The use of Bayesian Statistics to predict patterns of spatial repeatability

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Title: The use of Bayesian Statistics to predict patterns of spatial repeatability
Authors: Wilson, Simon P.
Harris, Niall K.
O'Brien, Eugene J.
Permanent link: http://hdl.handle.net/10197/2335
Date: Oct-2006
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. The sensitivity of the model to errors in parameter estimation is discussed, as is the potential for implementation of the method.
Funding Details: Irish Research Council for Science, Engineering and Technology
Type of material: Journal Article
Publisher: Elsevier
Copyright (published version): 2006 Elsevier Ltd
Keywords: Weigh-in-motionWIMStatistical spatial repeatabilityBayesianPavementImpact factor
Subject LCSH: Spatial analysis (Statistics)
Bayesian statistical decision theory
Live loads--Statistical methods
DOI: 10.1016/j.trc.2006.07.002
Language: en
Status of Item: Peer reviewed
Appears in Collections:Critical Infrastructure Group Research Collection
Civil Engineering Research Collection

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