The use of Bayesian statistics to predict patterns of spatial repeatability
Citation:
Simon P. Wilson, Niall K. Harris, Eugene J. OBrien, The use of Bayesian statistics to predict patterns of spatial repeatability, Journal of Sound and Vibration, 14, 5, 2006, 303-315Download Item:
Abstract:
Statistical spatial repeatability (SSR) is an extension to the well known concept of
spatial repeatability. SSR states that the mean of many patterns of dynamic tyre force
applied to a pavement surface is similar for a fleet of trucks of a given type. A model
which can accurately predict patterns of SSR could subsequently be used in whole-life
pavement deterioration models as a means of describing pavement loading due to a
fleet of vehicles. This paper presents a method for predicting patterns of SSR, through
the use of a truck fleet model inferred from measurements of dynamic tyre forces. A
Bayesian statistical inference algorithm is used to determine the distributions of
multiple parameters of a fleet of quarter-car heavy vehicle ride models, based on prior
assumed distributions and the set of observed dynamic tyre force from a `true? fleet of
100 simulated models. Simulated forces are noted at 16 equidistant pavement
locations, similar to data from a multiple sensor weigh-in-motion site. It is shown that
the fitted model provides excellent agreement in the mean pattern of dynamic force
with the originally generated truck fleet. It is shown that good predictions are possible
for patterns of SSR on a given section of road for a fleet of similar vehicles.
Sponsor
Grant Number
Irish Research Council for Science Engineering and Technology
Author's Homepage:
http://people.tcd.ie/swilsonDescription:
PUBLISHED
Author: WILSON, SIMON PAUL
Publisher:
ElsevierCollections
Series/Report no:
Journal of Sound and Vibration;14;
5;
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