Optimization of Observation Condition on Inverse Analysis for Identifying Corrosion of Steel in Concrete

摘要

The purpose of this study is to optimize the observation condition on the inverse problem for estimating the real and imaginary parts of the concrete conductivity and the impedance of the steel-concrete interface. The optimization is achieved by minimizing the average of eigen values of a posteriori estimate error covariance matrix. We performed a numerical identification to demonstrate the effectiveness of the optimized observation condition. The estimation is carried out by using the Kalman Filter algorithm. The simulation result shows that the real part of the impedance can be estimated with a high accuracy while the others cannot be well estimated. To overcome the above difficulty, a priori information and other kinds of observation conditions are considered.