A large-area strain sensing technology for monitoring fatigue cracks in steel bridges. (11th July 2017)

• 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 26:Number 8(2017:Aug.)
• Publication Date: 2017-07-11
• Subjects: fatigue crack -- sensing skin -- structural health monitoring -- capacitive strain sensor -- soft elastomeric capacitor -- high-cycle fatigue -- crack detection; Smart materials -- Periodicals; Strucural design -- Periodicals; 620.11
• DOI: 10.1088/1361-665X/aa75ef
• Languages: English
• ISSNs: 0964-1726

Abstract

Abstract: This paper presents a novel large-area strain sensing technology for monitoring fatigue cracks in steel bridges. The technology is based on a soft elastomeric capacitor (SEC), which serves as a flexible and large-area strain gauge. Previous experiments have verified the SEC's capability to monitor low-cycle fatigue cracks experiencing large plastic deformation and large crack opening. Here an investigation into further extending the SEC's capability for long-term monitoring of fatigue cracks in steel bridges subject to traffic loading, which experience smaller crack openings. It is proposed that the peak-to-peak amplitude (pk–pk amplitude) of the sensor's capacitance measurement as the indicator of crack growth to achieve robustness against capacitance drift during long-term monitoring. Then a robust crack monitoring algorithm is developed to reliably identify the level of pk–pk amplitudes through frequency analysis, from which a crack growth index (CGI) is obtained for monitoring fatigue crack growth under various loading conditions. To generate representative fatigue cracks in a laboratory, loading protocols were designed based on constant ranges of stress intensity to limit plastic deformations at the crack tip. A series of small-scale fatigue tests were performed under the designed loading protocols with various stress intensity ratios. Test results under the realistic fatigue crack conditions demonstrated the proposed crack monitoring algorithm can generate robust CGIs which are positively correlated with crack lengths and independent from loading conditions.