Bond between steel and concrete is one of the key aspects of structural design and its performance evaluation. In the past much research work has been focused on understanding bond deterioration owing to corrosion of reinforcement, however, there exists no nondestructive method to access the bond condition. In this regard, the presented experimental research work details the development of a nondestructive testing method to estimate the crack condition of concrete surrounding the steel reinforced by using ultrasonic pulse velocity test. In addition, a multilayer feedforward back propagation perceptron artificial neural network (ANN) is developed in order to avoid simplification assumptions for developing models to predict the cracking, owing to the nonlinear complex stress distribution at the steel-concrete interface. The ANN is used to predict the crack width and to conduct sensitivity analysis of the various factors influencing the bond deterioration. A high accuracy level is achieved between the predicted and the experimental values with R² of 0.97 and the most influential parameter is highlighted.

中文翻译：

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使用人工神经网络和无损检测检测预埋钢筋周围混凝土的裂缝

钢与混凝土之间的粘结是结构设计及其性能评价的关键方面之一。在过去，许多研究工作都集中在了解由于钢筋腐蚀引起的粘结退化，然而，没有非破坏性的方法来获得粘结条件。在这方面，所提出的实验研究工作详细介绍了一种无损检测方法的发展，该方法通过使用超声波脉冲速度测试来估计钢筋周围混凝土的裂缝状况。此外，由于钢 - 混凝土界面处的非线性复杂应力分布，开发了多层前馈反向传播感知器人工神经网络（ANN）以避免开发模型来预测开裂的简化假设。人工神经网络用于预测裂纹宽度并对影响粘结劣化的各种因素进行敏感性分析。在预测值和实验值之间实现了高精度水平0.97 的R ²和最有影响的参数被突出显示。