Two-tier data fusion method for bridge condition assessment

Abstract

Fusing collected inspection data provides comprehensive and relatively more accurate diagnostics of defects and accordingly more accurate condition assessment of structures. This paper presents a new two-tier method that utilized data fusion methods for condition assessment of reinforced concrete bridge decks. The method utilizes pixel and feature levels fusion of data collected from multiple nondestructive evaluation (NDE) methods such as ground penetrating radar, impact echo, half-cell potential, and electrical resistivity. Data and measurements of NDE methods are extracted from the Iowa Highway research board project 2011 report for three case studies. It is observed from the three cases that each level of data fusion has its unique advantage. The power of pixel level fusion lies in its ability to provide an overview of bridge deck deterioration in one map as it appears in the fused image. On the other hand, feature fusion works better when only specific types of defects such as corrosion, delamination, and deterioration captured from inspection carried out by each of technologies referred to above. The proposed method is tested against filed inspection methods and core sample results described in the three case studies. The main findings of this research recommend utilizing data fusion in two levels as a new method to facilitate and enhance the confidence and capabilities of inspectors in interpretation of the NDE test results.