scholarly journals Probabilistic Assessment of Degree of Bending in Tubular X-Joints of Offshore Structures Subjected to Bending Loads

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Hamid Ahmadi ◽  
Amirreza Ghaffari
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Accurate Determination ◽  
Offshore Structures ◽  
Fatigue Reliability ◽  
Distribution Models ◽  
Estimated Parameters ◽  
Jacket Structures ◽  
Kolmogorov Smirnov ◽  
Bending Loads

Fatigue life of tubular joints in offshore structures is significantly influenced by the degree of bending (DoB). The DoB exhibits considerable scatter calling for greater emphasis in accurate determination of its governing probability distribution which is a key input for the fatigue reliability analysis of a tubular joint. Although the tubular X-joints are commonly found in offshore jacket structures, as far as the authors are aware, no comprehensive research has been carried out on the probability distribution of the DoB in tubular X-joints. In the present paper, results of parametric equations available for the calculation of the DoB have been used to develop probability distribution models for the DoB in the chord member of tubular X-joints subjected to four types of bending loads. Based on a parametric study, a set of samples was prepared and density histograms were generated for these samples using Freedman-Diaconis method. Twelve different probability density functions (PDFs) were fitted to these histograms. In each case, Kolmogorov-Smirnov test was used to evaluate the goodness of fit. Finally, after substituting the values of estimated parameters for each distribution, a set of fully defined PDFs have been proposed for the DoB in tubular X-joints subjected to bending loads.

scholarly journals Best Fitted Distribution For Meteorological Data In Kuala Krai

2021 ◽  
Vol 3 (1) ◽  
pp. 16-25
Author(s):  
Siti Mariam Norrulashikin ◽  
Fadhilah Yusof ◽  
Siti Rohani Mohd Nor ◽  
Nur Arina Bazilah Kamisan
Keyword(s):  
Climate Change ◽  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Meteorological Data ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Data Series ◽  
Distribution Models ◽  
Goodness Of Fit Tests

Modeling meteorological variables is a vital aspect of climate change studies. Awareness of the frequency and magnitude of climate change is a critical concern for mitigating the risks associated with climate change. Probability distribution models are valuable tools for a frequency study of climate variables since it measures how the probability distribution able to fit well in the data series. Monthly meteorological data including average temperature, wind speed, and rainfall were analyzed in order to determine the most suited probability distribution model for Kuala Krai district. The probability distributions that were used in the analysis were Beta, Burr, Gamma, Lognormal, and Weibull distributions. To estimate the parameters for each distribution, the maximum likelihood estimate (MLE) was employed. Goodness-of-fit tests such as the Kolmogorov-Smirnov, and Anderson-Darling tests were conducted to assess the best suited model, and the test's reliability. Results from statistical studies indicate that Burr distributions better characterize the meteorological data of our research. The graph of probability density function, cumulative distribution function as well as Q-Q plot are presented.

scholarly journals Flood Flow Probability Distribution Model Selection on Niger/Benue River Basins in Nigeria

2021 ◽  
pp. 76-94
Author(s):  
Itolima Ologhadien
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
River Basins ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Distribution Model ◽  
Distribution Models ◽  
Type Iii ◽  
Pearson Type ◽  
Best Fit

Flood frequency analysis is a crucial component of flood risk management which seeks to establish a quantile relationship between peak discharges and their exceedance (or non-exceedance) probabilities, for planning, design and management of infrastructure in river basins. This paper evaluates the performance of five probability distribution models using the method of moments for parameter estimation, with five GoF-tests and Q-Q plots for selection of best –fit- distribution. The probability distributions models employed are; Gumbel (EV1), 2-parameter lognormal (LN2), log Pearson type III (LP3), Pearson type III(PR3), and Generalised Extreme Value( GEV). The five statistical goodness – of – fit tests, namely; modified index of agreement (Dmod), relative root mean square error (RRMSE), Nash – Sutcliffe efficiency (NSE), Percent bias (PBIAS), ratio of RMSE and standard deviation of the measurement (RSR) were used to identify the most suitable distribution models. The study was conducted using annual maximum series of nine gauge stations in both Benue and Niger River Basins in Nigeria. The study reveals that GEV was the best – fit distribution in six gauging stations, LP3 was best – fit distribution in two gauging stations, and PR3 is best- fit distribution in one gauging station. This study has provided a significant contribution to knowledge in the choice of distribution models for predicting extreme hydrological events for design of water infrastructure in Nigeria. It is recommended that GEV, PR3 and LP3 should be considered in the development of regional flood frequency using the existing hydrological map of Nigeria.

A Fast Calculation Procedure for Fatigue Reliability Estimates of Offshore Structures

2003 ◽  
Author(s):  
H. Karadeniz
Keyword(s):  
Spectral Analysis ◽  
Probability Distribution ◽  
Offshore Structures ◽  
Inertia Force ◽  
Stress Process ◽  
Fatigue Reliability ◽  
Uncertainty Modelling ◽  
Reliability Estimates ◽  
Reliability Method ◽  
Stress Spectrum

This paper presents formulations and procedure of a fast and efficient computation of fatigue reliability estimates of offshore structures, which eliminates repetitive execution of spectral analysis procedure so that it is performed only once for all reliability iterations. This is archived by a suitable uncertainty modelling and spectral formulation of the stress process. For this purpose, a new uncertainty variable is defined to represent all uncertainties in the stress spectrum, except those in the damping and inertia force coefficients, thicknesses of marine growths and structural members, which are represented by their own uncertainty variables. Apart from uncertainties in the stress spectrum, a detailed modelling of the fatigue-related uncertainties is presented. Uncertainties in SCF, damage model (S-N line), analytical modelling of the probability distribution of non-narrow banded stress process, long-term probability distribution of sea states and in the reference damage at which failure occurs, are all considered in the group of fatigue-related uncertainties. Formulation of the stress spectrum and stress spectral moments is presented explicitly in the idealized uncertainty space. Then, the failure function of the reliability analysis is expressed in terms of uncertainty variables as being independent of the spectral analysis. The advanced FORM reliability method is used to calculate the reliability index and to identify important uncertainty origins. The procedure presented in the paper is demonstrated by an example jacket type structure and the results are compared with previously calculated results using more sophisticated uncertainty modelling of the stress spectrum.

scholarly journals Goodness-of-Fit of Reference Evapotranspiration to Gamma Probability Distribution

2021 ◽  
Vol 13 (6) ◽  
pp. 70
Author(s):  
Janilson Pinheiro de Assis ◽  
Roberto Pequeno de Sousa ◽  
Isaac Reinaldo Pinheiro de Lima ◽  
Paulo César Ferreira Linhares ◽  
Walter Rodrigues Martins ◽  
...  
Keyword(s):  
Probability Distribution ◽  
Gamma Distribution ◽  
Goodness Of Fit ◽  
Reference Evapotranspiration ◽  
Chi Square ◽  
Confidence Levels ◽  
Use Of Data ◽  
Maximum Benefit ◽  
Kolmogorov Smirnov ◽  
Data Period

This paper aims to estimate, using the Penman-Monteith method, the probabilities of reference evapotranspiration (ET0) in millimeters, as well as their accumulated values for ten days (decendial), in Mossoró, northeast Brazil. The Meteorological Station of the Federal Rural University of Semi-Arid (UFERSA) provided the daily records of evapotranspiration. The construction of tables based on the approximation of the variable to the Gamma distribution allows the use of data without transformations. The probabilities were estimated with the Gamma distribution at confidence levels of 1% to 95% over the 1970-2007 data period. The results of the chi-square and Kolmogorov-Smirnov tests at 10% probability (p ≥ 0.10) demonstrated the adequacy of the table construction process, providing essential support in the planning of agricultural activities in the region to obtain the maximum benefit from evapotranspiration data. The Gamma probability distribution best described the ET0 for scaling irrigation systems in the county. The maximum daily ET0 for irrigation projects in the Mossoró region is 10 mm, and the cumulative 10-day ET0 averages 80 mm.

scholarly journals Probability Distribution of Rainfall in Medan

2019 ◽  
Vol 1 (2) ◽  
pp. 43-49 ◽  
Author(s):  
Elly Rosmaini
Keyword(s):  
Probability Distribution ◽  
Gamma Distribution ◽  
Goodness Of Fit ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Goodness Of Fit Test ◽  
Normal Distributions ◽  
Lognormal Distributions ◽  
Kolmogorov Smirnov ◽  
Anderson Darling

In this paper we chose three stations in Medan City , Indonesia to estimate Monthly Rainfall Data i.e. Tuntungan, Tanjung Selamat, and Medan Selayang Stations. We took the data from 2007 to 2016. In this case fitted with Normal, Gamma, and Lognormal Distributions. To estimate parameters, we used this method. Furthermore, Kolmogorov-Smirnov and Anderson Darling tests were used the goodness-of-fit test. The Gamma and Normal Distributions is suitable for Tuntungan and Medan Selayang Stations were stated by Kolmogorov-Smirnov's test. Anderson Darling's test stated that Gamma Distribution was suitable for all stations.

scholarly journals Comparative Evaluation of Probability Distribution Models of Flood flow in Lower Niger Basin

2021 ◽  
Vol 6 (2) ◽  
pp. 107-117
Author(s):  
Itolima Ologhadien
Keyword(s):  
Water Resources ◽  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Distribution Model ◽  
Distribution Models ◽  
Optimum Distribution ◽  
Water Resources Systems ◽  
Type Iii ◽  
Pearson Type

The choice of optimum probability distribution model that would accurately simulate flood discharges at a particular location or region has remained a challenging problem to water resources engineers. In practice, several probability distributions are evaluated, and the optimum distribution is then used to establish the quantile - probability relationship for planning, design and management of water resources systems, risk assessment in flood plains and flood insurance. This paper presents the evaluation of five probability distributions models: Gumbel (EV1), 2-parameter lognormal (LN2), log pearson type III (LP3), Pearson type III(PR3), and Generalised Extreme Value (GEV) using the method of moments (MoM) for parameter estimation and annual maximum series of five hydrological stations in the lower Niger River Basin in Nigeria. The choice of optimum probability distribution model was made on five statistical goodness – of – fit measures; modified index of agreement (Dmod), relative root mean square error (RRMSE), Nash – Sutcliffe efficiency (NSE), Percent bias (PBIAS), ratio of RMSE and standard deviation of the measurement (RSR), and probability plot correlation coefficient (PPCC). The results show that GEV is the optimum distribution in 3 stations, and LP3 in 2 stations. On the overall GEV is the best – fit distribution, seconded by PR3 and thirdly, LP3. Furthermore, GEV simulated discharges were in closest agreement with the observed flood discharges. It is recommended that GEV, PR3 and LP3 should be considered in the final selection of optimum probability distribution model in Nigeria.

scholarly journals Application of Harmony Search to Design Storm Estimation from Probability Distribution Models

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Sukmin Yoon ◽  
Changsam Jeong ◽  
Taesam Lee
Keyword(s):  
Parameter Estimation ◽  
Probability Distribution ◽  
Harmony Search ◽  
Computation Time ◽  
The Other ◽  
Distribution Model ◽  
Distribution Models ◽  
Design Storm ◽  
Estimated Parameters

The precision of design storm estimation depends on the selection of an appropriate probability distribution model (PDM) and parameter estimation techniques. Generally, estimated parameters for PDMs are provided based on the method of moments, probability weighted moments, and maximum likelihood (ML). The results using ML are more reliable than the other methods. However, the ML is more laborious than the other methods because an iterative numerical solution must be used. In the meantime, metaheuristic approaches have been developed to solve various engineering problems. A number of studies focus on using metaheuristic approaches for estimation of hydrometeorological variables. Applied metaheuristic approaches offer reliable solutions but use more computation time than derivative-based methods. Therefore, the purpose of the current study is to enhance parameter estimation of PDMs for design storms using a recently developed metaheuristic approach known as a harmony search (HS). The HS is compared to the genetic algorithm (GA) and ML via simulation and case study. The results of this study suggested that the performance of the GA and HS was similar and showed more accurate results than that of the ML. Furthermore, the HS required less computation time than the GA.

AN ALTERNATIVE TEST FOR UNIFORMITY

2009 ◽  
Vol 16 (04) ◽  
pp. 343-356 ◽  
Author(s):  
ZHENMIN CHEN ◽  
CHUNMIAO YE
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Integral Transformation ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Underlying Distribution ◽  
Goodness Of Fit Tests ◽  
Kolmogorov Smirnov ◽  
Alternative Test ◽  
Smirnov Test

Improving power of goodness-of-fit tests is an important research topic in statistics. The goal of the goodness-of-fit test is to check whether the underlying probability distribution, from which a sample is drawn, differs from a hypothesized distribution. Numerous research papers have been published in this area. It has been shown that the power of the existing goodness-of-fit tests in the literature is unsatisfactory when the alternative distributions are of V-shape or when the sample sizes are small. This motivates the development of more powerful test statistics. In this research, a new test statistic is proposed. The result can be used to test whether the underlying probability distribution differs from a uniform distribution. By applying the probability integral transformation, the proposed test statistic can be used to check whether the underlying distribution differs from any hypothesized distribution. The performance of the method proposed in this research is compared with the Kolmogorov–Smirnov test, which is a widely adopted statistical test in the literature. It has been shown that the test proposed in this proposal is more powerful than the Kolmogorov–Smirnov test in some cases.

scholarly journals Modelling Rainfall Intensity by Optimization Technique in Abeokuta, South-West, Nigeria

2019 ◽  
pp. 1-10
Author(s):  
A. O. David ◽  
Ify L. Nwaogazie ◽  
J. C. Agunwamba
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Rainfall Intensity ◽  
Mean Squared Error ◽  
Optimization Technique ◽  
Distribution Models ◽  
Goodness Of Fit Test ◽  
Maximum Rainfall ◽  
Pearson Type ◽  
Type 3

The design of water resources engineering control structures is best achieved with adequate estimation of rainfall intensity over a particular catchment. To develop the rainfall intensity, duration and frequency (IDF) models, 25 year daily rainfall data were collected from Nigerian Meteorological Agency (NIMET) Abuja for Abeokuta. The annual maximum rainfall amounts with durations of 5, 10, 15, 20, 30, 45, 60, 90, 120, 180, 240, 300 and 420 minutes were extracted and subjected to frequency analysis using the Excel Optimization Solver wizard. Specific and general IDF models were developed for return periods of 2, 5, 10, 25, 50 and 100 years using the Gumbel Extreme Value Type -1 and Log Pearson Type -3 distributions. The Anderson-Darling goodness of fit test was used to ascertain the best fit probability distribution. The R2 values range from 0.973 – 0.993 and the Mean Squared Error, MSE from 84.49 – 134.56 for the Gumbel and 0.964 – 0.997 with MSE of 42.88 – 118.68 for Log Pearson Type -3 distribution, respectively. The probability distribution models are recommended for the prediction of rainfall intensities for Abeokuta metropolis.

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