一般大气环境下钢筋锈蚀深度的RBF神经网络预测模型研究

钢筋锈蚀深度预测是评估在役RC结构服役性能的基础。为建立一般大气环境RC构件中钢筋锈蚀深度预测模型,通过收集实测数据,分析影响钢筋锈蚀深度的主要参数及其影响规律,继而基于实测数据建立数值模型和RBF神经网络预测模型,并进行参数敏感性分析。研究结果表明:与数值模型相比,RBF神经网络对钢筋锈蚀深度预测效率与精度更高,能够有效映射各影响参数与钢筋锈蚀深度之间复杂的非线性关系。参数敏感性分析结果显示,钢筋混凝土表面锈胀裂缝宽度对钢筋锈蚀深度影响最大,钢筋直径、保护层厚度与钢筋直径之比和混凝土抗压强度等其他因素影响次之。所得模型可用于工程检测中钢筋锈蚀程度预测与RC构筑物剩余服役寿命评估。

RBF neural network prediction model for steel bar corrosion depth under general atmospheric environment

The prediction of steel bar corrosion depth is the basis for evaluating the service performance of RC structures. To establish a prediction model for steel bar corrosion depth in RC members under general atmospheric environment, the main parameters affecting the corrosion depth and the influence law were analyzed in this paper. Then, a numerical model and a RBF neural network prediction model were established based on the measured data, and the parameter sensitivity analysis was carried out. Results show that: compared with the numerical model, the RBF neural network has higher efficiency and accuracy in predicting the corrosion depth of steel bars; it can effectively map the complex nonlinear relationship between the influencing parameters and the corrosion depth of steel bars. The results of parameter sensitivity analysis show that the expansive crack width on RC surface has the greatest influence on the corrosion depth of steel bars, followed by other factors, i.e., the diameter of steel bar, the ratio of concrete cover thickness to steel bar diameter, and the compressive strength of concrete. The proposed model can be used to predict the corrosion degree of steel bars and evaluate the remaining service life of RC structures in engineering detection.