The Limit of Incomplete Information in Inverse Analysis for Hidden Corrosion Inference in Reinforced Concrete

Abstract

One recurring problem in reinforced concrete structures is hidden corrosion. Detection of hidden corrosion is difficult due to incomplete information that can be measured. To overcome this problem, inverse analysis can be employed. Inverse analysis uses incomplete information from field measurement, and employs optimization to infer further information. Previous research has demonstrated the inference capability of inverse analysis for hidden corrosion detection in reinforced concrete. However, question remains on the limit of incompleteness of measurement data. The aim of this study is to simulate and examine the limit of incomplete information that can be allowed for an effective inverse analysis. The inverse analysis of hidden corrosion in reinforced concrete is formulated as a PDE-constrained optimization. The objective function of the optimization is the distance function between measurement data and FEM simulation. The search space is the PDE boundary conditions configuration. A FEM simulation is used to simulate measurement data by half-cell potential mapping on a concrete block of 2 by 1 meters. To simulate incomplete information, the number of measurement points is varied from 2 to 20 points on the surface of the concrete block. Results show that 20 measurement points are needed to successfully infer hidden corrosion profile. However, when the number of measurement points are below this, the effectiveness of inverse analysis varies and does not seem to correlate to the amount of information (i.e., number of measurement points). Further study is needed if this is due to a fluke or choice of measurement point’s location.