Student, Univ. of New Hampshire, Mechanical Engineer, Bedford, NH
Fussell, B. K.
Professor, Department of Mechanical Engineering, Univ. of New Hampshire, Durham, NH
Gullison, M. A.
Graduate Student, Department of Civil Engineering, Univ. of New Hampshire, Durham, NH
Professor, Department of Civil Engineering, Univ. of New Hampshire, Durham, NH
de Alba, P. A.
Univ. of New Hampshire, Durham, NH
Stock #: GTJ20130172