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Modeling Corrosion in Suspension Bridge Main Cables. Ⅰ: Annual Corrosion Rate

机译:悬索桥主电缆中的腐蚀建模。 Ⅰ:年腐蚀率

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Accurately determining the current state of the main cables of a suspension bridge is critical to assessing the safety of the bridge. These cables are composed of thousands of individual bridge wires, each of which deteriorates over time at a different rate. Current inspection methods provide an incomplete picture of the condition across the cable section, so cable strength estimation methods that rely on this inspection data involve considerable uncertainty. Furthermore, there is no method for estimating the continuing decline in cable strength following an inspection due to ongoing corrosion. This paper lays the groundwork for a time-dependent corrosion-rate model for bridge wires by using monitored environmental parameters from the cable interior. To establish this model, a methodology to estimate the annual corrosion rate as a function of environmental variables was proposed. First, experimental data on the corrosion rate of carbon steel from previous studies were analyzed using machine learning methods. Temperature, relative humidity, pH, and CI- concentration were determined to be the most relevant variables for predicting the corrosion rate. Next, cyclic corrosion tests were performed by subjecting bridge wires to various levels of these environmental variables, and the resulting data were used to augment the experimental data set from previous studies. Finally, a corrosion-rate model that predicts the annual corrosion rate of bridge wires was developed using the augmented data set. (C) 2018 American Society of Civil Engineers.
机译:准确确定悬索桥主电缆的当前状态对于评估桥梁的安全性至关重要。这些电缆由数千根单独的桥线组成,每根桥线都会随着时间的推移以不同的速率劣化。当前的检查方法不能提供完整的电缆截面状况图,因此依赖于该检查数据的电缆强度估算方法会带来很大的不确定性。此外,没有方法可以估计由于持续腐蚀导致的检查后电缆强度的持续下降。本文通过使用来自电缆内部的受监控环境参数,为桥梁导线的随时间变化的腐蚀速率模型奠定了基础。为了建立该模型,提出了一种估算年腐蚀速率随环境变量变化的方法。首先,使用机器学习方法分析了先前研究中有关碳钢腐蚀速率的实验数据。确定温度,相对湿度,pH和CI浓度是预测腐蚀速率最相关的变量。接下来,通过使桥线经受这些环境变量的不同水平进行循环腐蚀测试,并将所得数据用于扩充先前研究的实验数据集。最后,使用增强的数据集开发了预测桥梁线材年腐蚀速率的腐蚀速率模型。 (C)2018美国土木工程师学会。

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