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Compressive strength of tubular members with localized pitting damage considering variation of corrosion features
Marine Structures ( IF 4.0 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.marstruc.2020.102805
Renhua Wang , Haichao Guo , R.Ajit Shenoi

Abstract Localized pitting corrosion often occurs on marine and offshore structures in the form of patch corrosion with great uncertainties in the location, size and shape. The variation of corrosion features affects ultimate strength of tubular members significantly, but it is still not well understood. This paper presents a numerical study on tubular members of diverse slenderness ratios to clarify the localized pitting effect on ultimate strength. Numerical analyses were performed based on novel models of pitted members that were calibrated against benchmark column tests. Corrosion pits were randomly introduced on the local outside surfaces of members via stochastic simulation, forming corrosion patches varied in the location, size and shape. Numerical results obtained were regressed to propose a unified empirical formula to predict ultimate strength. It turned out that the shape of the corrosion patch has a significant influence on the ultimate strength. The shape change of the patch can alter failure modes of medium length columns. The reduction of ultimate strength is closely related to the shape ratio of the patch besides the volume loss of corroded material. The unified empirical formula incorporating the shape ratio and the volume loss shows a good ability to predict the experimental results.

中文翻译:

考虑腐蚀特征变化的具有局部点蚀损伤的管状构件的抗压强度

摘要 局部点蚀在海洋和近海结构上常以斑块腐蚀的形式发生,其位置、大小和形状具有很大的不确定性。腐蚀特征的变化显着影响管状构件的极限强度,但仍不清楚。本文对不同长细比的管状构件进行了数值研究,以阐明局部点蚀对极限强度的影响。数值分析是基于新的凹坑构件模型进行的,这些模型根据基准柱测试进行了校准。通过随机模拟在构件局部外表面随机引入腐蚀坑,形成位置、大小和形状各异的腐蚀斑块。对所得数值结果进行回归,以提出统一的经验公式来预测极限强度。结果表明,腐蚀斑块的形状对极限强度有显着影响。补丁的形状变化可以改变中等长度柱的失效模式。极限强度的降低除腐蚀材料的体积损失外,还与补片的形状比密切相关。结合形状比和体积损失的统一经验公式显示出良好的预测实验结果的能力。极限强度的降低除受腐蚀材料的体积损失外,还与补片的形状比密切相关。结合形状比和体积损失的统一经验公式显示出良好的预测实验结果的能力。极限强度的降低除腐蚀材料的体积损失外,还与补片的形状比密切相关。结合形状比和体积损失的统一经验公式显示出良好的预测实验结果的能力。
更新日期:2020-09-01
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