Pyroclastic density currents (PDCs) can erode soil and bedrock, yet we currently lack a mechanistic understanding of particle entrainment that can be incorporated into models and used to understand how PDC bulking affects runout. Here we quantify how particle splash, the ejection of particles due to impact by a projectile, entrains particles into dilute PDCs. We use scaled laboratory experiments to measure the mass of sand ejected by impacts of pumice, wood, and nylon spheres. We then derive an expression for particle splash that we validate with our experimental results as well as results from seven other studies. We find that the number of ejected particles scales with the kinetic energy of the impactor and the depth of the crater generated by the impactor. Last, we use a one‐dimensional model of a dilute, compressible density current—where runout distance is controlled by air entrainment and particle exchange with the substrate—to examine how particle entrainment by splash affects PDC density and runout. Splash‐driven particle entrainment can increase the runout distance of dilute PDCs by an order of magnitude. Furthermore, the temperature of entrained particles greatly affects runout and PDCs that entrain ambient temperature particles runout farther than those that entrain hot particles. Particle entrainment by splash therefore not only increases the runout of dilute PDCs but demonstrates that the temperature and composition of the lower boundary have consequences for PDC density, temperature, runout, hazards and depositional record.

We measure experimentally the penetration depth d of spherical particles into a water-saturated granular medium made of much smaller sand-sized grains. We vary the density, size R, and velocity U of the impacting spheres, and the size δ of the grains in the granular medium. We consider velocities between 7 and 107 m/s, a range not previously addressed, but relevant for impacts produced by volcanic eruptions. We find that d∝R(1/3)δ(1/3)U(2/3). The scaling with velocity is similar to that identified in previous, low-velocity collisions, but it also depends on the size of the grains in the granular medium. We develop a model, consistent with the observed scaling, in which the energy dissipation is dominated by the work required to rearrange grains along a network of force chains in the granular medium.

Vibration-based damage detection research aims to develop efficient algorithms to identify structural damage from monitoring data. One of the main categories of such algorithms is data-driven techniques which extract features from measured signals, and identify the damage by evaluating the significance of potential changes in these features. This paper presents application of several data-driven damage identification methodologies on a multivariate simulated data set. First, general regression models are applied to data collected through clusters of sensors and damage sensitive features are extracted. For systems with linear topology, it is shown that substructural regression modeling can also be performed on time-and frequency-domain transforms of the measured signals to estimate local stiffness of the structure as damage features. Subsequently, change detection techniques are utilized to statistically determine the significance of changes in the extracted features in order to distinguish between assignable changes as a result of damage and common changes due to environmental factors. Finally, a toolsuite is developed to facilitate application of the developed algorithms and improve the damage identification performance through incorporation of various combinations of regression models, damage features and statistical tests. IntroductionThe ultimate goal of vibration-based structural health monitoring (SHM) research is to develop efficient algorithms capable of detecting the time, location, and severity of any damage induced changes in structural components of monitored systems. Toward this goal, multitudes of methods have been proposed over recent decades that commonly establish a baseline for selected damage features and monitor their change from the baseline in the following unknown health conditions. Various features have been used for the purpose of damage identification; uncertain parameters of a finite element (FE) simulation of the structure that is calibrated with reference to modal quantities extracted using system identification (SID) algorithms [1,2], or features that are directly extracted from measured signals without using FE or SID procedures to train the data [3][4][5][6]. Each group of these damage identification methods has their own advantages and drawbacks. The first group is usually more laborious to implement and require certain a priori knowledge of structural properties, and location of damage; however, the calibrated model could be beneficial in design of repair scenarios or estimating the remaining life of the structure. The main advantage of the second group is their efficiency, and that they can be readily applied to measured signals without any prior information. Therefore, their application for a general automated damage detection platform is more promising. However, these data-driven methods are ineffective without statistical analyses to determine the change threshold for the extracted features. This paper presents application of multiple data-driven damage detection pr...