Cubic Function-Based Bayesian Dynamic Linear Prediction Approach of Bridge Extreme Stress

Yuefei Liu,Qingkai Xiao,Xueping Fan

doi:10.1155/2017/7460378

Yuefei Liu, Qingkai Xiao + Show 1 more

Open Access

https://doi.org/10.1155/2017/7460378

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Abstract

In structural health monitoring (SHM) field, the structural stress prediction and assessment are the research bottleneck. To reasonably and dynamically predict structural extreme stress based on the time-variant monitored data, the objectives of this paper are to present (a) cubic function-based Bayesian dynamic linear models (BDLM) about monitored extreme stress, (b) choosing method of optimum probability distribution functions about initial stress state, (c) monitoring mechanism of the optimum BDLM, and (d) an effective way of taking advantage of BDLM to incorporate the time-variant monitored data into structural extreme stress prediction. The monitored data of an existing bridge is adopted to illustrate the feasibility and application of the proposed models and procedures.

Highlights

Nowadays, most of the service bridges are close or above their planned lifetime at home and abroad
For bridge performance prediction and assessment based on structural health monitoring (SHM) data, some achievements have been obtained, for instance, extreme stress prediction of bridge based on Bayesian dynamic linear models (BDLM) and monitored data [5,6,7], Bayesian prediction of structural bearing capacity of aging bridges based on dynamic linear model [8], and structural performance prediction using monitored extreme data [9]
These above dynamic linear prediction models are all built with linear functions

Summary

Introduction

Most of the service bridges are close or above their planned lifetime at home and abroad. Based on the dynamic SHM data of bridges, the research on building more reasonable dynamic linear models for predicting the structural extreme stress should be further studied. For the long-term health monitoring extreme stress data, the fitted cubic function h(t) shown in (1) can be more approximately and reasonably adopted than linear function and quadratic function to build the state equation, namely, h (t) = at3 + bt2 + ct + d,. With (5)–(8), the updating relationship between monitored data and state parameters can be obtained as (yt+1 | θt+1) ∼ N [θt+1, Vt+1] , The second step is to get a posteriori PDF of θt+1 based on a priori PDF of θt+1 and monitored data yt+1

Determination of the Main Probability Parameters about

Monitoring Mechanisms about BDLM

Application to an Existing Bridge

G2 G3 G4

Conclusions