A Combined Optimal Sensor Placement Strategy for the Structural Health Monitoring of Bridge Structures

Abstract

Optimal sensor placement is an important part in the structural health monitoring of bridge structures. However, some defects are present in the existing methods, such as the focus on a single optimal index, the selection of modal order and sensor number based on experience, and the long computation time. A hybrid optimization strategy named MSE-AGA is proposed in this study to address these problems. The approach firstly selects modal order using modal participation factor. Then, the modal strain energy method is adopted to conduct the initial sensor placement. Finally, the adaptive genetic algorithm (AGA) is utilized to determine the optimal number and locations of the sensors, which uses the root mean square of off-diagonal elements in the modal assurance criterion matrix as the fitness function. A case study of sensor placement on a numerically simulated bridge structure is provided to verify the effectiveness of the MSE-AGA strategy, and the AGA method without initial placement is used as a contrast experiment. A comparison of these strategies shows that the optimal results obtained by the MSE-AGA method have a high modal strain energy index, a short computation time, and small off-diagonal elements in the modal assurance criterion matrix.