Pipe in Pipe PiP

PiP software is a powerful computational tool for calculating vibration from underground railways and for assessing the performance of vibration countermeasures. The software has a user-friendly interface and it uses the state-of-the-art techniques to perform quick calculations for the problem. The software employs a model of a slab track coupled to a circular tunnel embedded in the ground. The software calculates the Power Spectral Density (PSD) of the vertical displacement at any selected point in the soil. Excitation is assumed to be due to an infinitely-long train moving on a slab-track supported at the tunnel bed. The PSD is calculated for a roughness excitation of a unit value (i.e. "white noise"). The software also calculates the Insertion Gain (IG) which is the ratio between the PSD displacement after and before changing parameters of the track, tunnel or soil. Version 4 of the software accounts for important developments of the numerical model. The tunnel wall is modelled as a thick shell (using the elastic continuum theory) rather than a thin shell. More importantly, the numerical model accounts now for a tunnel embedded in a half space rather than a full space as done in the previous versions. The software can now be used to calculate vibration due to a number of typical PSD roughnesses for rails in good, average and bad conditions.

Discrete element calculations of the impact of a sand column against rigid structures

Discrete particle simulations of column of an aggregate of identical particles impacting a rigid, fixed target and a rigid, movable target are presented with the aim to understand the interaction of an aggregate of particles upon a structure. In most cases the column of particles is constrained against lateral expansion. The pressure exerted by the particles upon the fixed target (and the momentum transferred) is independent of the co-efficient of restitution and friction co-efficient between the particles but are strongly dependent upon the relative density of the particles in the column. There is a mild dependence on the contact stiffness between the particles which controls the elastic deformation of the densified aggregate of particles. In contrast, the momentum transfer to a movable target is strongly sensitive to the mass ratio of column to target. The impact event can be viewed as an inelastic collision between the sand column and the target with an effective co-efficient of restitution between 0 and 0.35 depending upon the relative density of the column. We present a foam analogy where impact of the aggregate of particles can be modelled by the impact of an equivalent foam projectile. The calculations on the equivalent projectile are significantly less intensive computationally and yet give predictions to within 5% of the full discrete particle calculations. They also suggest that "model" materials can be used to simulate the loading by an aggregate of particles within a laboratory setting. © 2012 Elsevier Ltd. All rights reserved.

Measurements and calculations of transport AC loss in second generation high temperature superconducting pancake coils

Theoretical and experimental AC loss data on a superconducting pancake coil wound using second generation (2 G) conductors are presented. An anisotropic critical state model is used to calculate critical current and the AC losses of a superconducting pancake coil. In the coil there are two regions, the critical state region and the subcritical region. The model assumes that in the subcritical region the flux lines are parallel to the tape wide face. AC losses of the superconducting pancake coil are calculated using this model. Both calorimetric and electrical techniques were used to measure AC losses in the coil. The calorimetric method is based on measuring the boil-off rate of liquid nitrogen. The electric method used a compensation circuit to eliminate the inductive component to measure the loss voltage of the coil. The experimental results are consistent with the theoretical calculations thus validating the anisotropic critical state model for loss estimations in the superconducting pancake coil. © 2011 American Institute of Physics.

Warped Mixtures for Nonparametric Cluster Shapes

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. We show that our model is effective for density estimation, performs better than infinite Gaussian mixture models at recovering the true number of clusters, and produces interpretable summaries of high-dimensional datasets.

Construction site image retrieval based on material cluster recognition

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.