首页> 外文期刊>Journal of structural engineering >Lifetime Multiobjective Optimization of Cost and Spacing of Corrosion Rate Sensors Embedded in a Deteriorating Reinforced Concrete Bridge Deck
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Lifetime Multiobjective Optimization of Cost and Spacing of Corrosion Rate Sensors Embedded in a Deteriorating Reinforced Concrete Bridge Deck

机译:老化的钢筋混凝土桥面板中腐蚀速率传感器的寿命和成本的多目标优化

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摘要

Due to variations in concrete properties, environmental conditions, and other factors, the rate of corrosion of reinforcing steel can be highly variable within a given structural component. By placing multiple corrosion rate sensors throughout a reinforced concrete (RC) bridge deck, this variability can be monitored and corrected for use in a reliability model. There is a limit, however, on the number of sensors that can be feasibly placed in a structure, due to economic and constructability constraints. The constraints on the design variables of a permanent structural health monitoring (SHM) sensor network can be used to formulate a multiobjective optimization problem. This investigation describes the formulation of this problem for a RC bridge deck to be outfitted with corrosion rate sensors. The total cost of sensor system installation and the maximum lifetime dispersion of corrosion current density serve as the two objective functions to be minimized. The design variables are the spacing between adjacent sensors and the unit cost of each sensor. A set of optimal (Pareto) solutions are found for various assumptions using a multiobjective goal seeking algorithm in conjunction with Bayesian updating and interpolation techniques. The set of optimal combinations of sensor spacing and unit cost provide the best tradeoff between total SHM system cost and performance for a given set of constraints.
机译:由于混凝土性能,环境条件和其他因素的变化,在给定的结构部件内,钢筋的腐蚀速率可能会发生很大变化。通过在钢筋混凝土(RC)桥面板上放置多个腐蚀速率传感器,可以监视并校正此变化以用于可靠性模型。但是,由于经济和可施工性的限制,可合理放置在结构中的传感器数量受到限制。永久性结构健康监测(SHM)传感器网络的设计变量约束可用于制定多目标优化问题。这项研究描述了配备腐蚀速率传感器的RC桥面板的这一问题的解决方案。传感器系统安装的总成本和腐蚀电流密度的最大使用寿命分散是要最小化的两个目标功能。设计变量是相邻传感器之间的间距以及每个传感器的单位成本。使用多目标目标搜索算法结合贝叶斯更新和插值技术,针对各种假设找到了一​​组最佳(Pareto)解。对于给定的约束条件,传感器间距和单位成本的最佳组合集提供了SHM系统总成本和性能之间的最佳折衷。

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