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A platform for research: civil engineering, architecture and urbanism
Optimal Sensor Placement Using a Combined Genetic Algorithm–Ensemble Kalman Filter Framework
With recent technological advances, the deployment of monitoring devices in structures is becoming more abundant. In an ideal scenario, one would deploy sensors on every corner of a structure, but the problem of dealing with the substantial amount of online data thus generated arises. This study presents a novel framework based on combining genetic algorithm (GA) techniques with the Ensemble Kalman filter (EnKF) approach to identify the optimal sensor locations for structural system identification and damage-detection purposes. The GA approach is first used to generate a random initial set of sensor locations, then through a minimization procedure, the best locations of the sensors are determined. In this study, the fitness function to be minimized is taken to be the difference between synthetically generated actual measurement data and their respective predicted values, calculated using EnKF through estimating and updating the system state and model parameters. The sensor configuration of a 10-story shear building subjected to El-Centro earthquake excitation at its base is developed to illustrate the performance of the proposed methodology.
Optimal Sensor Placement Using a Combined Genetic Algorithm–Ensemble Kalman Filter Framework
With recent technological advances, the deployment of monitoring devices in structures is becoming more abundant. In an ideal scenario, one would deploy sensors on every corner of a structure, but the problem of dealing with the substantial amount of online data thus generated arises. This study presents a novel framework based on combining genetic algorithm (GA) techniques with the Ensemble Kalman filter (EnKF) approach to identify the optimal sensor locations for structural system identification and damage-detection purposes. The GA approach is first used to generate a random initial set of sensor locations, then through a minimization procedure, the best locations of the sensors are determined. In this study, the fitness function to be minimized is taken to be the difference between synthetically generated actual measurement data and their respective predicted values, calculated using EnKF through estimating and updating the system state and model parameters. The sensor configuration of a 10-story shear building subjected to El-Centro earthquake excitation at its base is developed to illustrate the performance of the proposed methodology.
Optimal Sensor Placement Using a Combined Genetic Algorithm–Ensemble Kalman Filter Framework
Nasr, Dana E. (author) / Saad, George A. (author)
2016-07-20
Article (Journal)
Electronic Resource
Unknown
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