论文部分内容阅读
钢筋混凝土粘结强度受多种因素 (如混凝土强度、混凝土保护层厚度、钢筋直径、钢筋类型、钢筋锈蚀率等 )的共同作用 ,建立计算模型比较困难。在试验研究的基础上 ,利用人工神经网络技术 ,分别在考虑单一因素和多种因素的情况下建立BP网络模型预测锈蚀钢筋与混凝土之间的极限粘结力 ,从而不需建立具体的数学模型就可以得到较满意的预测结果。为受腐蚀钢筋混凝土结构力学性能的研究提供一种新方法和新思路 ,为工程实际应用提供简便的预测方法
The bond strength of reinforced concrete is affected by many factors (such as the strength of concrete, the thickness of concrete cover, the diameter of rebar, the type of rebar, the corrosion rate of rebar, etc.). It is difficult to establish the computational model. Based on the experimental study, the artificial neural network technology is used to establish the BP network model considering the single factor and multiple factors separately to predict the ultimate bond between the corroded steel bar and the concrete, so there is no need to establish a specific mathematical model You can get more satisfactory prediction results. Provide a new method and new ideas for the study of the mechanical properties of corrosion reinforced concrete structures, and provide a simple prediction method for the practical application of the project