Date of Award

Spring 2021

Project Type

Thesis

Program or Major

Civil Engineering

Degree Name

Master of Science

First Advisor

Jean Benoit

Second Advisor

Majid Ghayoomi

Third Advisor

Neil Olson

Abstract

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.

Download

Share

COinS