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首页> 外文期刊>Corrosion Reviews >LABORATORY INVESTIGATION AND NEURAL NETWORKS MODELING OF DEICER INGRESS INTO PORTLAND CEMENT CONCRETE AND ITS CORROSION IMPLICATIONS
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LABORATORY INVESTIGATION AND NEURAL NETWORKS MODELING OF DEICER INGRESS INTO PORTLAND CEMENT CONCRETE AND ITS CORROSION IMPLICATIONS

机译:水泥硅酸盐水泥中除冰剂的实验室研究和神经网络建模及其腐蚀意义

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By exposing reinforced concrete samples to four common chloride-based deicers, the corrosive effect of chloride-based deicers on rebars and dowel bars was systematically investigated. The experiments were designed in such a way that the effect of deicers on reinforced concrete can be characterized in an accelerated manner, by either ponding the concrete samples with deicer solutions at room temperature, or incorporating pressurized ingress, wet-dry cycling and temperature cycling into the test regime. The chloride ingress over time was monitored using a custom-made chloride sensor embedded in each concrete sample. Also periodically measured were the open circuit potentials (OCPs) of the top bar in concrete. Once the chloride sensor detected the arrival of sufficient chlorides near the top bar and the OCP data indicated the possible initiation of top bar corrosion, the corrosion rates of rebars and dowel bars are characterized by macrocell current and corrosion current density derived from electrochemical impedance measurements. From a modeling perspective, artificial neural networks (ANNs) were used to achieve better understanding of the complex cause-and-effect relationships inherent in the deicer/concrete/bar systems and were successful in finding meaningful, logical results from the noisy data associated with the deicer ponding experiments. According to the ANN modeling, corrosion inhibitor (and possibly other additives in the inhibited CaCl_2 and MgCl_2 deicers) did slow down the ingress of chloride into concrete (indicated by more positive chloride sensor potentials), reduce the pitting risk of rebars and dowel bars in concrete (indicated by more positive OCP values) and reduce their corrosion rate in concrete (indicated by reduced macrocell current and corrosion current density of top bar). For both concrete mixes andboth types of deicer ponding, the overall risk of deicers to reinforced concrete was in the order of: non-inhibited NaCl > inhibited NaCl > inhibited CaCl_2 deicer > inhibited MgCl_2 deicer. While the corrosion inhibitors in deicer products, provide some benefits in delaying the corrosion initiation of rebars and dowel bars in concrete, such benefits seem to diminish once the active corrosion of the bars is initiated. In other words, the inhibitors showed little benefits in re-passivating the actively corroding bars in concrete or in stifling the corrosion propagation.
机译:通过将钢筋混凝土样品暴露于四种常见的基于氯的除冰剂,系统地研究了基于氯的除冰剂对钢筋和销钉的腐蚀作用。实验的设计方式是通过加速除冰剂对钢筋混凝土的影响来表征,方法是在室温下用除冰剂溶液浇筑混凝土样品,或者将加压进入,湿-干循环和温度循环并入。测试制度。使用嵌入在每个混凝土样品中的定制氯传感器监测氯随时间的变化。还定期测量混凝土顶部钢筋的开路电位(OCP)。一旦氯化物传感器检测到足够的氯化物到达顶部钢筋附近,并且OCP数据表明可能开始顶部钢筋腐蚀,则钢筋和销钉的腐蚀速率将通过电化学阻抗测量得出的宏单元电流和腐蚀电流密度来表征。从建模的角度来看,人工神经网络(ANN)用于更好地理解除冰器/混凝土/钢筋系统中固有的复杂因果关系,并成功地从与之相关的嘈杂数据中找到了有意义的逻辑结果除冰器的沉思实验。根据ANN模型,缓蚀剂(以及可能受抑制的CaCl_2和MgCl_2除冰剂中的其他添加剂)确实减缓了氯离子进入混凝土的速度(由更多的正氯离子传感器电位指示),降低了钢筋和木钉在混凝土中的点蚀风险。混凝土(由更正的OCP值表示)并降低其在混凝土中的腐蚀速率(由宏电池电流和顶杆腐蚀电流密度降低表示)。对于两种混凝土拌合料和两种类型的除冰剂浇筑,除冰剂对钢筋混凝土的总体风险依次为:非抑制性NaCl>抑制性NaCl>抑制性CaCl_2去除性> MgCl_2抑制性。除冰剂产品中的缓蚀剂在延迟混凝土中钢筋和销钉的腐蚀开始时提供了一些好处,但一旦开始对钢筋进行主动腐蚀,这些好处似乎就消失了。换句话说,缓蚀剂在重新钝化混凝土中积极腐蚀的钢筋或抑制腐蚀传播方面几乎没有益处。

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