Corrosion of the steel reinforcing bars in concrete structures is one of the major maintenance problems. Corrosion results in expansive pressure on the surrounding concrete, which causes internal damage that may become visible as surface cracking. Such damage may degrade structural safety and serviceability. Effective maintenance requires the evaluation of residual performance based on estimates of spatially nonuniform levels of corrosion, which are typically obtained through surface measurements only. In this study, the authors have developed a simulation system for estimating the levels of internal corrosion along the reinforcing bar length from surface crack information. This innovative system is produced by integrating the technique of Model Predictive Control (MPC) with Rigid-Body-Spring Models (RBSM) of corrosion-induced cracking at the concrete mesoscale. In this study, MPC controls the simulated surface cracks such that they match the observed cracks by optimizing the internal expansions of springs representing the steel-concrete interface within the RBSM. The applicability of the system is verified using both synthetic crack width data and crack data collected from in-house laboratory testing. In the laboratory testing, corrosion levels were quantified by 3D scanning of the extracted reinforcing bars. The simulation results agree with the corrosion measurements, demonstrating the potential of the MPC-RBSM system for predicting the corrosion distribution along reinforcing bars using surface crack data.

中文翻译：

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使用模型预测控制的中尺度模拟，从观察到的混凝土表面开裂中估计内部腐蚀程度

混凝土结构中钢筋的腐蚀是主要的维护问题之一。腐蚀会在周围的混凝土上产生膨胀压力，从而导致内部损坏，当表面开裂时，这种损坏可能会变得可见。此类损坏可能会降低结构安全性和可维护性。有效的维护需要基于对空间不​​均匀腐蚀水平的估算来评估残余性能，而腐蚀水平的估算通常仅通过表面测量获得。在这项研究中，作者开发了一种模拟系统，用于根据表面裂纹信息估算沿钢筋长度的内部腐蚀程度。该创新系统是通过将模型预测控制（MPC）技术与混凝土中尺度腐蚀引起的开裂的刚体-弹簧模型（RBSM）集成在一起而生产的。在这项研究中，MPC通过优化表示RBSM中钢-混凝土界面的弹簧的内部膨胀，来控制模拟的表面裂纹，使其与观察到的裂纹匹配。使用合成裂缝宽度数据和从内部实验室测试收集的裂缝数据验证了该系统的适用性。在实验室测试中，通过对提取的钢筋进行3D扫描来量化腐蚀程度。仿真结果与腐蚀测量结果吻合，证明了MPC-RBSM系统利用表面裂纹数据预测钢筋沿腐蚀分布的潜力。