Sensing distortion-induced fatigue cracks in steel bridges with capacitive skin sensor arrays. (11th October 2018)

• Record Type: Journal Article
• Authors: Kong, Xiangxiong; Li, Jian; Collins, William; Bennett, Caroline; Laflamme, Simon; Jo, Hongki
• Is Part Of: Smart materials and structures. Volume 27:Number 11(2018:Nov.)
• Publication Date: 2018-10-11
• Subjects: distortion-induced fatigue -- sensing skin -- structural health monitoring -- capacitive strain sensor -- large area electronics -- steel bridges; Smart materials -- Periodicals; Strucural design -- Periodicals; 620.11
• DOI: 10.1088/1361-665X/aadbfb
• Languages: English
• ISSNs: 0964-1726

Abstract

Abstract: Distortion-induced fatigue cracks represent the majority of fatigue cracks in steel bridges in the United States. Currently, bridge owners, such as the state departments of transportation, rely on human inspection to detect, monitor, and quantify these cracks so that appropriate repairs can be applied before cracks reach critical sizes. However, visual inspections are costly, labor intensive, and may be prone to error due to inconsistent skills among bridge inspectors. In this study, we represent a novel strain-based approach for sensing distortion-induced fatigue cracks in steel bridges using soft elastomeric capacitor (SEC) arrays. Compared with traditional foil strain gauges, the SEC technology is a large-area and flexible skin-type strain sensor that can measure a wide range of strain over a large surface. Previous investigations have verified the suitability of a single SEC for sensing an in-plane fatigue crack in a small-scale steel specimen. In this paper, we further demonstrate the ability of SECs for sensing distortion-induced fatigue cracks. The proposed strategy consists of deploying an array of SECs to cover a large fatigue-susceptible region and establishing a fatigue sensing algorithm by constructing a crack growth index (CGI) map. The effectiveness of the strategy was experimentally validated through fatigue tests of bridge girder to cross-frame connection models with distortion-induced fatigue cracks. Test results verified that by deploying an SEC array, multiple CGIs can be obtained over the fatigue-susceptible region, offering a more comprehensive picture of fatigue damage. Furthermore, by monitoring a series of CGI maps constructed under different fatigue cycles, the fatigue crack growth can be clearly visualized through the intensity change in the CGI maps.