Abstract
In the long-term service period, the bridge health monitoring system produced a huge amount of monitoring stress data; proper handling of these data is one of the main difficulties in the field of structural health monitoring, especially to predict the structural stress based on the monitored data. The objectives of this article are to present: (1) a nonlinear dynamic model, (2) a nonlinear mixed Gaussian particle filtering algorithm for predicting the monitored data based on the dynamic model, and (3) an approach combining nonlinear mixed Gaussian particle filtering algorithm with structural health monitoring data to predict the structural stress under uncertainty in real time. And an actual example is provided to illustrate the application and feasibility of the proposed models and methods.
Highlights
The past several decades have witnessed civil infrastructure design, assessment, and prediction shift from deterministic methodology to probabilistic methodology.[1]
A sound number of studies are mainly focused on the modal parameter identification, structural damage detection, performance prediction, and reliability assessment
Novel monitoring systems used in structural engineering contain sensors providing a large amount of monitored data
Summary
The past several decades have witnessed civil infrastructure design, assessment, and prediction shift from deterministic methodology to probabilistic methodology.[1] Structural health monitoring (SHM) develops so rapidly that it is likely to become a predominant emerging technology to challenge and improve traditional way of design, assessment, and prediction of civil infrastructure. The research on SHM generally experiences two stages. The first stage, falling in the mature stage, is to install sensors on the bridge structures and conduct much research on the data transition system, data acquisition technology, system integration technology, and other aspects.[2,3,4] The second stage is mainly the reasonable application of SHM data. A sound number of studies are mainly focused on the modal parameter identification, structural damage detection, performance prediction, and reliability assessment. Novel monitoring systems used in structural engineering contain sensors providing a large amount of monitored data (stress data).
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