scholarly journals Long term prediction of nonlinear viscoelastic creep behaviour of elastomers: extended Schapery model

2008 ◽  
Vol 9 (5) ◽  
pp. 407-416 ◽  
Author(s):  
Hatem Gacem ◽  
Yvon Chevalier ◽  
Jean-Luc Dion ◽  
Mohamed Soula ◽  
Brahim Rezgui
Keyword(s):  
Creep Behaviour ◽  
Viscoelastic Creep ◽  
Term Prediction ◽  
Nonlinear Viscoelastic ◽  
Schapery Model ◽  
Long Term Prediction

Nonlinear Viscoelastic Creep Characterization of HDPE-Rice Husk Composites

2005 ◽  
Vol 13 (6) ◽  
pp. 581-598 ◽  
Author(s):  
A. Pramanick ◽  
M. Sain
Keyword(s):  
Rice Husk ◽  
Creep Behaviour ◽  
Nonlinear Creep ◽  
Flexural Mode ◽  
Viscoelastic Creep ◽  
Nonlinear Viscoelastic ◽  
Bearing Materials ◽  
Term Creep

Rice husk based plastic composites are increasingly being used as deck-boards, railings and other load-bearing materials. Since this material typically contains 40% plastic, and plastics creep with respect to time when they carry load, creep is an important issue here. So the viscoelastic characterization of this material and the prediction of creep as a function time is of paramount importance for the material's long-term commercial success. Creep is a time related deformation but it can also be affected by the stress level and environmental conditions, such as time and temperature. In order to predict the creep of this composite, it is important to derive a relationship between deformation, time, temperature, relative humidity and stress. Nonlinearity can exist in the stress, temperature, and moisture related deformation. In this study, hollow extruded rice husk -HDPE beams were subjected to creep and recovery in flexural mode and the stress related nonlinear creep behaviour of the same was studied phenomenologically. Both linear and non-linear region constants were determined with modified models, and a predictive model was developed. These constants will be used to define, model and predict long-term creep deformation.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

Long term prediction of forest resources

1985 ◽  
Vol 12 ◽  
pp. 176
Author(s):  
H Kurth ◽  
B Anders ◽  
G Lucas
Keyword(s):  
Forest Resources ◽  
Term Prediction ◽  
Long Term Prediction
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