A study of multi-target image-based displacement measurement approach for field testing of bridges

As the demand for field testing of bridges grows, so does the need to optimize field testing procedures to allow for a simplified testing strategy that can be employed more effectively. With the advent of computer vision, there has been limited research exploring Feature-Based Image Registration (FBIR) methods for structural testing of bridges. In this paper, the potential of a simple FBIR approach to accurately capture submillimeter displacements using consumer-grade cameras will be demonstrated through a field test on a reinforced concrete slab bridge and on a full-scale bridge deck specimen in the laboratory. The internal and external parameters that influence the results of this measurement strategy were investigated by using various camera positions during the laboratory tests and applying different threshold parameters to the Speeded-Up Robust Features algorithm used for the feature detection and matching. The FBIR method demonstrates great potential, producing an average measurement accuracy within 1.6% of conventional displacement sensors during the field test and 3.3% during the laboratory tests. Altogether, the advantages to this image-based measurement approach enhance the load testing strategy to be implemented by bridge owners at much lower costs and with minimal complication and field setup.