2004 Fiscal Year Final Research Report Summary
Study on the Damage Assessment method of Reinforced Concrete Structure by Nondestructive Impact Type Inspection
| Project/Area Number |
15560407
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Structural engineering/Earthquake engineering/Maintenance management engineering
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| Research Institution | KYUSHU UNIVERSITY |
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Principal Investigator |
SONODA Yoshimi Kyushu University, Faculty of Engineering, Associate Professor, 工学研究院・建設デザイン部門, 助教授 (40304737)
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| Co-Investigator(Kenkyū-buntansha) |
KATSUKI Satoshi National Defense Academy, Faculty of Engineering, Professor, 建設環境工学科, 教授
HIKOSAKA Hiroshi Kyushu University, Faculty of Engineering, Professor, 工学研究院・建設デザイン部門, 教授 (10037864)
HINO Shinichi Kyushu University, Faculty of Engineering, Professor, 工学研究院・建設デザイン部門, 教授 (00136532)
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| Project Period (FY) |
2003 – 2004
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| Keywords | Damage Assessment / Nondestructive test / Reinforced Concrete Structure / Damage Mechanics / Multi-pattern Differentiation Neural Network |
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| Research Abstract |
Recently, the maintenance problem of the existing RC structures has increased in Japan, and it is important to estimate the total life cycle cost of structure. In order to attain this purpose, it is necessary to evaluate the durability and predict the deterioration of the existing structures accurately. In this study, the following considerations are presented.
1)Several Nondestructive Impact Type Inspections were performed for the existing reinforced concrete bridges in order to investigate its reliability and the accuracy.
2)To develop the analytical method that could estimate the damage evolution process caused by chemical and mechanical effect, numerical program based on continuum damage mechanics was constructed. It is found that the proposed method could evaluate the durability of the existing RC beam, and the change of its ultimate strength under the various exposition conditions.
3)This study also presents an application of multi-pattern differentiation neural network for monitoring of fatigue damage in concrete material. This system uses the relationship between input data and output data for monitoring signal to detect the change of structural characteristic, and it can detect the progress of fatigue damage of concrete very well which has plural pattern between input data and output data
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