Optimal Sensor Configuration for Bridge Structures Following a Probabilistic Approach

Abstract

Listen

Translate article

In this paper, a statistical methodology is introduced for identifying the most effective way to install a limited number of sensors on a typical distributed-parameter system, i.e., a three-span continuous bridge model with two elastic supports to extract as much information as possible. This is performed by minimizing the uncertainties associated with the identified bridge modeling parameters. In the proposed methodology, the information entropy is employed as a measure to quantify the uncertainty of the identified structural modeling parameters. The problem of optimal sensor placement is then formulated as a continuous optimization problem, in which the information entropy is minimized, with the sensor configurations as the minimization variables. The generally used discrete-coordinate systems modeled by the finite element (FE) method can only approximate their actual dynamic behavior, which would in turn influence the sensor configuration results, and the sensors are confined to be only put on discrete nodes related to the coarse mesh scheme usually employed. For structures such as bridges belonging to typical distributed-parameter systems, it’s more reasonable and convenient to be modeled as continues-coordinate system with analytical formulation. It’s the main purpose of this paper to develop a sensor placement method for continues-coordinate systems following the Bayesian theorem and the information entropy method, with the binary-encoded genetic algorithm (GA) employed as the optimization technique.