Possible Regional Probability Distribution Type of Canadian Annual Streamflow by L-moments

2004 ◽  
Vol 18 (5) ◽  
pp. 425-438 ◽  
Author(s):  
Sheng Yue ◽  
Chun Yuan Wang
Keyword(s):  
Probability Distribution ◽  
Annual Streamflow ◽  
L Moments ◽  
Distribution Type

Probability Model of Corrosion Initiation Time of Steel in Concrete Structure in Chloride Environment

2011 ◽  
Vol 243-249 ◽  
pp. 5632-5636
Author(s):  
Ya Li Ma ◽  
Ai Lin Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Probability Distribution ◽  
Probability Model ◽  
Chloride Concentration ◽  
Initiation Time ◽  
Distribution Law ◽  
Corrosion Initiation ◽  
Corrosion Initiation Time ◽  
Chloride Environment ◽  
Distribution Type

Probability distribution law of corrosion initiation time of steel in concrete under chloride environment is discussed. Based on the Fick’s second law, by Monte Carlo, frequency distribution, distribution type and probability density is analyzed. The statistic parameters of the factors influencing the probability distribution of corrosion initiation time are studied and the expression for sensitivity analysis of corrosion initiation time is deduced. By sensitivity analysis can know, corrosion initiation time is found to be more sensitive to cover than the diffusion coefficient, and more sensitive to surface chloride concentration than the critical chloride level. The analysis of the paper perfects the methods of predicting the corrosion initiation time.

Predicting the critical path changes using sensitivity analysis: a delay analysis approach

2020 ◽  
Vol 13 (5) ◽  
pp. 1097-1119
Author(s):  
Mohammed Hammad ◽  
Alireza Abbasi ◽  
Ripon K. Chakrabortty ◽  
Michael J. Ryan
Keyword(s):  
Sensitivity Analysis ◽  
Probability Distribution ◽  
Critical Path ◽  
Research Work ◽  
Delay Analysis ◽  
Content Type ◽  
Project Completion ◽  
Analysis Scheme ◽  
Completion Date ◽  
Distribution Type

PurposeThis research presents a framework that allows project managers to predict the next critical paths (CP(s)) and to take extra care when planning and executing those activities that have the potential to cause changes in a project's current CP(s).Design/methodology/approachThe method presented here is based on an assessment of each activity's contribution to the overall schedule variance, which involves assigning a probability distribution function to each activity duration in the project. A sensitivity analysis is also carried out, which forms the basis of identifying which activity most affects the project completion date and therefore will have the greatest effect in changing the CP.FindingsThe authors’ analysis reveals that the most appropriate probability density function (PDF) for the targeted project is the normal distribution. However, the aim of this work is not to determine the most suitable distribution for each activity but rather to study the effect of the activity distribution type on the CP prediction. The results show that the selection of the appropriate probability distribution is very important, since it can impact the CP prediction and estimated project completion date.Originality/valueThis research work proposes a delay analysis scheme which can help the project manager to predict the next CP and to improve performance by identifying which activity is the bottleneck. On the other hand, the simplicity arises from the fact that this method does not require any expensive machines or software to generate results.

scholarly journals Performance of the probability distribution models applied to heavy rainfall daily events

2014 ◽  
Vol 38 (4) ◽  
pp. 335-342 ◽  
Author(s):  
Rosângela Francisca de Paula Vitor Marques ◽  
Carlos Rogério de Mello ◽  
Antônio Marciano da Silva ◽  
Camila Silva Franco ◽  
Alisson Souza de Oliveira
Keyword(s):  
Probability Distribution ◽  
Heavy Rainfall ◽  
Daily Rainfall ◽  
Distribution Model ◽  
Distribution Models ◽  
Rain Gauges ◽  
Daily Events ◽  
Chi Squared ◽  
L Moments ◽  
Erosive Rainfall

Probabilistic studies of hydrological variables, such as heavy rainfall daily events, constitute an important tool to support the planning and management of water resources, especially for the design of hydraulic structures and erosive rainfall potential. In this context, we aimed to analyze the performance of three probability distribution models (GEV, Gumbel and Gamma two parameter), whose parameters were adjusted by the Moments Method (MM), Maximum Likelihood (ML) and L - Moments (LM). These models were adjusted to the frequencies from long-term of maximum daily rainfall of 8 rain gauges located in Minas Gerais state. To indicate and discuss the performance of the probability distribution models, it was applied, firstly, the non-parametric Filliben test, and in addition, when differences were unidentified, Anderson-Darlling and Chi-Squared tests were also applied. The Gumbel probability distribution model showed a better adjustment for 87.5% of the cases. Among the assessed probability distribution models, GEV fitted by LM method has been adequate for all studied rain gauges and can be recommended. Considering the number of adequate cases, MM and LM methods had better performance than ML method, presenting, respectively, 83% and 79.2% of adequate cases.

Study on Reliability Analysis of Tailings Dam Stability

2007 ◽  
Vol 353-358 ◽  
pp. 2619-2622
Author(s):  
Chao Zhang ◽  
Chun He Yang ◽  
Feng Chen
Keyword(s):  
Probability Distribution ◽  
Reliability Analysis ◽  
Limit Equilibrium ◽  
Reliability Theory ◽  
Sensitivity Analyses ◽  
Tailings Dam ◽  
Tailings Dams ◽  
Dam Stability ◽  
The Stability ◽  
Distribution Type

Since the construction method of tailings dams determines the uneven distribution of tailings, a reliability theory is introduced to analyze the stability of tailings dams. Based on the limit equilibrium method and reliability theory, the sensitivity and reliability of a typical tailings dam are analyzed. Reliability analyses with different types of the variable probability distribution types show that the effect of the probability distribution type on reliability analysis can almost be ignored. Besides, the sensitivity analyses of different variables show that the strength indexes and density of tailings will affect the analysis results of stability reliability. Therefore the strength indexes c, φ and density ρ must be considered as basic variables to analyze the stability reliability of tailings dams.

scholarly journals Investigation on the Effect of Probability Distribution on the Dynamic Response of Liquefiable Soils

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Juncheng Wang ◽  
Li Zhou ◽  
Wenzhi Song ◽  
Houle Zhang ◽  
Yongxin Wu
Keyword(s):  
Sensitivity Analysis ◽  
Probability Distribution ◽  
Shear Modulus ◽  
Significant Influence ◽  
Beta Distribution ◽  
Coefficient Of Variation ◽  
Site Investigation ◽  
Parameter Sensitivity Analysis ◽  
Scale Of Fluctuation ◽  
Distribution Type

This study investigated the effect of different probabilistic distributions (Lognormal, Gamma, and Beta) to characterize the spatial variability of shear modulus on the soil liquefiable response. The parameter sensitivity analysis included the coefficient of variation and scale of fluctuation of soil shear modulus. The results revealed that the distribution type had no significant influence on the liquefication zone. In particular, the estimation with Beta distribution is the worst scenario. It illuminated that the estimation with Beta distribution can provide a conservative design if site investigation is absent.

scholarly journals WAVELET TRANSFORM FOR MEDIUM-RANGE STREAMFLOWS PROJECTIONS IN NATIONAL INTERCONNECTED SYSTEM

2021 ◽  
pp. 1-12
Author(s):  
Carlos Eduardo Sousa Lima ◽  
Marx Vinicius Maciel da Silva ◽  
Cleiton Da Silva Silveira ◽  
Francisco Das Chagas Vasconcelos Junior
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Probability Distribution ◽  
Cumulative Probability ◽  
Annual Streamflow ◽  
Interconnected System ◽  
Projection Model ◽  
Annual Series ◽  
Medium Range ◽  
Calibration And Verification

This work aims to analyze the variability of average annual streamflow time series of the SIN (Brazil) and create a projection model of future streamflow scenarios from 3 to 10 years using wavelet transform. The streamflow time series were used divided into two periods: 1931 to 2005 and 2006 to 2017, for calibration and verification, respectively. The annual series was standardized, and by the wavelet transform, it was decomposed into two bands plus the residue for each Base Posts (BP) for later reconstruction. Then an autoregressive model per band and residue was made. The projection was obtained by adding the autoregressive models. For performance evaluation, a qualitative analysis of the cumulative probability distribution of the projected years and the likelihood were made. The model identified the probability distribution function of the projected years and obtained likelihood greater than 1 in most of the SIN regions, indicating that this methodology can capture the medium-range variability.

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