An artificial neural network was used to assess deteriorating reinforced concrete (RC) structures using periodical inspection data for thermal power plants along the coast of Tokyo Bay arranged by the Tokyo Electric Power Company. In the analysis, the focus is on chloride-induced corrosion damage of RC structures. 13 input variables such as crack width, crack direction, number of cracks, etc. were selected as the inputs to the artificial neural network, and four output variables were chosen as the desired damage levels. Using a successfully trained neural network, a sensitivity analysis determines the influence of a change in each variable such as maximum crack width, area of peeling-off of concrete, exposure of reinforcement, etc., on the damage level.
展开▼
机译：东京电力公司（Tokyo Electric Power Company）利用东京湾沿岸的火力发电厂的定期检查数据，使用人工神经网络来评估劣化的钢筋混凝土（RC）结构。在分析中，重点是氯化物引起的钢筋混凝土结构的腐蚀破坏。选择了13个输入变量（如裂缝宽度，裂纹方向，裂纹数量等）作为人工神经网络的输入，并选择了4个输出变量作为所需的损伤水平。使用经过成功训练的神经网络，敏感性分析可以确定每个变量（例如最大裂缝宽度，混凝土剥离面积，钢筋暴露情况等）变化对损伤程度的影响。
展开▼
