Analysis of axle and vehicle load properties through Bayesian networks based on weigh-in-motion data
article
Weigh-in-Motion (WIM) systems are used, among other applications, in pavement and bridge reliability.
The system measures quantities such as individual axle load, vehicular loads, vehicle speed, vehicle
length and number ofaxles. Because ofthe nature ofúamc configuration, the quantities measured are
evidently regarded as random variables. The dependence structure of the data of such complex systems
as the traffic systems is also very complex. It is desirable to be able to represent the complex
multidimensional-distribution with models where the dependence may be explained in a clear way
and different locations where the system operates may be treated simultaneously.
Bayesian Networks (BNs) are models that comply with the characteristics listed above. In this paper
we discuss BN models and results concerning their ability to adequately represent the data. The paper
places attention on the construction and use of the models. We discuss applications of the proposed BNs
in reliability analysis. In part¡cular we show how the proposed BNs may be used for computing design
values for individual axles, vehicle weight and maximum bending moments of bridges in certain time
intervals. These estimates have been used to advise authorities with respect to bridge reliability.
Directions as to how the model may be extended to include locations where the WIM system does not
operate are given whenever possible, These ideas benefit from structured expert judgment techniques
previously used to quantify Hybrid Bayesian Networks (HBNS) with success.
The system measures quantities such as individual axle load, vehicular loads, vehicle speed, vehicle
length and number ofaxles. Because ofthe nature ofúamc configuration, the quantities measured are
evidently regarded as random variables. The dependence structure of the data of such complex systems
as the traffic systems is also very complex. It is desirable to be able to represent the complex
multidimensional-distribution with models where the dependence may be explained in a clear way
and different locations where the system operates may be treated simultaneously.
Bayesian Networks (BNs) are models that comply with the characteristics listed above. In this paper
we discuss BN models and results concerning their ability to adequately represent the data. The paper
places attention on the construction and use of the models. We discuss applications of the proposed BNs
in reliability analysis. In part¡cular we show how the proposed BNs may be used for computing design
values for individual axles, vehicle weight and maximum bending moments of bridges in certain time
intervals. These estimates have been used to advise authorities with respect to bridge reliability.
Directions as to how the model may be extended to include locations where the WIM system does not
operate are given whenever possible, These ideas benefit from structured expert judgment techniques
previously used to quantify Hybrid Bayesian Networks (HBNS) with success.
Topics
TNO Identifier
489328
Source
Reliability Engineering and System Safety, 125, pp. 153-164.
Pages
153-164
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