Damage evaluation for concrete structures using fiber-reinforced composites as self-diagnosis materials

Kenji Yoshitake; Keiji Shiba; Makoto Suzuki; Minoru Sugita; Yoshiki Okuhara

doi:10.1117/12.539732

27 July 2004 Damage evaluation for concrete structures using fiber-reinforced composites as self-diagnosis materials

Kenji Yoshitake, Keiji Shiba, Makoto Suzuki, Minoru Sugita, Yoshiki Okuhara

Proceedings Volume 5384, Smart Structures and Materials 2004: Smart Sensor Technology and Measurement Systems; (2004) https://doi.org/10.1117/12.539732
Event: Smart Structures and Materials, 2004, San Diego, CA, United States

Abstract

Health monitoring techniques that utilize structural materials with the ability to diagnose their own condition, so-called self-diagnosis materials, have been under development. The authors have developed two types of electrically conductive fiber reinforced composite to diagnose cracks in concrete structures: a high sensitivity detection sensor and maximum strain memory sensor. Three points bending tests on pre-notched reinforced concrete beam under the cyclic loading is presented using these two self-diagnosis materials, with attention towards the relationship between crack width of the concrete beam and electric resistance. Moreover, effects of volume fraction of carbon particle on memorizing maximum strain are investigated. It has been proved that both self-diagnosis materials are highly effective to detect the cracks in the concrete. And present strain can be obtained by the proposed fiber reinforced plastic composites. Although volume fraction of carbon particle has significant influence on the characteristics of memorizing maximum strain, maximum strain of the concrete structures can be memorized using the appropriate self-diagnosis materials.

Citation Download Citation

Kenji Yoshitake, Keiji Shiba, Makoto Suzuki, Minoru Sugita, and Yoshiki Okuhara "Damage evaluation for concrete structures using fiber-reinforced composites as self-diagnosis materials", Proc. SPIE 5384, Smart Structures and Materials 2004: Smart Sensor Technology and Measurement Systems, (27 July 2004); https://doi.org/10.1117/12.539732

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