Extreme value distribution model of vehicle loads incorporating de-correlated tail fitting and stationary gamma process

2012 ◽  
pp. 676-682
Author(s):  
L Shunlong ◽  
L Hui ◽  
Z Fujian ◽  
G Yiming ◽  
Z Guo
Keyword(s):  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Gamma Process ◽  
Distribution Model ◽  
Vehicle Loads

scholarly journals Probabilistic Modelling of Spatio-Temporal Uncertainties in Degradation

2021 ◽  
Author(s):  
Adetola Adegbola ◽  
Arnold Yuan
Keyword(s):  
Decision Making ◽  
Random Field ◽  
Process Model ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Gamma Process ◽  
Model Parameters ◽  
Two Dimensional ◽  
Practical Application

Deterioration is a major problem facing engineering structures, systems and components (SSCs). To maintain the structural integrity and safe operation of such SSCs all through their service life, it is important to understand how degradation phenomena progress over time and space. Hence degradation modelling has been increasingly used to model existing deterioration, predict future deterioration as well as provide input for infrastructure management in terms of inspection and maintenance decision making. As deterioration is known to be random, modelling of spatial and temporal uncertainty remains a crucial challenge for infrastructure asset professionals. The main objective of the thesis is to develop sophisticated models for characterizing spatial and temporal uncertainties in deterioration modelling with a view to enhancing decision making under uncertainty. The thesis proposes a two-dimensional copula-based gamma distributed random field for the spatial uncertainties, and a copula-based multivariate gamma process model to characterize stochastic dependence of multiple degradation phenomena. Techniques for estimating the model parameters and simulating the field or process, prediction of the remaining lifetime distribution as well as condition-based maintenance optimization are also presented. To study the extreme value distribution of the random field, the thesis also presents a numerical method based on the Karhunen-Loève expansion for evaluating extrema of both one- and two-dimensional homogeneous random fields. The simulation results are benchmarked against existing analytical models for special cases. In addition, the study also investigates the effect of parameter (epistemic) uncertainty on the extreme value distribution of the field. Finally, the thesis presents a practical application of the proposed copula-based gamma field by treating the wall profile of a feeder pipe as one- and twodimensional gamma fields. The thesis demonstrates a practical application of the multivariate gamma process model to rutting, cracking, and surface roughness of highway pavements. In summary, the proposed models have advanced the knowledge and techniques of stochastic deterioration modelling in the engineering field.

scholarly journals Probabilistic Modelling of Spatio-Temporal Uncertainties in Degradation

2021 ◽  
Author(s):  
Adetola Adegbola ◽  
Arnold Yuan
Keyword(s):  
Decision Making ◽  
Random Field ◽  
Process Model ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Gamma Process ◽  
Model Parameters ◽  
Two Dimensional ◽  
Practical Application

Deterioration is a major problem facing engineering structures, systems and components (SSCs). To maintain the structural integrity and safe operation of such SSCs all through their service life, it is important to understand how degradation phenomena progress over time and space. Hence degradation modelling has been increasingly used to model existing deterioration, predict future deterioration as well as provide input for infrastructure management in terms of inspection and maintenance decision making. As deterioration is known to be random, modelling of spatial and temporal uncertainty remains a crucial challenge for infrastructure asset professionals. The main objective of the thesis is to develop sophisticated models for characterizing spatial and temporal uncertainties in deterioration modelling with a view to enhancing decision making under uncertainty. The thesis proposes a two-dimensional copula-based gamma distributed random field for the spatial uncertainties, and a copula-based multivariate gamma process model to characterize stochastic dependence of multiple degradation phenomena. Techniques for estimating the model parameters and simulating the field or process, prediction of the remaining lifetime distribution as well as condition-based maintenance optimization are also presented. To study the extreme value distribution of the random field, the thesis also presents a numerical method based on the Karhunen-Loève expansion for evaluating extrema of both one- and two-dimensional homogeneous random fields. The simulation results are benchmarked against existing analytical models for special cases. In addition, the study also investigates the effect of parameter (epistemic) uncertainty on the extreme value distribution of the field. Finally, the thesis presents a practical application of the proposed copula-based gamma field by treating the wall profile of a feeder pipe as one- and twodimensional gamma fields. The thesis demonstrates a practical application of the multivariate gamma process model to rutting, cracking, and surface roughness of highway pavements. In summary, the proposed models have advanced the knowledge and techniques of stochastic deterioration modelling in the engineering field.

A maximum extreme-value distribution model for switching conductance of oxide-RRAM in memory applications

2020 ◽  
Vol 116 (8) ◽  
pp. 082901 ◽  
Author(s):  
Ernest Wu ◽  
Takashi Ando ◽  
Youngseok Kim ◽  
Ramachandran Muralidhar ◽  
Eduard Cartier ◽  
...  
Keyword(s):  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Distribution Model ◽  
Memory Applications

scholarly journals Introduction of Confidence Interval Based on Probability Limit Method Test into Non-Stationary Hydrological Frequency Analysis

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2727
Author(s):  
Keita Shimizu ◽  
Tadashi Yamada ◽  
Tomohito J. Yamada
Keyword(s):  
Confidence Interval ◽  
Frequency Analysis ◽  
Extreme Value Distribution ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Value Distribution ◽  
Distribution Model ◽  
Probability Limit ◽  
Time Variations ◽  
Hydrological Frequency Analysis

Nonstationarity in hydrological variables has been identified throughout Japan in recent years. As a result, the reliability of designs derived from using method based on the assumption of stationary might deteriorate. Non-stationary hydrological frequency analysis is among the measures to counter this possibility. Using this method, time variations in the probable hydrological quantity can be estimated using a non-stationary extreme value distribution model with time as an explanatory variable. In this study, we build a new method for constructing the confidence interval regarding the non-stationary extreme value distribution by applying a theory of probability limit method test. Furthermore, by introducing a confidence interval based on probability limit method test into the non-stationary hydrological frequency analysis, uncertainty in design rainfall because of lack of observation information was quantified, and it is shown that assessment pertaining to both the occurrence risk of extremely heavy rainfall and changes in the trend of extreme rainfall accompanied with climate change is possible.

scholarly journals Bayesian Updates for an Extreme Value Distribution Model of Bridge Traffic Load Effect Based on SHM Data

2021 ◽  
Vol 13 (15) ◽  
pp. 8631
Author(s):  
Xin Gao ◽  
Gengxin Duan ◽  
Chunguang Lan
Keyword(s):  
Probability Model ◽  
Extreme Value Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Value Distribution ◽  
Traffic Load ◽  
Monitoring Data ◽  
Distribution Model ◽  
Load Effect ◽  
Tail Distribution

As the distribution function of traffic load effect on bridge structures has always been unknown or very complicated, a probability model of extreme traffic load effect during service periods has not yet been perfectly predicted by the traditional extreme value theory. Here, we focus on this problem and introduce a novel method based on the bridge structural health monitoring data. The method was based on the fact that the tails of the probability distribution governed the behavior of extreme values. The generalized Pareto distribution was applied to model the tail distribution of traffic load effect using the peak-over-threshold method, while the filtered Poisson process was used to model the traffic load effect stochastic process. The parameters of the extreme value distribution of traffic load effect during a service period could be determined by theoretical derivation if the parameters of tail distribution were estimated. Moreover, Bayes’ theorem was applied to update the distribution model to reduce the statistical uncertainty. Finally, the rationality of the proposed method was applied to analyze the monitoring data of concrete-filled steel tube arch bridge suspenders. The results proved that the approach was convenient and found that the extreme value distribution type III might be more suitable as the traffic load effect probability model.

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