Abstract
Civil engineering structures can undergo serious damage due to impact forces. But accurate and rapid identification of impact force is quite challenging because its measurement is difficult and location is unpredictable. This study proposes a novel approach for the complete identification of impact force including its location and time history. The proposed method combines an augmented Kalman filter (AKF) and Genetic algorithm (GA) for accurate identification of impact force. In AKF unknow force is included in the state vector and estimated in conjunction with the states. First, the location of impact force is statistically determined in the way to minimize the AKF response estimate error at measured locations, assumed co-variance values are used in AKF at this stage. These values are assumed based on a few analyses in which force location is assumed to be known. Then, GA is applied to optimize the error co-variances by minimizing the error between measured and estimated structural response. Once optimized co-variances are obtained, the exact time history of impact force can be constructed using AKF. Numerical example of a truss is considered to validate the efficacy of proposed approach. Strain and acceleration measurements are used as input for the AKF. Both modelling error and measurement noise are considered in the analysis to simulate the actual field conditions.
| Original language | English (US) |
|---|---|
| Title of host publication | Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018 |
| Editors | Kon-Well Wang, Hoon Sohn, Jerome P. Lynch |
| Publisher | SPIE |
| Volume | 10598 |
| ISBN (Electronic) | 9781510616929 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Externally published | Yes |
| Event | Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018 - Denver, United States Duration: Mar 5 2018 → Mar 8 2018 |
Other
| Other | Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018 |
|---|---|
| Country | United States |
| City | Denver |
| Period | 3/5/18 → 3/8/18 |
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Keywords
- Augmented Kalman Filter
- data fusion
- Genetic algorithm
- Impact force
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
Heterogeneous data fusion for impact force identification in truss structures. / Saleem, Muhammad M.; Jo, Hongki.
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018. ed. / Kon-Well Wang; Hoon Sohn; Jerome P. Lynch. Vol. 10598 SPIE, 2018. 105981X.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Heterogeneous data fusion for impact force identification in truss structures
AU - Saleem, Muhammad M.
AU - Jo, Hongki
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Civil engineering structures can undergo serious damage due to impact forces. But accurate and rapid identification of impact force is quite challenging because its measurement is difficult and location is unpredictable. This study proposes a novel approach for the complete identification of impact force including its location and time history. The proposed method combines an augmented Kalman filter (AKF) and Genetic algorithm (GA) for accurate identification of impact force. In AKF unknow force is included in the state vector and estimated in conjunction with the states. First, the location of impact force is statistically determined in the way to minimize the AKF response estimate error at measured locations, assumed co-variance values are used in AKF at this stage. These values are assumed based on a few analyses in which force location is assumed to be known. Then, GA is applied to optimize the error co-variances by minimizing the error between measured and estimated structural response. Once optimized co-variances are obtained, the exact time history of impact force can be constructed using AKF. Numerical example of a truss is considered to validate the efficacy of proposed approach. Strain and acceleration measurements are used as input for the AKF. Both modelling error and measurement noise are considered in the analysis to simulate the actual field conditions.
AB - Civil engineering structures can undergo serious damage due to impact forces. But accurate and rapid identification of impact force is quite challenging because its measurement is difficult and location is unpredictable. This study proposes a novel approach for the complete identification of impact force including its location and time history. The proposed method combines an augmented Kalman filter (AKF) and Genetic algorithm (GA) for accurate identification of impact force. In AKF unknow force is included in the state vector and estimated in conjunction with the states. First, the location of impact force is statistically determined in the way to minimize the AKF response estimate error at measured locations, assumed co-variance values are used in AKF at this stage. These values are assumed based on a few analyses in which force location is assumed to be known. Then, GA is applied to optimize the error co-variances by minimizing the error between measured and estimated structural response. Once optimized co-variances are obtained, the exact time history of impact force can be constructed using AKF. Numerical example of a truss is considered to validate the efficacy of proposed approach. Strain and acceleration measurements are used as input for the AKF. Both modelling error and measurement noise are considered in the analysis to simulate the actual field conditions.
KW - Augmented Kalman Filter
KW - data fusion
KW - Genetic algorithm
KW - Impact force
UR - http://www.scopus.com/inward/record.url?scp=85049577231&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049577231&partnerID=8YFLogxK
U2 - 10.1117/12.2296763
DO - 10.1117/12.2296763
M3 - Conference contribution
AN - SCOPUS:85049577231
VL - 10598
BT - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018
A2 - Wang, Kon-Well
A2 - Sohn, Hoon
A2 - Lynch, Jerome P.
PB - SPIE
ER -