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Probabilistic study of lifetime load effect distribution of bridges
Date Issued
2012-07-02
Date Available
2013-02-12T09:41:46Z
Abstract
Assessment of highway bridge safety requires a prediction of the probability of
occurrence of extreme load effects during the remaining life of the structure. While the
assessment of the strength of an existing bridge is relatively well understood, the traffic
loading it is subject to, has received less attention in the literature. The recorded traffic
data are often limited to a number of days or weeks due to the cost of data collection.
Studies in the literature have used many different methods to predict the lifetime
maximum bridge load effect using a small amount of data, including fitting block
maximum results to a Weibull distribution and raising maximum daily or maximum
weekly distributions to an appropriate power. Two examples are used in this study to show the importance of the quantity of data in predicting the lifetime maximum distribution. In the first, a simple example is used for which the exact theoretical probabilities are available. Hence, the errors in estimations can be assessed directly. In the second, ‘long-run’ simulations are used to generate a very large database of load effects from which very accurate estimates can be deduced of lifetime maximum effects. Results are presented for bidirectional traffic, with one lane in each direction, based on Weigh-in-Motion data from the Netherlands.
occurrence of extreme load effects during the remaining life of the structure. While the
assessment of the strength of an existing bridge is relatively well understood, the traffic
loading it is subject to, has received less attention in the literature. The recorded traffic
data are often limited to a number of days or weeks due to the cost of data collection.
Studies in the literature have used many different methods to predict the lifetime
maximum bridge load effect using a small amount of data, including fitting block
maximum results to a Weibull distribution and raising maximum daily or maximum
weekly distributions to an appropriate power. Two examples are used in this study to show the importance of the quantity of data in predicting the lifetime maximum distribution. In the first, a simple example is used for which the exact theoretical probabilities are available. Hence, the errors in estimations can be assessed directly. In the second, ‘long-run’ simulations are used to generate a very large database of load effects from which very accurate estimates can be deduced of lifetime maximum effects. Results are presented for bidirectional traffic, with one lane in each direction, based on Weigh-in-Motion data from the Netherlands.
Type of Material
Conference Publication
Subjects
Language
English
Status of Item
Not peer reviewed
Conference Details
6th International ASRANet Conference for Integrating Structural Analysis, Risk and Reliability, Croydon, London, United Kingdom, 2-4 July 2012
This item is made available under a Creative Commons License
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c129.pdf
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968.13 KB
Format
Adobe PDF
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6791a210a09529a69f496db10d2d60ad
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