Paper
29 July 2004 Experimental study on structural health monitoring of RC columns using self-diagnosis materials
Author Affiliations +
Abstract
The authors have been continuously conducting a series of research works on the development of the fiber reinforced composites as self-diagnosis materials. The function to detect damage is based on the property of carbon materials as a conductor of electricity. The conductive fiber reinforced composite, which is the glass fiber reinforced plastics added carbon particles for electrical conductivity, has been confirmed to possess excellent sensitivity as a self-diagnosis materials. In this study, a self-diagnosis material with the ability to memorize damage history has been applied. Irreversible resistance changes dependent on the strain histories of the composites were utilized to achieve this ability. The authors have also developed an electrically conductive film sensor bonded on the concrete surface to detect cracks and measure crack width. The specimens of the reinforced concrete bridge pier columns were tested under quasi-static cyclic lateral loading. The performance of the proposed self-diagnosis materials to detect damage to concrete structures is evaluated through confirmation of the relationship between the extent of damage and the variation of electrical conductivity of self-diagnosis materials. On the basis of the obtained experimental results, the applicability of self-diagnosis materials to structural health monitoring for concrete structures are discussed in detail, and the practical monitoring techniques for structures are proposed.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hiroshi Inada, Yoshiki Okuhara, and Hitoshi Kumagai "Experimental study on structural health monitoring of RC columns using self-diagnosis materials", Proc. SPIE 5391, Smart Structures and Materials 2004: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, (29 July 2004); https://doi.org/10.1117/12.546269
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Cited by 16 scholarly publications.
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KEYWORDS
Sensors

Resistance

Carbon

Particles

Composites

Structural health monitoring

Glasses

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