Road damage due to dynamic tyre forces measured on a public road

This paper describes a series of tests conducted on a UK trunk road, in which the dynamic tyre forces generated by over 1500 heavy goods vehicles (HGVs) were measured using a load measuring mat containing 144 capacitive strip sensors. The data was used to investigate the relative road damaging potential of the various classes of vehicles, and the degree of spatial repeatability of tyre forces present in a typical highway fleet. Approximately half the vehicles tested were found to contribute to a spatially repeatable pattern of pavement loading. On average, air suspended vehicles were found to generate lower dynamic load coefficients than steel suspended vehicles. However, air suspended vehicles also generated higher mean levels of theoretical road damage (aggregate force) than steel suspended vehicles, indicating that the ranking of suspensions depends on the pavement damage criterion used.

The modified Kanerva model for automatic speech recognition

A parallel processing network derived from Kanerva's associative memory theory Kanerva 1984 is shown to be able to train rapidly on connected speech data and recognize further speech data with a label error rate of 0·68%. This modified Kanerva model can be trained substantially faster than other networks with comparable pattern discrimination properties. Kanerva presented his theory of a self-propagating search in 1984, and showed theoretically that large-scale versions of his model would have powerful pattern matching properties. This paper describes how the design for the modified Kanerva model is derived from Kanerva's original theory. Several designs are tested to discover which form may be implemented fastest while still maintaining versatile recognition performance. A method is developed to deal with the time varying nature of the speech signal by recognizing static patterns together with a fixed quantity of contextual information. In order to recognize speech features in different contexts it is necessary for a network to be able to model disjoint pattern classes. This type of modelling cannot be performed by a single layer of links. Network research was once held back by the inability of single-layer networks to solve this sort of problem, and the lack of a training algorithm for multi-layer networks. Rumelhart, Hinton & Williams 1985 provided one solution by demonstrating the "back propagation" training algorithm for multi-layer networks. A second alternative is used in the modified Kanerva model. A non-linear fixed transformation maps the pattern space into a space of higher dimensionality in which the speech features are linearly separable. A single-layer network may then be used to perform the recognition. The advantage of this solution over the other using multi-layer networks lies in the greater power and speed of the single-layer network training algorithm. © 1989.

Numerical analysis of shallow foundations under combined horizontal and torsional loading

Foundations of subsea infrastructure in deep water subjected to asymmetric environmental loads have underscored the importance of combined torsional and horizontal loading effects on the bearing capacity of rectangular shallow foundations. The purpose of this study is to investigate the undrained sliding and torsional bearing capacity of rectangular and square shallow foundations together with the interaction response under combined loading using three-dimensional finite element (3D-FE) analysis. Upper bound plastic limit analysis is employed to establish a reference value for horizontal and torsional bearing capacity, and an interaction relationship for the combined loading condition. Satisfactory agreement of plastic limit analysis (PLA) and 3D-FE results for ultimate capacity and interaction curves ensures that simple PLA solution could be used to evaluate the bearing capacity problem of foundation under combined sliding and torsion.