Crack occurrence and propagation are among critical factors that affect the performance and lifespan of civil infrastructures such as bridges, pipelines, and so on. As a consequence, numerous crack detection and characterization techniques have been researched and developed in the past decades in the areas of SHM and non-destructive evaluation (NDE). The significant amount of performed studies and the large number of publications give rise to the need to systematize, condensate, and summarize this enormous effort. The aims of this paper are to summarize the knowledge about cracking and its sources, review both existing and emerging methods for crack detection and characterization, and identify the advantages and challenges for these methods. In general, this paper identifies two sensing approaches (direct and indirect) and two data analysis approaches (model-based and model-free or data-driven) along with a range of associated technologies. The advantages and challenges of each approach and technology are discussed and summarized, and the future research needs are identified. This paper is intended to serve as a reference for researchers who are interested in crack detection and characterization as well as for those who are generally interested in SHM and NDE. Copyright © 2014 John Wiley & Sons, Ltd.

Reliable early-stage damage detection requires continuous monitoring over large areas of structure, and with sensors of high spatial resolution. Technologies based on Large Area Electronics (LAE) can enable direct sensing and can be scaled to the level required for Structural Health Monitoring (SHM) of civil structures and infrastructure. Sensing sheets based on LAE contain dense arrangements of thin-film strain sensors, associated electronics and various control circuits deposited and integrated on a flexible polyimide substrate that can cover large areas of structures. This paper presents the development stage of a prototype strain sensing sheet based on LAE for crack detection and localization. Two types of sensing-sheet arrangements with size 6 × 6 inch (152 × 152 mm) were designed and manufactured, one with a very dense arrangement of sensors and the other with a less dense arrangement of sensors. The sensing sheets were bonded to steel plates, which had a notch on the boundary, so the fatigue cracks could be generated under cyclic loading. The sensors within the sensing sheet that were close to the notch tip successfully detected the initialization of fatigue crack and localized the damage on the plate. The sensors that were away from the crack successfully detected the propagation of fatigue cracks based on the time history of the measured strain. The results of the tests have validated the general principles of the proposed sensing sheets for crack detection and identified advantages and challenges of the two tested designs.

Reliable early-stage damage detection and localization ideally requires continuous sensing over large areas with dense arrays of sensors, and that is the reason for the creation of sensing sheets based on large area electronics, which contains a large number of densely spaced unit sensors. However, although the sensors are densely spaced, there are some empty spaces between them and these spaces are not sensitive to minute damage. This raises three fundamental questions addressed in this article: How do the sizes of the non-instrumented areas influence the reliability in damage detection? What is the most efficient arrangement of the sensors in the sensing sheet? Given the size of the sensing sheet and given the combined size of all the sensors, is it more efficient to use small amount of large sensors, or large amount of small sensors? A probabilistic approach has been taken to address these questions. Analytical expressions were derived first, followed with the generation and validation of Monte Carlo simulations. Then the probability that the sensing sheet with a given surface area and given number, size, and spacing of unit sensors can detect a crack with a given size was derived. Based on these probability of detection functions, it was possible to assess the reliability of sensing sheets for crack detection and to establish general principles for the design of sensing sheets so that the probability of detection is maximized. The presented probability of detection analysis can be modified and applied beyond the scope of the sensing sheet project to other sensor networks used in strain-based structural health monitoring.