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Conference object . 2018
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Genetic Algorithm to optimize the strategies for bridge repair works

Authors: Bodnar, Larysa; Kanin, Alexander; Stepanov, Sergii;

Genetic Algorithm to optimize the strategies for bridge repair works

Abstract

An approach to solve the problem of reducing the operating costs and increasing the residual life of highway bridges is showed in this article. The main goal of this study is to optimize a lifecycle model of highway bridge’s elements using a special type of genetic algorithm (GA). GA allows managing the residual life of highway bridges. The goal is achieved by performing the following tasks: developing a degradation model of bridge elements; developing a model for the definition of bridge performance measures costs at the network level; optimizing the lifecycle of the bridge’s elements using GA. A special high-speed GA was developed and designed for working with the Ukrainian Bridge Management System (AEBMS). The developed GA differs from the traditional GA. For example, the GA takes into account the peculiarities of the allowable sequence of various repair works depending on the degree of the bridge’s degradation and the steps which were taken during the previous years.

Keywords

highway bridges; lifecycle model; genetic algorithm; Bridge Management System

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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