Uncertainty Analysis of Inverse Problem of Resistivity Model in Internal Defects Detection of Buildings

Abstract

Fissure detection in ancient buildings is of vital importance in the evaluation of resistance or remediation in urban areas. Electrical resistivity imaging is an efficient tool to detect fissures or moisture erosion in buildings by highlighting the resistivity contrasts in the inversion models. The traditional results of ERT images give deterministic interpretations of the internal artifact. However, the existence of equivalent models may correspond to different physical realities in engineering cases, to which the traditional ERT model cannot respond. In this paper, through the application of a field test on an ancient wall, it is shown that the segmentation of the equivalent model family is applicable to solve the internal defects detection problem in a probabilistic approach. It is achieved by performing a probabilistic approach to apply the uncertainty analysis. The procedure begins with the reduction in dimensions of the model by spectral decomposition, and the uncertainty space is rebuilt via Particle Swarm Optimization (PSO). By computing the uncertainty space, probabilistic maps are created to demonstrate the electrical anomaly in a simpler structure. The proposed method provides a more accurate approach for the internal defects detection of buildings by considering the possibilities hidden in the equivalent model family of ERT results.