Structural health monitoring of bridges using digital image correlation

Abstract

Due to the aging global civil infrastructure (e.g. bridges), there is a critical need for monitoring and assessing structural integrity of large scale structures. According to the ASCE, in 2008, the average bridge in the U.S.A. was 43 years old and 161,892 bridges were structurally deficient or obsolete. Currently, bridge health is assessed primarily using qualitative visual inspection, which is not always reliable because some damage is difficult to detect, quantify visually, or is subject to human interpretation. Traditional sensors such as strain gages, and displacement sensors, have been recently used to monitor bridges. These sensors only measure at discrete points or along a line, making it difficult to detect damage that is not in the immediate vicinity of the sensor or is difficult to interpret. To address these issues, this paper investigates the use of three-dimensional (3D) digital image correlation (DIC) as a sensing approach for improved bridge structural health monitoring. 3D DIC is a non-contact, full field, optical measuring technique that uses digital cameras to measure surface geometry, displacement, and strain. It is proposed that DIC can be used for monitoring by imaging a bridge periodically and computing strain and displacement from images recorded at different dates or operating conditions. In this paper, DIC is shown to locate non-visible cracks in concrete, quantify spalling, and measure bridge deformation. These techniques are first demonstrated in the laboratory. Field measurements are also made on three full-scale bridges. This paper discusses challenges and solutions to implementing DIC on large structures in the field. The results reveal that DIC is an effective approach to monitor the integrity of large scale civil infrastructure.