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An approach to modelling concrete bridge condition deterioration using a statistical causal relationship based on inspection data
While physical models of concrete structures may give a fundamental understanding of structural deterioration mechanisms, their application is more or less limited in reflecting actual scenarios as a result of inevitable theoretical assumptions, whereby it is often difficult to cover complex structural behaviour and environment in the real world. Uncertainties and fuzziness, along with complexity, add more difficulty to the estimation of structural deterioration by physical models, which are typically described by quantitative mathematical equations. To complement physical models, an approach based on a statistical causal relationship, combining logics and statistics, is established for modelling structural condition deterioration. The relationship includes three kinds of basic variables in terms of causal factors, consequences and indicators, and it is represented by a set of conditional probabilities among those variables. Level two inspection data on concrete bridge slabs, as an example, is applied to establish the relationship. On the basis of the probabilistic causal relationship, prediction and diagnosis of structural conditions are implemented. To take advantage of this approach in dealing with qualitative issues such as human factors, the influence of inspectors' judgement on structural condition rating is addressed, while the effects of weather conditions on inspectors' judgement are also illustrated.
An approach to modelling concrete bridge condition deterioration using a statistical causal relationship based on inspection data
While physical models of concrete structures may give a fundamental understanding of structural deterioration mechanisms, their application is more or less limited in reflecting actual scenarios as a result of inevitable theoretical assumptions, whereby it is often difficult to cover complex structural behaviour and environment in the real world. Uncertainties and fuzziness, along with complexity, add more difficulty to the estimation of structural deterioration by physical models, which are typically described by quantitative mathematical equations. To complement physical models, an approach based on a statistical causal relationship, combining logics and statistics, is established for modelling structural condition deterioration. The relationship includes three kinds of basic variables in terms of causal factors, consequences and indicators, and it is represented by a set of conditional probabilities among those variables. Level two inspection data on concrete bridge slabs, as an example, is applied to establish the relationship. On the basis of the probabilistic causal relationship, prediction and diagnosis of structural conditions are implemented. To take advantage of this approach in dealing with qualitative issues such as human factors, the influence of inspectors' judgement on structural condition rating is addressed, while the effects of weather conditions on inspectors' judgement are also illustrated.
An approach to modelling concrete bridge condition deterioration using a statistical causal relationship based on inspection data
Wang, Xiaoming (author) / Nguyen, Minh (author) / Foliente, Greg (author) / Ye, Lin (author)
Structure and Infrastructure Engineering ; 3 ; 3-15
2007-03-01
13 pages
Article (Journal)
Electronic Resource
Unknown
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