scholarly journals Based on M-Copula Reliability Analysis of Random Load Correlation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Huang Bin ◽  
Yan Mingdong ◽  
Liu Xiaogang ◽  
Xiao Mao
Keyword(s):  
Structural Reliability ◽  
Weight Coefficient ◽  
Model Parameters ◽  
Copula Model ◽  
Structural Failure ◽  
Random Load ◽  
Load Combination ◽  
Frank Copula ◽  
Nonparametric Kernel Density ◽  
Nonparametric Kernel

Load is one of the main causes of structural failure, and the correlation among loads would affect the evaluation results of structural performance. The purpose of this paper is to analyze the influence of the correlation among multiple loads on the structural reliability. In this paper, the nonparametric kernel density estimation (NKDE) method is used to estimate the probability density function (PDF) of related loads. In addition, the mixed copula (M-Copula) model is proposed, which combines Gumbel copula, Frank copula, Clayton copula, and weight coefficient, and the model parameters are fitted by MATLAB software to get the correlation of related loads. The reliability based on the related load combination is calculated according to the constructed model. After analyzing three numerical cases, the results show that the probability characteristics of NKDE estimation are very close to the actual conditions, and the reliability calculated by the M-Copula model is larger than those calculated by JCSS, Turkstra, and Gong methods. Using the M-Copula model for load correlation would avoid underestimating the reliability of the structure, which is conducive to structural economic development.

scholarly journals Semiparametric estimation of structural failure time models in continuous-time processes

Biometrika ◽  
2019 ◽  
Author(s):  
S Yang ◽  
K Pieper ◽  
F Cools
Keyword(s):  
Continuous Time ◽  
Failure Time ◽  
Dependent Censoring ◽  
Model Parameters ◽  
Structural Failure ◽  
Time Model ◽  
Efficiency Theory ◽  
Doubly Robust ◽  
Continuous Time Processes ◽  
Artificial Censoring

Summary Structural failure time models are causal models for estimating the effect of time-varying treatments on a survival outcome. G-estimation and artificial censoring have been proposed for estimating the model parameters in the presence of time-dependent confounding and administrative censoring. However, most existing methods require manually pre-processing data into regularly spaced data, which may invalidate the subsequent causal analysis. Moreover, the computation and inference are challenging due to the nonsmoothness of artificial censoring. We propose a class of continuous-time structural failure time models that respects the continuous-time nature of the underlying data processes. Under a martingale condition of no unmeasured confounding, we show that the model parameters are identifiable from a potentially infinite number of estimating equations. Using the semiparametric efficiency theory, we derive the first semiparametric doubly robust estimators, which are consistent if the model for the treatment process or the failure time model, but not necessarily both, is correctly specified. Moreover, we propose using inverse probability of censoring weighting to deal with dependent censoring. In contrast to artificial censoring, our weighting strategy does not introduce nonsmoothness in estimation and ensures that resampling methods can be used for inference.

scholarly journals Recent Developments on the Unified Fatigue Life Prediction Method Based on Fracture Mechanics and its Applications

2020 ◽  
Vol 8 (6) ◽  
pp. 427 ◽  
Author(s):  
Fang Wang ◽  
Weicheng Cui
Keyword(s):  
Fatigue Life ◽  
Fracture Mechanics ◽  
Crack Growth ◽  
Life Prediction ◽  
Structural Reliability ◽  
Influential Factors ◽  
Fatigue Life Prediction ◽  
Model Parameters ◽  
Sensitivity Index ◽  
Marine Structure

Safety analysis and prediction of a marine structure is of great concern by many stakeholders and the general public. In order to accurately predict the structural reliability of an in-use marine structure, one needs to calculate accurately the fatigue crack growth at any service time. This can only be possible by using fracture mechanics approach and the core of fracture-mechanics-based method is to establish an accurate crack growth rate model which must include all the influential factors of the same order of sensitivity index. In 2011, based on the analysis of various influencing factors, the authors put forward a unified fatigue life prediction (UFLP) method for marine structures. In the following ten years of research, some further improvements of this method have been made and the applications of this UFLP are carried out. In this paper, these progresses are reported and its underlying principles are further elaborated. Some basic test data used to determine model parameters are also provided.

Non-Destructive Tracking of Structural Changes of Concrete Mixtures during Thermal Stress

2014 ◽  
Vol 617 ◽  
pp. 152-155 ◽  
Author(s):  
Ivo Kusák ◽  
Miroslav Lunak ◽  
Zdeněk Chobola
Keyword(s):  
Thermal Stress ◽  
Structural Reliability ◽  
Structural Changes ◽  
Dielectric Material ◽  
Temperature Treatment ◽  
High Temperature Treatment ◽  
Model Parameters ◽  
Debye Theory ◽  
Concrete Mixtures ◽  
Non Destructive

This paper deals with the application of the method of impedance spectroscopy to testing of cement-based composites prepared from a mix of cement mortar and quartz sand, which were intentionally degraded by high-temperature treatment (in the temperatures range from 25 °C to 400 °C). The monitoring of structural changes in the thermal stress is very important, for example for determining the reliability of the whole structure. Based on the Debye theory of dielectric the models were created, their applications and received parameters measured dielectric material is characterized and discussed the uniqueness of determining the values of model parameters. Development of electrical parameters and permittivity at each temperature areas reliably show these structural changes and thus the structural reliability.

scholarly journals Structural Reliability Prediction Using Acoustic Emission-Based Modeling of Fatigue Crack Growth

2018 ◽  
Vol 8 (8) ◽  
pp. 1225 ◽  
Author(s):  
Azadeh Keshtgar ◽  
Christine Sauerbrunn ◽  
Mohammad Modarres
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Titanium Alloys ◽  
Crack Growth ◽  
Structural Reliability ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Loading Frequency ◽  
Proposed Model ◽  
Crack Growth Model

In this paper, AE signals collected during fatigue crack-growth of aluminum and titanium alloys (Al7075-T6 and Ti-6Al-4V) were analyzed and compared. Both the aluminum and titanium alloys used in this study are prevalent materials in aerospace structures, which prompted this current investigation. The effect of different loading conditions and loading frequencies on a proposed AE-based crack-growth model were studied. The results suggest that the linear model used to relate AE and crack growth is independent of the loading condition and loading frequency. Also, the model initially developed for the aluminum alloy proves to hold true for the titanium alloy while, as expected, the model parameters are material dependent. The model parameters and their distributions were estimated using a Bayesian regression technique. The proposed model was developed and validated based on post processing and Bayesian analysis of experimental data.

Application of Nonparametric Kernel Density Estimation in Hongkong Stock Market

2011 ◽  
Vol 55-57 ◽  
pp. 209-214
Author(s):  
Yu Ling Wang ◽  
Jing Wang
Keyword(s):  
Distribution Function ◽  
Stock Returns ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
New Method ◽  
Fat Tails ◽  
Nonparametric Kernel Density ◽  
Nonparametric Kernel ◽  
Better Than

A new method, non-parametric kernel density, is used to research the distribution function of HangSeng index returns. The new method can not only depict the character of peak and fat tails of stock returns, but also capture the market risk better than normal distribution. Further more, more accurate conclusions are concluded.

scholarly journals A fast algorithm for background tracking in video surveillance, using nonparametric kernel density estimation

2005 ◽  
Vol 18 (1) ◽  
pp. 127-144 ◽  
Author(s):  
Codrut Ianasi ◽  
Vasile Gui ◽  
Corneliu Toma ◽  
Dan Pescaru
Keyword(s):  
Real Time ◽  
Density Estimation ◽  
Video Surveillance ◽  
Kernel Density Estimation ◽  
Computing Time ◽  
Kernel Density ◽  
Surveillance Systems ◽  
Event Analysis ◽  
Nonparametric Kernel Density ◽  
Nonparametric Kernel

Moving object detection and tracking in video surveillance systems is commonly based on background estimation and subtraction. For satisfactory performance in real world applications, robust estimators, tolerating the presence of outliers in the data, are needed. Nonparametric kernel density estimation has been successfully used in modeling the background statistics due to its capability to perform well without making any assumption about the form of the underlying distributions. However, in real-time applications, the O(N2) complexity of the method can be a bottleneck preventing the object tracking and event analysis modules from having the computing time needed. In this paper, we propose a new background subtraction technique, using multiresolution and recursive density estimation with mean shift based mode tracking. An algorithm with complexity independent on N is developed for fast, real-time implementation. Comparative results with known methods are included, in order to attest the effectiveness and quality of the proposed approach.

Structural Reliability of Oil Tanker in the Adriatic Sea Damaged in Collision and Exposed to Combined Bending Moments

2019 ◽  
Vol 142 (3) ◽  
Author(s):  
Maro Ćorak ◽  
Joško Parunov
Keyword(s):  
Monte Carlo ◽  
Structural Reliability ◽  
Adriatic Sea ◽  
Regression Equations ◽  
Bending Moments ◽  
Load Combination ◽  
Horizontal Wave ◽  
Safety Indices ◽  
Oil Tanker ◽  
Damaged Ship

Abstract The aim of the paper is the assessment of structural reliability of oil tanker, damaged in collision accident in the Adriatic Sea and exposed to combined, horizontal, and vertical bending moments. Damage size is assumed based on the direct numerical simulation of the ship–ship collision. This is justified for some specific sea environments, as the Adriatic Sea, where ship sailing routes and representative ship types involved in accidents are known, so possible collision scenarios may be reasonably predicted. Residual bending moment capacity under combined bending moments (CBM) is calculated using regression equations developed based on non-linear finite element analysis. Still-water vertical bending moments are obtained by damage stability analysis for different collision scenarios that are generated by Monte Carlo (MC) simulations. Vertical and horizontal wave bending moments are determined by short-term response analysis of damaged ship in the Adriatic Sea, using transfer functions obtained by 3D panel hydrodynamic method. Monte Carlo time simulations are performed in order to study probabilistic load combination (LC) considering randomness of the wave process due to different phase angles. Limit state function is defined using interaction equation for damaged ship exposed to combined bending moments. Safety indices are calculated by FORM for each damage scenario by using Turkstra's rule for load combination of vertical and horizontal wave bending moments. Such an approach enables to determine the safety indices for the most frequent damages and also to reveal the most critical situations resulting in the lowest safety indices.

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