Assessing the road-damaging potential of heavy vehicles

The relative influence of various heavy vehicle design features on road-damaging potential is discussed. Testing procedures that could be used to measure the road-damaging potential of heavy vehicles are examined. A validated vehicle simulation is used to examine some of the characteristics of dynamic tyre forces generated by typical leaf sprung and air sprung articulated heavy vehicles for typical highway conditions. The proposed EC suspension test is simulated and the results compared with dynamic tyre forces generated under highway conditions. It is concluded that the road-damaging potential of a vehicle cannot be assessed by the simplistic parametric measurement of the proposed EC test. It is questionable whether a vehicle that passes the test will be any more 'road friendly' than one that fails.

Assessing the relative road damaging potential of HGVs

Assessing the road damaging potential of heavy vehicles is becoming an increasingly important issue. In this paper, current vehicle regulations and possible future alternatives are reviewed, and are categorized as tests on individual axles and whole vehicles, and 'direct' and 'indirect' tests. Whole vehicle methods of assessing road damaging potential accurately are then discussed. Direct methods are investigated (focussing on using a force measuring mat), and drawbacks are highlighted. Indirect methods using a transient input applied to individual axles are then examined. Results indicate that if non-linearities are accounted for properly, indirect methods of assessing whole vehicle road damaging potential could offer the required accuracy for a possible future test procedure.

Development of low entropy coding in a recurrent network.

In this paper we present an unsupervised neural network which exhibits competition between units via inhibitory feedback. The operation is such as to minimize reconstruction error, both for individual patterns, and over the entire training set. A key difference from networks which perform principal components analysis, or one of its variants, is the ability to converge to non-orthogonal weight values. We discuss the network's operation in relation to the twin goals of maximizing information transfer and minimizing code entropy, and show how the assignment of prior probabilities to network outputs can help to reduce entropy. We present results from two binary coding problems, and from experiments with image coding.