Abstract
A method using the support vector machine (SVM) to detect local damages in a building structure with the limited number of sensors is proposed. The SVM is a powerful pattern recognition tool applicable to complicated classification problems. The method is verified to have capability to identify not only the location of damage but also the magnitude of damage with satisfactory accuracy. In our proposed method, feature vectors derived from the modal frequency patterns are used after proper normalization. The feature vectors contain the information on the location and magnitude of damages. As the method does not require modal shapes, typically only two vibration sensors are enough for detecting input and output signals to obtain the modal frequencies. The support vector machines trained for single damage is also effective for detecting damage in multiple stories.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Pages | 118-125 |
| Number of pages | 8 |
| Volume | 5057 |
| DOIs | |
| Publication status | Published - 2003 |
| Event | Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures - San Diego, CA, United States Duration: 2003 Mar 3 → 2003 Mar 6 |
Other
| Other | Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures |
|---|---|
| Country | United States |
| City | San Diego, CA |
| Period | 03/3/3 → 03/3/6 |
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Keywords
- Damage detection
- Health monitoring
- Modal frequency change
- Support vector machine
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics
Cite this
Damage Diagnosis of a Building Structure Using Support Vector Machine and Modal Frequency Patterns. / Mita, Akira; Hagiwara, Hiromi.
Proceedings of SPIE - The International Society for Optical Engineering. Vol. 5057 2003. p. 118-125.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Damage Diagnosis of a Building Structure Using Support Vector Machine and Modal Frequency Patterns
AU - Mita, Akira
AU - Hagiwara, Hiromi
PY - 2003
Y1 - 2003
N2 - A method using the support vector machine (SVM) to detect local damages in a building structure with the limited number of sensors is proposed. The SVM is a powerful pattern recognition tool applicable to complicated classification problems. The method is verified to have capability to identify not only the location of damage but also the magnitude of damage with satisfactory accuracy. In our proposed method, feature vectors derived from the modal frequency patterns are used after proper normalization. The feature vectors contain the information on the location and magnitude of damages. As the method does not require modal shapes, typically only two vibration sensors are enough for detecting input and output signals to obtain the modal frequencies. The support vector machines trained for single damage is also effective for detecting damage in multiple stories.
AB - A method using the support vector machine (SVM) to detect local damages in a building structure with the limited number of sensors is proposed. The SVM is a powerful pattern recognition tool applicable to complicated classification problems. The method is verified to have capability to identify not only the location of damage but also the magnitude of damage with satisfactory accuracy. In our proposed method, feature vectors derived from the modal frequency patterns are used after proper normalization. The feature vectors contain the information on the location and magnitude of damages. As the method does not require modal shapes, typically only two vibration sensors are enough for detecting input and output signals to obtain the modal frequencies. The support vector machines trained for single damage is also effective for detecting damage in multiple stories.
KW - Damage detection
KW - Health monitoring
KW - Modal frequency change
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=0242636137&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0242636137&partnerID=8YFLogxK
U2 - 10.1117/12.482705
DO - 10.1117/12.482705
M3 - Conference contribution
AN - SCOPUS:0242636137
VL - 5057
SP - 118
EP - 125
BT - Proceedings of SPIE - The International Society for Optical Engineering
ER -