Bridge extreme stress prediction based on Bayesian dynamic linear models and non-uniform sampling

Fan, Xiaohui

doi:10.1177/1475921716688166

Cited by 18 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang and his colleagues 30 handle SHM issues under BDLM framework, where time-dependent structural response was modeled through the separation of subcomponents, with the model parameters estimated utilizing an expectation maximization (EM) algorithm. [31][32][33] Fan 34 predicted the nonuniform extreme stress of I-39 Northbound Bridge in the USA using BDLM with the secondorder polynomial and showed that the predicted extreme stress was basically consistent with the observations. Liu 35 proposed a BDLM framework based on the cubic function to select the optimal probability distribution function of the initial stress state to predict the extreme stress of the structure effectively.…”

Section: Introductionsupporting

confidence: 56%

Bayesian dynamic linear model framework for structural health monitoring data forecasting and missing data imputation during typhoon events

Wang

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

A Bayesian dynamic linear model (BDLM) framework for data modeling and forecasting is proposed to evaluate the performance of an operational cable-stayed bridge, that is, Ting Kau Bridge in Hong Kong, by using SHM strain field data acquired. One of the major challenges in dealing with the existing in-service bridge under extreme typhoon loads is to forecast structural behavior using the typhoon response exhibiting non-stationarity, large data fluctuations and strong randomness. The first attempt for SHM data modeling during extreme events, that is, typhoons, using BDLM framework, was conducted in this study. The data from multiple sensors are analyzed for one-step, multi-step ahead forecasting and missing data imputation. The overall bridge behavior is incorporated into a forecasting model by superposition of forecasting results of trend (representing the structural baseline response), periodic component (response component evolving regularly over time), and autoregressive component (time-dependent error) through BDLM algorithm. The results demonstrate that the BDLM framework yielded more accurate calculations compared with Gaussian process and Variational Heteroscedasticity Gaussian Process methods with respect to one-step ahead forecasting for strain data under typhoons. Multi-step ahead forecasting was successfully carried out both for non-typhoon and typhoon responses within an acceptable precision range. The correlation between periodic component and temperature was also investigated. Regarding missing data imputation, BLDM algorithm can generate robust results due to making full use of the monitoring data both before and after the missing segments.

show abstract

Section: Introductionsupporting

confidence: 56%

Bayesian dynamic linear model framework for structural health monitoring data forecasting and missing data imputation during typhoon events

Wang

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Usually, the relationship between each explanatory variable and the response variable should be solved at first. The abovementioned linking mechanism can be defined as generalized linear and non-linear models [ 26 , 44 ], of which the first one has a simpler structure and can be adopted by a wide range of problems. In this study, we establish the linear regression model through the mean response variable given the explanatory variables.…”

Section: Dynamic Bayesian Networkmentioning

confidence: 99%

Reliability Assessment of Deflection Limit State of a Simply Supported Bridge using vibration data and Dynamic Bayesian Network Inference

Liu

Jiao

et al. 2019

Sensors

View full text Add to dashboard Cite

Structural health monitoring (SHM) has been widely used in all kinds of bridges. It is significant to accurately assess the serviceability and reliability of bridge subjected to severe conditions by SHM technique. Bridge deflection as an essential evaluation index can reflect structural condition perfectly. In this study, an approach for deflection calculation and reliability assessment of simply supported bridge is presented. Firstly, a bridge deflection calculation method is proposed based on modal flexibility and Kriging method improved by artificial bee colony algorithm. Secondly, a dynamic Bayesian network is employed to evaluate the deflection reliability combined with monitoring results which include modal frequency, mode shape, environmental temperature, and humidity. A linear regression model is established to analyze the relationship between modal parameters and environmental factors. Thirdly, a simply supported bridge is constructed and monitored to verify the effectiveness of the proposed method. The results reveal that the proposed method can precisely calculate the bridge deflection. Finally, the time-dependent reliabilities of two cases are computed and the effects of monitoring factors on bridge deflection reliability are analyzed by sensitivity parameter. It indicates that the reliability is negatively correlated with temperature and more sensitive to mode shape than other three factors.

show abstract

“…29 ARMA models can be formulated using state-space equation as dynamic linear models (DLMs), and these models include regression, trend, seasonality and AR components within a single formulation to account for the environmental and operational effects. 29,40,41,43 Bayesian formulation of such models, BDLMs, have become increasingly popular in recent years for modelling static time-series data such as extreme stresses 41 and TIS. 29 While BDLM/DLM are the most generalised formulation, the complex task of variance and co-variance matrix conversion restricts the number of parameters that can be included in these models.…”

Section: Introductionmentioning

confidence: 99%

A dynamic harmonic regression approach for bridge structural health monitoring

Buckley

Pakrashi

Ghosh

2021

Structural Health Monitoring

View full text Add to dashboard Cite

Structural damage in a bridge is defined as a significant deviation in the structural response from its standard operating conditions, not explainable by variations in external environmental and operational effects. However, environmental effects such as temperature fluctuations can cause significant seasonal variations in the structural response of a bridge and can mask its changes due to structural damage. This poses a challenge for structural health monitoring of bridges where reliable diagnosis of damage or deterioration is often related to isolation of the responses. To address it, a statistical damage-detection methodology is introduced where strain data are modelled using a dynamic harmonic regression time-series model. Prediction intervals of multi-step ahead forecasts from the dynamic harmonic regression model are then used as statistical control limits within which the observed phenomenon should fall under standard operating conditions. This single recursive structural health monitoring framework for automatic fitting and multi-step ahead forecasting of 1-min interval time-series strain data includes recorded temperature values and diurnal trends as regressors in the model to account for environmental variations. The potential of this method as a robust automatic structural health monitoring strategy is demonstrated on strain data sampled at 1-min interval from a full-scale damaged pre-stressed concrete bridge – before, during and after repair. The proposed method can capture both sudden and daily changes in structural response due to temperature effects, and a rolling multi-step ahead interval forecast was able to identify damage on back-cast data transitioning from a healthy state to a damaged state.

show abstract

Bridge extreme stress prediction based on Bayesian dynamic linear models and non-uniform sampling

Cited by 18 publications

References 13 publications

Bayesian dynamic linear model framework for structural health monitoring data forecasting and missing data imputation during typhoon events

Bayesian dynamic linear model framework for structural health monitoring data forecasting and missing data imputation during typhoon events

Reliability Assessment of Deflection Limit State of a Simply Supported Bridge using vibration data and Dynamic Bayesian Network Inference

A dynamic harmonic regression approach for bridge structural health monitoring

Contact Info

Product

Resources

About