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Estimating RC corrosion distribution from surface cracks using mesoscale analysis integrated with machine learning
Cement and Concrete Composites ( IF 10.8 ) Pub Date : 2025-01-25 , DOI: 10.1016/j.cemconcomp.2025.105950
Tianyu Shao, Jie Luo, Kohei Nagai

Understanding the degree of reinforcing bar corrosion in reinforced concrete (RC) structures is crucial for evaluating their residual performance. This study proposes a simulation system for estimating the distribution of corrosion along the rebar of a RC beam member based on surface crack widths. The system integrates the rigid body spring model (RBSM) with machine learning methods. The inputs are surface crack widths and the desired output is the distribution of corrosion-induced expansion. A large dataset of training samples for machine learning is generated by running RBSM simulations using different expansion distributions. After training with this dataset, the neural network is able to correlate inputs and outputs, allowing it to estimate an expansion distribution from given cracking data. The estimated expansion distribution is then used to simulate the surface cracks using RBSM, and the error between the given (input) cracking data and simulated cracks is returned as an input to the trained network in order to optimize the results and enhance performance of the system. The applicability of this RBSM-neural network system is validated using both synthetic and experimental test data. The estimation results correlate well with the target data, demonstrating the effectiveness of the system in estimating internal expansive strain along the rebar and reproducing the cracking distribution using surface crack data. Internal distributions of cracking and stress are extracted from the simulations, providing additional information for analyzing structural performance.

中文翻译:


使用与机器学习集成的中尺度分析估计表面裂纹的 RC 腐蚀分布



了解钢筋混凝土 (RC) 结构中钢筋的腐蚀程度对于评估其残余性能至关重要。本研究提出了一种模拟系统,用于根据表面裂纹宽度估计沿 RC 梁构件钢筋的腐蚀分布。该系统将刚体弹簧模型 (RBSM) 与机器学习方法集成在一起。输入是表面裂纹宽度,所需的输出是腐蚀诱导膨胀的分布。通过使用不同的扩展分布运行 RBSM 模拟,生成用于机器学习的训练样本的大型数据集。使用此数据集进行训练后,神经网络能够将输入和输出相关联,从而能够根据给定的破解数据估计扩展分布。然后使用估计的膨胀分布来使用 RBSM 模拟表面裂纹,并将给定(输入)裂纹数据与模拟裂纹之间的误差作为输入返回给经过训练的网络,以优化结果并提高系统的性能。这种 RBSM 神经网络系统的适用性使用合成和实验测试数据进行了验证。估计结果与目标数据高度相关,证明了该系统在估计沿钢筋的内部膨胀应变和使用表面裂纹数据再现开裂分布方面的有效性。从仿真中提取开裂和应力的内部分布,为分析结构性能提供附加信息。
更新日期:2025-01-30
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