Sensitivity Analysis of Dam Breach Parameters for Variation Capacity Earthen Dams

2021 ◽  
Vol 18 (3) ◽  
pp. 1-9
Author(s):  
Chau Kim Tran
Keyword(s):  
Sensitivity Analysis ◽  
Development Time ◽  
Failure Process ◽  
Parameter Analysis ◽  
Earth Dam ◽  
Dam Failure ◽  
Fast Method ◽  
Input Variables ◽  
Rising Time ◽  
Breach Width

Sensitivity analysis is an effective tool to determine the robustness of an assessment by examining the extent to which the results are affected by changes in input. In this study, the FAST method was applied to analyse the sensitivity to the earth dam failure process. Four (04) input variables were selected including breach development time, breach width, side slope, and initial breach position. The effects of these parameters on the two (02) outputs i.e., the maximum outflow, and rising time were assessed. The study was applied to 08 reservoirs with different capacities. The sensitivity analysis showed that the development time and initial breach location dominantly affect these outputs. Additionally, development time is the most important factor in rising time. The lateral slope has an insignificant effect on outputs. The effect of breach width can be neglected to rising time, however, its influence on maximum outflow is significant. The results of this study show the role of input variables in the flow hydrograph due to dam failure. Through this research, the workload of the breach parameter analysis process can be substantially reduced.

scholarly journals A Novel Global Sensitivity Measure Based on Probability Weighted Moments

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 90
Author(s):  
Shufang Song ◽  
Lu Wang
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Small Samples ◽  
Double Loop ◽  
Numerical Estimation ◽  
Global Sensitivity ◽  
Sensitivity Measure ◽  
Probability Weighted Moments ◽  
Input Variables ◽  
Single Set

Global sensitivity analysis (GSA) is a useful tool to evaluate the influence of input variables in the whole distribution range. Variance-based methods and moment-independent methods are widely studied and popular GSA techniques despite their several shortcomings. Since probability weighted moments (PWMs) include more information than classical moments and can be accurately estimated from small samples, a novel global sensitivity measure based on PWMs is proposed. Then, two methods are introduced to estimate the proposed measure, i.e., double-loop-repeated-set numerical estimation and double-loop-single-set numerical estimation. Several numerical and engineering examples are used to show its advantages.

scholarly journals Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection

2017 ◽  
Vol 20 (2) ◽  
pp. 520-532 ◽  
Author(s):  
A. B. Dariane ◽  
Sh. Azimi
Keyword(s):  
Wavelet Transform ◽  
Multistep Method ◽  
Test Period ◽  
Fast Method ◽  
Streamflow Forecasting ◽  
Input Selection ◽  
Fuzzy Models ◽  
Monthly Streamflow ◽  
Input Variables ◽  
Learning Machine

Abstract In this paper the performance of extreme learning machine (ELM) training method of radial basis function artificial neural network (RBF-ANN) is evaluated using monthly hydrological data from Ajichai Basin. ELM is a newly introduced fast method and here we show a novel application of this method in monthly streamflow forecasting. ELM may not work well for a large number of input variables. Therefore, an input selection is applied to overcome this problem. The Nash–Sutcliffe efficiency (NSE) of ANN trained by backpropagation (BP) and ELM algorithm using initial input selection was found to be 0.66 and 0.72, respectively, for the test period. However, when wavelet transform, and then genetic algorithm (GA)-based input selection are applied, the test NSE increase to 0.76 and 0.86, respectively, for ANN-BP and ANN-ELM. Similarly, using singular spectral analysis (SSA) instead, the coefficients are found to be 0.88 and 0.90, respectively, for the test period. These results show the importance of input selection and superiority of ELM and SSA over BP and wavelet transform. Finally, a proposed multistep method shows an outstanding NSE value of 0.97, which is near perfect and well above the performance of the previous methods.

Budget Impact Analysis of Rapid Screening for Staphylococcus aureus Colonization Among Patients Undergoing Elective Surgery in US Hospitals

2008 ◽  
Vol 29 (1) ◽  
pp. 16-24 ◽  
Author(s):  
Gary A. Noskin ◽  
Robert J. Rubin ◽  
Jerome J. Schentag ◽  
Jan Kluytmans ◽  
Edwin C. Hedblom ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Sensitivity Analysis ◽  
Budget Impact ◽  
Elective Surgery ◽  
Cost Savings ◽  
Nasal Carriage ◽  
Rapid Screening ◽  
Marginal Effect ◽  
Primary Input ◽  
Input Variables

Objective.To evaluate the economic impact of performing rapid testing for Staphylococcus aureus colonization before admission for all inpatients who are scheduled to undergo elective surgery and providing subsequent decolonization therapy for those patients found to be colonized with S. aureus.Methods.A budget impact model that used probabilistic sensitivity analysis to account for the uncertainties in the input variables was developed. Primary input variables included the marginal effect of S. aureus infection on patient outcomes among patients who underwent elective surgery, patient demographic characteristics, the prevalence of nasal carriage of S. aureus, the sensitivity and specificity of the rapid diagnostic test for S. aureus colonization, the efficacy of decolonization therapy for nasal carriage of S. aureus, and cost data. Data sources for the input variables included the 2003 Nationwide Inpatient Sample data and the published literature.Results.In 2003, there were an estimated 7,181,484 patients admitted to US hospitals for elective surgery. Our analysis indicated preadmission testing and subsequent decolonization therapy for patients colonized with S. aureus would have produced a mean annual cost savings to US hospitals of $231,538,400 (95% confidence interval [CI], -$300 million to $1.3 billion). The mean annual number of hospital-days that could have been eliminated was estimated at 364,919 days (95% CI, 67,893-926,983 days), and a mean of 935 in-hospital deaths (95% CI, 88-3,691) could have been avoided per year. Sensitivity analysis indicated a 64.5% probability that there would be cost savings to US hospitals as a result of preadmission testing and subsequent decolonization therapy.Conclusion.The addition of preadmission testing and decolonization therapy to standard care would result in significant cost savings, even after accounting for variations in the model input values.

scholarly journals Failure of Zoned Earth Dam (An analysis of earth dam break)

2019 ◽  
Vol 27 (1) ◽  
pp. 344-353
Author(s):  
Abdul-Hassan K. Al-Shukur ◽  
Ranya Badea’ Mahmoud
Keyword(s):  
Water Level ◽  
Factor Of Safety ◽  
Limit Equilibrium ◽  
Water Flux ◽  
Dam Break ◽  
The Body ◽  
Earth Dam ◽  
Dam Failure ◽  
Maximum Water Level ◽  
Maximum Water

One of the most common type of embankment dam failure is the dam-break due to overtopping. In this study, the finite elements method has been used to analyze seepage and limit equilibrium method to study stability of the body of an earthfill dam during the flood condition. For this purpose, the software Geostudio 2012 is used through its subprograms SEEP/W and SLOPE/W. Al-Adhaim dam in Iraq has been chosen to analysis the 5 days of flood. It was found that the water flux of seepage during the flood reaches about 8.772*10-5. m3/sec when the water level 146.5 m at 2nd day. Seepage through the embankment at maximum water level increased by 55.1 % from maximum water level. It was concluded that the factor of safety against sliding in downstream side decrease with increasing water level and vice versa. It was also concluded that the deposits are getting more critical stability during the conditions of flood when the factor of safety value reaches 1.219 at 2nd day.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

Bayesian and Sensitivity Analysis Approaches to Modelling the Direct Solar Irradiance

Solar Energy ◽  
2005 ◽  
Author(s):  
Philippe Lauret ◽  
Mathieu David ◽  
Eric Fock ◽  
Laetitia Adelard
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Solar Irradiance ◽  
Second Step ◽  
Practical Implementation ◽  
Bayesian Techniques ◽  
Systematic Methodology ◽  
Important Input ◽  
Input Variables ◽  
Selection Of

In this paper, emphasis is put on the design of a neural network to model the direct solar irradiance. Since unfortunately a neural network (NN) is not a statistician in-a-box, building a NN for a particular problem is a non trivial task. As a consequence, we argue that in order to properly model the direct solar irradiance, a systematic methodology must be employed. For this purpose, we propose a two-step approach to building the NN model. The first step deals with a probabilistic interpretation of the NN learning by using Bayesian techniques. The Bayesian approach to modelling offers significant advantages over the classical NN learning process. Among others, one can cite a) automatic complexity control of the NN using all the available data b) selection of the most important input variables. The second step consists in using a new sensitivity analysis-based pruning method in order to infer the optimal NN structure. We show that the combination of the two approaches makes the practical implementation of the Bayesian techniques more reliable.

scholarly journals Use of the correlation between input variables in estimating the risk of feedlot finishing of steers and young steers

2014 ◽  
Vol 86 (2) ◽  
pp. 945-954 ◽  
Author(s):  
PAULO S. PACHECO ◽  
JOÃO RESTLE ◽  
LEONIR L. PASCOAL ◽  
FABIANO N. VAZ ◽  
RICARDO Z. VAZ ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Stochastic Dominance ◽  
Net Present Value ◽  
Latin Hypercube Sampling ◽  
Risk Estimate ◽  
Present Value ◽  
Random Input ◽  
Financial Indicator ◽  
Input Variables ◽  
Diet Intake

The objective of this study was to evaluate the risk of feedlot finishing of steers (22.8 months) and young steers (15.2 months), using or not a correlation between the random input variables (data collected from 2004 to 2010) in the simulation of the Net Present Value (NPV) financial indicator. The animals were fed a diet containing roughage:concentrate ratio of 60:40 for 34 and 143 days, respectively, until they had reached a predetermined slaughter weight of 430 kg. For the NPV simulation, Latin Hypercube sampling was used, with 2000 interactions. The stochastic dominance analysis, test of differences between pairs of curves of cumulative distributions and sensitivity analysis were carried out. The NPV simulation using the correlation resulted in the best option for risk estimate. The confinement of young steers was the alternative of investment most viable than confinement of steers (NPV ≥ 0 of 80.4 vs. 62.3% in the simulation with correlation, respectively). Sensitivity analysis determined the following items had the greatest impact on the estimate of NPV: prices of fat and thin cattle, initial and final weights, diet costs, minimum rate of attractiveness and diet intake.

Spill alert device for earth dam failure warning

1984 ◽  
Vol 9 (3) ◽  
pp. 373-380
Author(s):  
Robert M. Koerner ◽  
Arthur E. Lord
Keyword(s):  
Earth Dam ◽  
Dam Failure

scholarly journals Application of Effective Stress Model to Analysis of Liquefaction and Seismic Performance of an Earth Dam in China

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Changqing Qi ◽  
Wei Lu ◽  
Jimin Wu ◽  
Xing Liu
Keyword(s):  
Pore Pressure ◽  
Seismic Performance ◽  
Effective Stress ◽  
Earth Dam ◽  
Dam Failure ◽  
Excess Pore Pressure ◽  
Stress Model ◽  
Gravelly Soil ◽  
Excess Pore

Earthquake-induced liquefaction is one of the major causes of catastrophic earth dam failure. In order to assess the liquefaction potential and analyze the seismic performance of an earth dam in Fujian, Southeastern China, the in situ shear wave velocity test was firstly carried out. Results indicate that the gravelly filling is a type of liquefiable soil at present seismic setting. Then the effective stress model was adopted to thoroughly simulate the response of the soil to a proposed earthquake. Numerical result generally coincides with that of the empirical judgment based on in situ test. Negative excess pore pressure developed in the upper part of the saturated gravelly filling and positive excess pore pressure developed in the lower part. The excess pore pressure ratio increases with depth until it reaches a maximum value of 0.45. The displacement of the saturated gravelly soil is relatively small and tolerable. Results show that the saturated gravelly filling cannot reach a fully liquefied state. The dam is overall stable under the proposed earthquake.

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