Predicting surface vibration from underground railways through inhomogeneous soil

Noise and vibration from underground railways is a major source of disturbance to inhabitants near subways. To help designers meet noise and vibration limits, numerical models are used to understand vibration propagation from these underground railways. However, the models commonly assume the ground is homogeneous and neglect to include local variability in the soil properties. Such simplifying assumptions add a level of uncertainty to the predictions which is not well understood. The goal of the current paper is to quantify the effect of soil inhomogeneity on surface vibration. The thin-layer method (TLM) is suggested as an efficient and accurate means of simulating vibration from underground railways in arbitrarily layered half-spaces. Stochastic variability of the soils elastic modulus is introduced using a KL expansion; the modulus is assumed to have a log-normal distribution and a modified exponential covariance kernel. The effect of horizontal soil variability is investigated by comparing the stochastic results for soils varied only in the vertical direction to soils with 2D variability. Results suggest that local soil inhomogeneity can significantly affect surface velocity predictions; 90 percent confidence intervals showing 8 dB averages and peak values up to 12 dB are computed. This is a significant source of uncertainty and should be considered when using predictions from models assuming homogeneous soil properties. Furthermore, the effect of horizontal variability of the elastic modulus on the confidence interval appears to be negligible. This suggests that only vertical variation needs to be taken into account when modelling ground vibration from underground railways. © 2012 Elsevier Ltd. All rights reserved.

Inhomogeneous soils and their effect on ground vibration due to underground railways

Ground vibration due to underground railways is a significant source of disturbance for people living or working near subways. Numerical models are commonly used to predict vibration levels; however, uncertainty inherent to these simulations must be understood to give confidence in the predictions. A semi-analytical approach is developed herein to investigate the effect of uncertainty in soil material properties on the surface vibration of layered halfspaces excited by an underground railway. The half-space is simulated using the thin-layer method coupled with the pipe-in-pipe (PiP) method for determining the load on the buried tunnel. The K-L expansion method is employed to smoothly vary the material properties throughout the soil by up to 10%. The simulation predicts a surface rms velocity variation of 5-10dB compared to a homogeneous, layered halfspace. These results suggest it may be prudent to include a 5dB error band on predicted vibration levels when simulating areas of varied material properties.

Vortex breaking and cutting in type II superconductors.

In technological superconductors, the Lorentz force on the flux vortices is opposed by inhomogeneous pinning and so the critical current may be controlled by a combination of vortex entanglement, cutting, and cross-joining. To understand the roles of these processes we report measurements of structures in which a weak pinning layer is sandwiched between two strongly pinning leads. Quantitative modeling of the results demonstrates that in such systems the critical current is limited by the deformation of individual vortices and not by subsequent cross-joining processes.

A microstructural investigation of deformation in fine SiC particulate- and whisker-reinforced aluminium-matrix composites

Aluminium-based composites, reinforced with low volume fractions of whiskers and small particles, have been formed by a powder route. The materials have been tested in tension, and the microstructures examined using transmission electron microscopy. The whisker composites showed an improvement in flow stress over the particulate composites, and this was linked to an initially enhanced work-hardening rate in the whisker composites. The overall dislocation densities were estimated to be somewhat higher in the whisker composites than the particulate composites, but in the early stages of deformation the distribution was rather different, with deformation in the whisker material being far more localized and inhomogeneous. This factor, together with differences in the internal stress distribution in the materials, is used to explain the difference in mechanical properties.

Tractable nonparametric bayesian inference in poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets. Copyright 2009.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finite-dimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets.

Characterization of YBCO Bulk samples by local AC-susceptometry

Melt processed HTSC bulk samples usually show a high inhomogeneity. These inhomogeneities influence application-relevant properties such as the lévitation force or the trapped field. In this contribution a technique is presented which allows investigation of these inhomogeneous properties. The measurements are performed by scanning the sample surface with a small coil system and detecting the first and third harmonic of the inductive response. The critical current density jc is calculated from the measured signal using a modified critical state model. Jcdistributions yielded by this technique are shown. © 1997 IEEE.

Single domain YBCO/Ag bulk superconductors fabricated by seeded infiltration and growth

We have applied the seeded infiltration and growth (IG) technique to the processing of samples containing Ag in an attempt to fabricate Ag-doped Y-Ba-Cu-O (YBCO) bulk superconductors with enhanced mechanical properties. The IG technique has been used successfully to grow bulk Ag-doped YBCO superconductors of up to 25 mm in diameter in the form of single grains. The distribution of Ag in the parent Y-123 matrix fabricated by the IG technique is observed to be at least as uniform as that in samples grown by conventional top seeded melt growth (TSMG). Fine Y-211 particles were observed to be embedded within the Y-123 matrix for the IG processed samples, leading to a high critical current density, Jc, of over 70 kA/cm2 at 77.3 K in self-field. The distribution of Y-211 in the IG sample microstructure, however, is inhomogeneous, which leads to a variation in the spatial distribution of Jc throughout the bulk matrix. A maximum-trapped field of around 0.43 T at 1.2 mm above the sample surface (i.e. including 0.7 mm for the sensor mould thickness) is observed at liquid nitrogen temperature, despite the relatively small grain size of the sample (20 mm diameter × 7 mm thickness). © 2008 IOP Publishing Ltd.