scholarly journals Estimation of Extreme Cable Forces of Cable-Stayed Bridges Based on Monitoring Data and Random Vehicle Models

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yuan Ren ◽  
Zhiyuan Zhu ◽  
Ziyuan Fan ◽  
Qiao Huang
Keyword(s):  
Estimation Method ◽  
Value Theory ◽  
Extreme Value ◽  
Monitoring Data ◽  
Daily Maximum ◽  
Traffic Data ◽  
Cable Force ◽  
Maintenance Decisions ◽  
Cable Forces ◽  
Cable Stayed Bridges

For long-span cable-stayed bridges, cables serve as one of the most important components to guarantee structural integrity. Forces of stay cables indicate not only the performance of cables themselves but also the overall condition of bridges. In order to help stakeholders to make maintenance decisions, an extreme cable force estimation method was proposed based on cable force measurements and traffic data from the weighing system. First, raw monitoring data were preprocessed based on a median filtering to obtain usable cable force signals. The multiresolution wavelet method was used to extract traffic-induced force component from mixed signals. Then, a Monte Carlo-based random vehicle model was developed using traffic data from the weighing system. Based on field temperature measurements and simulation of traffic-induced effects, extreme cable forces with respect to vehicle loads and temperature effects were predicted by extreme value theory. The Generalized Pareto Distribution (GPD) was adopted to establish the probability distribution models of the daily maximum cable force. Then, the extreme value within a return period of 100 years was determined and compared with the design loading demand. Finally, the effectiveness of the proposed method was validated through a cable-stayed bridge in China. As a result, the low-frequency varying component of cable force response had positive correlation with environmental temperatures, and the extreme value of the predicted cable force under prospective traffic volumes was within limit interval value according to the design code. The conclusions can be utilized by bridge owners to make maintenance decisions.

scholarly journals Estimation of the Extreme Distribution Model of Economic Losses Due to Outbreaks Using the POT Method with Newton Raphson Iteration

2021 ◽  
Vol 2 (1) ◽  
pp. 37-45
Author(s):  
Riza Adrian Ibrahim ◽  
Sukono Sukono ◽  
Riaman Riaman
Keyword(s):  
Estimation Method ◽  
Random Variable ◽  
Value Theory ◽  
Extreme Value ◽  
Distribution Model ◽  
Economic Losses ◽  
Extreme Distribution ◽  
Newton Raphson ◽  
The Government ◽  
Raphson Iteration

Extreme distribution is the distribution of a random variable that focuses on determining the probability of small values in the tail areaof the distribution. This distribution is widely used in various fields, one of which is reinsurance. An outbreak catastrophe is non-natural disaster that can pose an extreme risk of economic loss to a country that is exposed to it. To anticipate this risk, the government of a country can insure it to a reinsurance company which is then linkedto bonds in the capital market so that new securities are issued, namely outbreakcatastrophe bonds. In pricing, knowledge of the extreme distribution of economic losses due to outbreak catastrophe is indispensable. Therefore, this study aims to determine the extreme distribution model of economic losses due to outbreak catastrophe whose models will be determined by the approaches and methods of Extreme Value Theory and Peaks Over Threshold, respectively. The threshold value parameter of the model will be estimated by Kurtosis Method, while the other parameters will be estimated with Maximum Likelihood Estimation Method based on Newton-Raphson Iteration. The result of the research obtained is the resulting model of extreme value distribution of economic losses due to outbreak catastrophe that can be used by reinsurance companies as a tool in determining the value of risk in the outbreak catastrophe bonds.

Monitored structural behavior of a long span cable-stayed bridge under environmental effects

2018 ◽  
Vol 4 (4) ◽  
pp. 137 ◽  
Author(s):  
Alemdar Bayraktar ◽  
Ashraf Ashour ◽  
Halil Karadeniz ◽  
Altok Kurşun ◽  
Arif Erdiş
Keyword(s):  
Air Temperature ◽  
Environmental Effects ◽  
Monitoring Data ◽  
Structural Behavior ◽  
Steel Cable ◽  
Daily Monitoring ◽  
Cable Stayed Bridge ◽  
Long Span ◽  
Cable Forces ◽  
Cable Stayed Bridges

An accurate numerical analysis of the behavior of long-span cable-stayed bridges under environmental effects is a challenge because of complex, uncertain and varying environmental meteorology. This study aims to investigate in-situ experimental structural behavior of long-span steel cable-stayed bridges under environmental effects such as air temperature and wind using the monitoring data. Nissibi cable-stayed bridge with total length of 610m constructed in the city of Adıyaman, Turkey, in 2015 is chosen for this purpose. Structural behaviors of the main structural elements including deck, towers (pylons) and cables of the selected long span cable-stayed bridge under environmental effects such as air temperature and wind are investigated by using daily monitoring data. The daily variations of cable forces, cable accelerations, pylon accelerations and deck accelerations with air temperature and wind speed are compared using the hottest summer (July 31, 2015) and the coldest winter (January 1, 2016) days data.

scholarly journals The exact extreme value distribution – applied study

2017 ◽  
Vol 5 (2) ◽  
pp. 87
Author(s):  
Aisha Fayomi ◽  
Neamat Qutb ◽  
Ohoud Al-Beladi
Keyword(s):  
Estimation Method ◽  
Simulated Data ◽  
Daily Rainfall ◽  
Extreme Value Distribution ◽  
Real Data ◽  
Value Theory ◽  
Extreme Value ◽  
Data Set ◽  
Applied Study ◽  
Extreme Value Model

Extreme value theory is used to develop models for describing the distribution of extreme events. Exact extreme value or compound distri-bution which is based on the theory of the maximum of random variables of random numbers is one of the most important models that are applicable in various situations, for instance of interest, it uses partial duration series (PDF) data to analyze extreme hydrological. As part of our earlier study, the parameters of this model were estimated by two methods, maximum likelihood (ML) and Bayesian- based on non-informative and informative priors. Moreover, a comparative study using simulated data showed that the Bayesian based on informative prior is the best estimation method. In this paper, a real data set taken from records of the largest daily rainfall data of Jeddah city in Saudi Arabia is used to fit the model when the parameters are estimated by Bayesian method. A comparative applied study indicates that the exact extreme value model under Bayesian estimates (BE) of its parameters provides appropriate fit for this data set and it is more applicable than the same model when the parameters are estimated by ML method and other three classical extreme value models.

scholarly journals Estimation of stochastic traffic capacity using extreme value theory and censoring: A case study in Salem, New Hampshire

2018 ◽  
Vol 4 (48) ◽  
pp. 61-75
Author(s):  
Eric M. LAFLAMME ◽  
Paul J. OSSENBRUGGEN
Keyword(s):  
Survival Analysis ◽  
New Hampshire ◽  
Extreme Value Theory ◽  
Maximum Flow ◽  
Value Theory ◽  
Extreme Value ◽  
Small Sample ◽  
Daily Maximum ◽  
Stream Data ◽  
Validation Set

In this work, we introduce a method of estimating stochastic freeway capacity using elements of both extreme value theory and survival analysis. First, we define capacity data, or estimates of the capacity of the roadway, as the daily maximum flow values. Then, under a survival analysis premise, we introduce censoring into our definition. That is, on days when flows are sufficiently high and congestion occurs, corresponding flow maxima are considered true estimates of capacity; otherwise, for those days that do not observe high flows or congestion, flow maxima are deemed censored observations and capacities must be higher than the observations. By extreme value theory, the collection of flow maxima (block maxima) can be appropriately approximated with a generalized extreme value (GEV) distribution. Because of small sample sizes and the presence of censoring, a Bayesian framework is pursued for model fitting and parameter estimation. To lend credence to our proposed methodology, the procedure is applied to real-world traffic stream data collected by the New Hampshire Department of Transportation (NHDOT) at a busy location on Interstate I-93 near Salem, New Hampshire. Data were collected over a period of 11 months and raw data were aggregated into 15-minute intervals. To assess our procedure, and to provide proof of concept, several validation procedures are presented. First, using distinct training and validation subsets of our data, the procedure yields accurate predictions of highway capacity. Next, our procedure is applied to a training set to yield random capacities which are then used to predict breakdown in the validation set. The frequency of these predicted breakdowns is found to be statistically similar to observed breakdowns observed in our validation set. Lastly, after comparing our methodology to other methods of stochastic capacity estimation, we find our procedure to be highly successful.

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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