Abstract
For a practical structural health monitoring (SHM) system, the traditional single objective methods for optimal sensor placement (OSP) cannot always obtain the optimal result of sensor deployment without sacrificing other targets, which creates obstacles to the efficient use of the sensors. This study mainly focuses on establishing a bi-objective optimization method to select the sensor placement positions. The practical significance of several single-objective criteria for OSP is firstly discussed, based on which a novel bi-objective optimization method is proposed based on the Pareto optimization process, and the corresponding objective functions are established. Furthermore, the non-dominated sorting genetic algorithm is introduced to obtain a series of the Pareto optimal solutions, from which the final solution can be determined based on a new defined membership degree index. Finally, a numerical example of a plane truss is applied to illustrate the proposed method. The Pareto optimization-based bi-objective OSP framework presented in this study could be well suited for solving the problem of multi-objective OSP, which can effectively improve the efficiency of the limited sensors in SHM system.
Highlights
In recent decades, more and more long-span bridges and other large civil infrastructures have been constructed all over the world
The rationality of sensor placement is crucial for the structural health monitoring (SHM) system to identify the structural behavior and evaluate the structural performance [5,6,7,8]
Increasing the number of the deployed sensors will obtain more data related to the structural behaviors and environmental actions, it will sacrifice the economy of SHM systems and cause difficulty for the data analysis
Summary
More and more long-span bridges and other large civil infrastructures have been constructed all over the world. Sankary and Ostfeld proposed a multi-objective optimal criterion for wireless sensor placement, which could considerably improve the quality of the modal information obtained by the sensors and reduce the energy consumption of the sensor network as much as possible [25]. Ostachowicz et al systematically reviewed the traditional sensor placement metrics for three commonly used monitoring techniques They discussed the different optimization algorithms and multi-objective optimization for OSP [29]. The selected sensor placement positions should minimize the maximum off-diagonal element in the MAC matrix, which is defined as the SMI criterion
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