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Rockfalls can often pose a significant risk to the public if protective designs do not properly account for block movement downslope and onto infrastructure facilities. Assessing these hazards is challenging, especially as current empirical and computational methods for predicting trajectories of falling blocks generally do not include the contribution associated with rotational behavior. Research undertaken at the University of New Hampshire, USA, over the last decade has led to the development of Smart Rock (SR) sensors inserted in natural rocks to evaluate these events from the perspective of the falling rock. The latest SRs consist of 3 D printed capsules 58.0 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, and an altimeter. Approximately 80 field experiments conducted in New Hampshire and Vermont provided SR data on rockfall at ten different sites. The Smart Rock data allowed more in-depth evaluations of accelerations, rotation rates, and modes of motion with precise time intervals, which cannot be easily captured in video recording systems or other instrumentation techniques. Such measurements are essential to improve our understanding and modeling of rockfall events for more economical and safer design of protective systems. [ABSTRACT FROM AUTHOR]

To overcome the limitations of pavement skid resistance prediction using the friction coefficient, a genetic-algorithm-improved neural network (GAI-NN) was developed in this study. First, three-dimensional (3D) point-cloud data of an asphalt pavement surface were obtained using a smart sensor (Gocator 3110). The friction coefficient of the pavement was then obtained using a pendulum friction tester. The 3D point-cloud dataset was then analyzed to recover the missing data and perform denoising. In particular, these data were filled using cubic spline interpolation. Parameters for texture characterization were defined, and methods for computing the parameters were developed. Finally, the GAI-NN model was developed by modifying the weights and thresholds. The test results indicated that using pavement surface texture 3D data, the GAI-NN was capable of predicting the pavement friction coefficient with sufficient accuracy, with an error of 12.1%. [ABSTRACT FROM AUTHOR]

Highly instrumented particles (i.e., "smart rocks") were included in monodisperse dry granular landslide experiments to quantify the collisional nature of such flows and to investigate the influence of collisional flow on the mobility of landslides. The total number of particles comprising a constant source volume of 0.4 m3 was varied by filling the volume with monodisperse particles of nominal diameters of 3, 6, 13 or 25 mm. Successively raising the total particle count resulted in flows that were increasingly thick relative to the respective particle size. Raw resultant acceleration data from the embedded smart rock sensors indicate that for each increase in grain size, there were increases in both the magnitude and frequency of particle collisions. Light detection and ranging (LiDAR)-generated point clouds of the landslide deposits indicated that increases in mobility and spreading, compared using differences in travel angle, were directly proportional to increases in collisional activity. By changing the size of the landslide particles from 3 to 25 mm, the travel angle at the gravity centre (αg) was observed to decrease from 27.8° to 25.3° (Δαg = −9.0%) and the Fahrböschung angle (α) was observed to decrease from 25.0° to 21.4° (Δα = −14.4%). [ABSTRACT FROM AUTHOR]