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

2017 ◽  
Vol 16 (3) ◽  
pp. 253-261 ◽  
Author(s):  
Xueping Fan
Keyword(s):  
Linear Models ◽  
Sampled Data ◽  
Bridge Monitoring ◽  
Uniform Sampling ◽  
Dynamic Linear Models ◽  
Extreme Stress ◽  
Wisconsin River ◽  
Stress Prediction ◽  
Effective Use

Bridge monitoring systems produce a large amount of data, including uniform and non-uniform sampled data in the long-term service periods; the proper handling of these data is one of the main difficulties in structural health monitoring. To properly predict structural non-uniform extreme stress and deal with the uncertainties of the monitored data, the objectives of this article are to present (a) Bayesian dynamic linear models about non-uniform extreme stress, (b) monitoring mechanism about the Bayesian dynamic linear models based on single and cumulative Bayes’ factors, and (c) an effective use of the Bayesian dynamic linear models to incorporate the dynamic monitored data into structural non-uniform extreme stress prediction. The proposed models and procedure are applied to the monitored data obtained from the I-39 Northbound Bridge over the Wisconsin River in Wausau, Wisconsin, USA.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yuefei Liu ◽  
Qingkai Xiao ◽  
Xueping Fan
Keyword(s):  
Linear Prediction ◽  
Linear Models ◽  
Distribution Functions ◽  
Initial Stress State ◽  
Dynamic Linear Models ◽  
Extreme Stress ◽  
Probability Distribution Functions ◽  
Stress Prediction ◽  
Prediction Approach ◽  
Cubic Function

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.

scholarly journals The Impact of Non-uniform Sampling on Stratospheric Ozone Trends Derived from Occultation Instruments

2017 ◽  
Author(s):  
Robert P. Damadeo ◽  
Joseph M. Zawodny ◽  
Ellis E. Remsberg ◽  
Kaley A. Walker
Keyword(s):  
Stratospheric Ozone ◽  
Primary Source ◽  
Data Sets ◽  
Sampled Data ◽  
Uniform Sampling ◽  
Ozone Trends ◽  
Long Term Trends ◽  
Non Uniform Sampling ◽  
The Impact

Abstract. This paper applies a recently developed technique for deriving long-term trends in ozone from sparsely sampled data sets to multiple occultation instruments simultaneously without the need for homogenization. The technique can compensate for the non-uniform temporal, spatial, and diurnal sampling of the different instruments and can also be used to account for biases and drifts between instruments. These problems have been noted in recent international assessments as being a primary source of uncertainty that clouds the significance of derived trends. Results show potential recovery trends of ~ 2–3 %/decade in the upper stratosphere at mid-latitudes, which are similar to other studies, and also how sampling biases present in these data sets can create differences in derived "recovery" trends of up to ~ 1 %/decade if not properly accounted for. Limitations inherent to all techniques (e.g., relative instrument drifts) and their impacts (e.g., trend differences up to ~ 2 %/decade) are also described and a potential path forward towards resolution is presented.

scholarly journals Incorporation of Structural Health Monitoring Coupled Data in Dynamic Extreme Stress Prediction of Steel Bridges Using Dynamic Coupled Linear Models

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xueping Fan ◽  
Zhipeng Shang ◽  
Guanghong Yang ◽  
Xiaoxiong Zhao ◽  
Yuefei Liu
Keyword(s):  
Time Series ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Linear Models ◽  
Steel Bridges ◽  
Steel Bridge ◽  
Structural Health ◽  
Extreme Stress ◽  
Stress Prediction ◽  
Bridge Reliability

In this article, an approach for using structural health monitoring coupled extreme stress data in dynamic extreme stress prediction of steel bridges is presented, where the coupled extreme stress data means the extreme stress data with dynamicity, randomness, and trend. Firstly, the modeling processes about dynamic coupled linear models (DCLM) are provided based on a supposed coupled time series; furthermore, the dynamic probabilistic recursion processes about DCLM are given with Bayes method; secondly, the monitoring dynamic coupled extreme stress data is taken as a time series, historical monitoring coupled extreme stress data-based DCLM and the corresponding Bayesian probabilistic recursion processes are given for predicting bridge extreme stresses; furthermore, the monitoring mechanism is provided for monitoring the prediction precision of DCLM; finally, the monitoring coupled extreme stress data of a steel bridge is used to illustrate the proposed approach which can provide the foundations for bridge reliability prediction and assessment.

Bayesian prediction of bridge extreme stresses based on DLTM and monitoring coupled data

2019 ◽  
Vol 19 (2) ◽  
pp. 454-462
Author(s):  
Yuefei Liu ◽  
Xueping Fan
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Linear Models ◽  
Linear Trend ◽  
Bayesian Probability ◽  
Dynamic Linear Models ◽  
Extreme Stress ◽  
Local Polynomial ◽  
Series Analysis ◽  
Polynomial Theory

For predicting dynamic coupled extreme stresses of bridges with monitoring coupled data, this article considers monitoring extreme stress data as a time series, and takes into account its coupling generated by the fusion of non-stationarity and randomness. First, the local polynomial theory is introduced, and the local polynomial order of monitoring coupled extreme stress data is estimated with time-series analysis method. Second, based on time-series analysis results, dynamic linear trend models (DLTM) and the corresponding Bayesian probability recursive processes are given to predict dynamic coupled extreme stresses. Finally, through the illustration of monitoring coupled extreme stress data from an actual bridge, the proposed method, which is compared with the traditional Bayesian dynamic linear models, is proved to be more effective for predicting dynamic coupled extreme stresses of bridges.

scholarly journals Predicting Rates of Inbreeding in Populations Undergoing Selection

Genetics ◽  
2000 ◽  
Vol 154 (4) ◽  
pp. 1851-1864 ◽  
Author(s):  
John A Woolliams ◽  
Piter Bijma
Keyword(s):  
Overlapping Generations ◽  
Linear Models ◽  
Selection Process ◽  
Correction Term ◽  
Random Mating ◽  
Nonrandom Mating ◽  
Selection Processes ◽  
Genetic Contributions ◽  
The Relationship

AbstractTractable forms of predicting rates of inbreeding (ΔF) in selected populations with general indices, nonrandom mating, and overlapping generations were developed, with the principal results assuming a period of equilibrium in the selection process. An existing theorem concerning the relationship between squared long-term genetic contributions and rates of inbreeding was extended to nonrandom mating and to overlapping generations. ΔF was shown to be ~¼(1 − ω) times the expected sum of squared lifetime contributions, where ω is the deviation from Hardy-Weinberg proportions. This relationship cannot be used for prediction since it is based upon observed quantities. Therefore, the relationship was further developed to express ΔF in terms of expected long-term contributions that are conditional on a set of selective advantages that relate the selection processes in two consecutive generations and are predictable quantities. With random mating, if selected family sizes are assumed to be independent Poisson variables then the expected long-term contribution could be substituted for the observed, providing ¼ (since ω = 0) was increased to ½. Established theory was used to provide a correction term to account for deviations from the Poisson assumptions. The equations were successfully applied, using simple linear models, to the problem of predicting ΔF with sib indices in discrete generations since previously published solutions had proved complex.

Mother, baby and Facebook makes three: does social media provide social support for new mothers?

2018 ◽  
Vol 168 (1) ◽  
pp. 122-139 ◽  
Author(s):  
Catherine Archer ◽  
Kai-Ti Kao
Keyword(s):  
Social Support ◽  
Social Media ◽  
Media Use ◽  
Long Term Effects ◽  
New Mothers ◽  
Social Media Use ◽  
Extreme Stress ◽  
Ability To Act

Many mothers can find themselves increasingly isolated and overwhelmed after giving birth to a new baby. This period can be a source of extreme stress, anxiety and depression, which can not only have an economic impact on national health services, but can also have long-term effects on the development of the child. At the same time, social media use among most new mothers has become ubiquitous. This research investigates the role of social media, potentially as a mechanism for social support, among Australian mothers of young children aged from birth to 4 years. The findings indicate that participants had mixed responses to their social media use. While social support was deemed a benefit, there were also some negative aspects to social media use identified. The findings highlight the need to critically interrogate social media’s ability to act as a source of social support for new mothers.

Prediction of typhoon tracks using dynamic linear models

2003 ◽  
Vol 20 (3) ◽  
pp. 379-384 ◽  
Author(s):  
Keon-Tae Sohn ◽  
H. Joe Kwon ◽  
Ae-Sook Suh
Keyword(s):  
Linear Models ◽  
Dynamic Linear Models ◽  
Typhoon Tracks
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