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Evaluation and modeling of ultimate bond strength of corroded reinforcement in reinforced concrete elements

机译:钢筋混凝土构件中锈蚀钢筋的极限粘结强度评估与建模

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Bond deterioration between the reinforcement and surrounding concrete is one of the crucial reasons for the structural degradation in the steel-concrete composite structures. The assessment of bond deterioration due to corrosion is of prime importance on this issue. This study aims to present the derivation of analytical formulation of ultimate bond strength su at the corroded reinforcement-concrete interface for the reinforced concrete (RC) elements subjected to various levels of corrosion. The modeling technique dealt in this work is gene-expression programming and artificial neural network. The data used for the development of the models are thoroughly selected from the available experimental studies reported in the technical literature. A total of 218 experimental data samples were arranged to obtain training and testing data sets. The critical predictive factors were compressive strength of concrete, concrete cover, steel type, diameter of the steel bar, bond length, and corrosion level. The performances of the proposed empirical models were also evaluated statistically. The results indicated that the soft-computing based models had a satisfactory performance to predict the ultimate bond strength of corroded steel bars in RC elements.
机译:钢筋混凝土与周围混凝土之间的粘结变质是钢混凝土复合结构中结构变质的关键原因之一。在此问题上,最重要的是评估因腐蚀引起的粘结变质。这项研究的目的是提出在遭受各种腐蚀程度的钢筋混凝土(RC)构件的锈蚀钢筋混凝土界面处的极限粘结强度su的解析公式的推导。这项工作涉及的建模技术是基因表达编程和人工神经网络。用于开发模型的数据是从技术文献中报告的可用实验研究中彻底选择的。总共布置了218个实验数据样本以获得训练和测试数据集。关键的预测因素是混凝土的抗压强度,混凝土表皮,钢筋类型,钢筋直径,粘结长度和腐蚀程度。提出的经验模型的性能也进行了统计评估。结果表明,基于软计算的模型具有令人满意的性能,可以预测钢筋混凝土构件中锈蚀钢筋的极限粘结强度。

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