scholarly journals IMPLICATIONS ON RC STRUCTURE PERFORMANCE OF MODEL PARAMETER SENSITIVITY: EFFECT OF CHLORIDES

2010 ◽  
Vol 16 (4) ◽  
pp. 561-566 ◽  
Author(s):  
Rui Miguel Ferreira
Keyword(s):  
Sensitivity Analysis ◽  
Probabilistic Models ◽  
Current Knowledge ◽  
Concrete Cover ◽  
Model Parameters ◽  
Model Parameter ◽  
Average Value ◽  
Model Code ◽  
Chloride Migration ◽  
The Impact

This paper presents a sensitivity analysis of the parameters of the model presented in the fib – Model Code for Service Life Design for corrosion initiation due to chloride penetration. In order to adequately apply probabilistic models, a basic understanding of the model and the parameters used and the impact input values for the model parameters have on the outcome is necessary. An erroneous parameter value selection has consequences that are not always appreciated. A sensitivity analysis of the main model parameters was performed by analysing the presented model outcome. The parameter's average value was varied in a range of 20% with respect to the reference average value, whereas keeping the standard deviation constant. The results of the sensitivity analysis show that the reinforcement concrete cover and the ageing exponent of the chloride migration coefficient parameters are those that most influence the outcome of the model and therefore the durability performance of the concrete structure. The least influential model parameters, contrary to what might be expected, are Ccrit and C S,Δx. Furthermore, the importance of environmental temperature on the outcome is noted. The choice of model parameter value is a critical one. Due to the uncertainty of current knowledge with regard to certain parameters caution should be exercised in the use of these models. Santrauka Straipsnyje pristatoma sukurta modelio parametru jautrumo analize – modelio sistema projektuoja korozijos, atsirandančios del chlorido skvarbos, atsiradimo tikimybe. Siekiant tinkamai taikyti tikimybinius modelius būtinas bazinis supratimas apie modelyje naudotu parametru ir ivesties reikšmiu parametru poveikio rezultatus. Klaidingas parametro reikšmes pasirinkimas gali lemti netinkama ivertinima. Pagrindiniu modelio parametru jautrumo analize buvo atlikta tiriant pateikto pavyzdžio rezultatus. Vidutine parametro verte buvo varijuojama 20 % atsižvelgiant i vidutine atskaitos verte ir išlaikant standartinio nuokrypio konstanta. Jautrumo analizes rezultatai rodo, kad armuotos betonines dangos ir chlorido migracijos koeficiento senejimo rodiklio parametrai yra tie, kurie gali labiausiai paveikti modelio rezultatus ir betono struktūros patvarumo charakteristikas. Mažiau itakingi modelio parametrai, priešingai nei būtu galima tiketis, yra Ccrit ir C S,Δx. Aprašyta aplinkos temperatūros itaka rezultatams. Pasirinkti modelio parametru vertes yra labai svarbu. Del dabartiniu žiniu apie tam tikru parametru neapibrežtuma reikia būti atsargiems taikant šiuos modelius.

scholarly journals Considerations and Caveats when Applying Global Sensitivity Analysis Methods to Physiologically Based Pharmacokinetic Models

2020 ◽  
Vol 22 (5) ◽  
Author(s):  
Dan Liu ◽  
Linzhong Li ◽  
Amin Rostami-Hodjegan ◽  
Frederic Y. Bois ◽  
Masoud Jamei
Keyword(s):  
Sensitivity Analysis ◽  
Plasma Concentration ◽  
Global Sensitivity Analysis ◽  
Model Parameters ◽  
Model Parameter ◽  
Global Sensitivity ◽  
Physiologically Based ◽  
The Impact ◽  
Influential Parameters

Abstract Three global sensitivity analysis (GSA) methods (Morris, Sobol and extended Sobol) are applied to a minimal physiologically based PK (mPBPK) model using three model drugs given orally, namely quinidine, alprazolam, and midazolam. We investigated how correlations among input parameters affect the determination of the key parameters influencing pharmacokinetic (PK) properties of general interest, i.e., the maximal plasma concentration (Cmax) time at which Cmax is reached (Tmax), and area under plasma concentration (AUC). The influential parameters determined by the Morris and Sobol methods (suitable for independent model parameters) were compared to those determined by the extended Sobol method (which considers model parameter correlations). For the three drugs investigated, the Morris method was as informative as the Sobol method. The extended Sobol method identified different sets of influential parameters to Morris and Sobol. These methods overestimated the influence of volume of distribution at steady state (Vss) on AUC24h for quinidine and alprazolam. They also underestimated the effect of volume of liver (Vliver) for all three drugs, the impact of enzyme intrinsic clearance of CYP2C9 and CYP2E1 for quinidine, and that of UGT1A4 abundance for midazolam. Our investigation showed that the interpretation of GSA results is not straightforward. Dismissing existing model parameter correlations, GSA methods such as Morris and Sobol can lead to biased determination of the key parameters for the selected outputs of interest. Decisions regarding parameters’ influence (or otherwise) should be made in light of available knowledge including the model assumptions, GSA method limitations, and inter-correlations between model parameters, particularly in complex models.

scholarly journals Parameter Dependencies of a Biomechanical Cervical Spine FSU - The Process of Finding Optimal Model Parameters by Sensitivity Analysis

2021 ◽  
Author(s):  
Sabine Bauer ◽  
Ivanna Kramer
Keyword(s):  
Sensitivity Analysis ◽  
Intervertebral Discs ◽  
Model Parameters ◽  
Biomechanical Behavior ◽  
Functional Spinal Unit ◽  
Spine Model ◽  
Biomechanical Parameters ◽  
Multi Body ◽  
The Impact

The knowledge about the impact of structure-specific parameters on the biomechanical behavior of a computer model has an essential meaning for the realistic modeling and system improving. Especially the biomechanical parameters of the intervertebral discs, the ligamentous structures and the facet joints are seen in the literature as significant components of a spine model, which define the quality of the model. Therefore, it is important to understand how the variations of input parameters for these components affect the entire model and its individual structures. Sensitivity analysis can be used to gain the required knowledge about the correlation of the input and output variables in a complex spinal model. The present study analyses the influence of the biomechanical parameters of the intervertebral disc using different sensitivity analysis methods to optimize the spine model parameters. The analysis is performed with a multi-body simulation model of the cervical functional spinal unit C6-C7.

Nonintrusive Global Sensitivity Analysis for Linear Systems With Process Noise

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Souransu Nandi ◽  
Tarunraj Singh
Keyword(s):  
Sensitivity Analysis ◽  
Linear Systems ◽  
Global Sensitivity Analysis ◽  
Benchmark Problems ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Sobol Indices ◽  
Global Sensitivity ◽  
Time Invariant ◽  
The Impact

The focus of this paper is on the global sensitivity analysis (GSA) of linear systems with time-invariant model parameter uncertainties and driven by stochastic inputs. The Sobol' indices of the evolving mean and variance estimates of states are used to assess the impact of the time-invariant uncertain model parameters and the statistics of the stochastic input on the uncertainty of the output. Numerical results on two benchmark problems help illustrate that it is conceivable that parameters, which are not so significant in contributing to the uncertainty of the mean, can be extremely significant in contributing to the uncertainty of the variances. The paper uses a polynomial chaos (PC) approach to synthesize a surrogate probabilistic model of the stochastic system after using Lagrange interpolation polynomials (LIPs) as PC bases. The Sobol' indices are then directly evaluated from the PC coefficients. Although this concept is not new, a novel interpretation of stochastic collocation-based PC and intrusive PC is presented where they are shown to represent identical probabilistic models when the system under consideration is linear. This result now permits treating linear models as black boxes to develop intrusive PC surrogates.

scholarly journals A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments

2011 ◽  
Vol 11 (9) ◽  
pp. 2567-2582 ◽  
Author(s):  
H. Roux ◽  
D. Labat ◽  
P.-A. Garambois ◽  
M.-M. Maubourguet ◽  
J. Chorda ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Soil Properties ◽  
Overland Flow ◽  
Hydrological Model ◽  
Extreme Event ◽  
Model Parameters ◽  
Peak Time ◽  
Efficiency Coefficient ◽  
Soil Saturation ◽  
The Impact

Abstract. A spatially distributed hydrological model, dedicated to flood simulation, is developed on the basis of physical process representation (infiltration, overland flow, channel routing). Estimation of model parameters requires data concerning topography, soil properties, vegetation and land use. Four parameters are calibrated for the entire catchment using one flood event. Model sensitivity to individual parameters is assessed using Monte-Carlo simulations. Results of this sensitivity analysis with a criterion based on the Nash efficiency coefficient and the error of peak time and runoff are used to calibrate the model. This procedure is tested on the Gardon d'Anduze catchment, located in the Mediterranean zone of southern France. A first validation is conducted using three flood events with different hydrometeorological characteristics. This sensitivity analysis along with validation tests illustrates the predictive capability of the model and points out the possible improvements on the model's structure and parameterization for flash flood forecasting, especially in ungauged basins. Concerning the model structure, results show that water transfer through the subsurface zone also contributes to the hydrograph response to an extreme event, especially during the recession period. Maps of soil saturation emphasize the impact of rainfall and soil properties variability on these dynamics. Adding a subsurface flow component in the simulation also greatly impacts the spatial distribution of soil saturation and shows the importance of the drainage network. Measures of such distributed variables would help discriminating between different possible model structures.

Sensitivity analysis of an integrated numerical flow model output to key parameters used in common qualitative vulnerability assessment methods

2020 ◽  
Author(s):  
Joanna Doummar ◽  
Assaad H. Kassem
Keyword(s):  
Sensitivity Analysis ◽  
Vulnerability Assessment ◽  
Groundwater Vulnerability ◽  
Assessment Methods ◽  
Qualitative Assessment ◽  
Model Parameters ◽  
Karst Aquifers ◽  
Model Output ◽  
Local Sensitivity ◽  
The Impact

<p>Qualitative vulnerability assessment methods applied in karst aquifers rely on key factors in the hydrological compartments usually assigned different weights according to their estimated impact on groundwater vulnerability. Based on an integrated numerical groundwater model on a snow-governed karst catchment area (Assal Spring- Lebanon), the aim of this work is to quantify the importance of the most influential parameters on recharge and spring discharge and outline potential parameters that are not accounted for in standard methods, when in fact they do play a role in the intrinsic vulnerability of a system. The assessment of the model sensitivity and the ranking of parameters are conducted using an automatic calibration tool for local sensitivity analysis in addition to a variance-based local sensitivity assessment of model output time series (recharge and discharge)  for two consecutive years (2016-2017) to various model parameters. The impact of each parameter was normalized to estimate standardized weights for each of the process based key-controlling parameters. Parameters to which model was sensitive were factors related to soil, 2) fast infiltration (bypass function) typical of karst aquifers, 3) climatic parameters (melting temperature and degree day coefficient) and 4) aquifer hydraulic properties that play a major role in groundwater vulnerability inducing a temporal effect and varied recession. Other less important parameters play different roles according to different assigned weights proportional to their ranking. Additionally, the effect of slope/geomorphology (e.g., dolines) was further investigated.  In general, this study shows that the weighting coefficients assigned to key vulnerability factors in the qualitative assessment methods can be reevaluated based on this process-based approach.</p><p> </p><p> </p><p> </p>

scholarly journals Uncertainty and sensitivity analysis of GIS based continuous hydrological modelling

2021 ◽  
Author(s):  
Harry R. Manson
Keyword(s):  
Sensitivity Analysis ◽  
Curve Number ◽  
Monte Carlo Analysis ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Lumped Model ◽  
Daily Average ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

The impact of uncertainty in spatial and a-spatial lumped model parameters for a continuous rainfall-runoff model is evaluated with respect to model prediction. The model uses a modified SCS-Curve Number approach that is loosely coupled with a geographic information system (GIS). The rainfall-runoff model uses daily average inputs and is calibrated using a daily average streamflow record for the study site. A Monte Carlo analysis is used to identify total model uncertainty while sensitivity analysis is applied using both a one-at-a-time (OAT) approach as well as through application of the extended Fourier Amplitude Sensitivity Technique (FAST). Conclusions suggest that the model is highly followed by model inputs and finally the Curve Number. While the model does not indicate a high degree of sensitivity to the Curve Number at present conditions, uncertainties in Curve Number estimation can potentially be the cause of high predictive errors when future development scenarios are evaluated.

scholarly journals Groundwater pollution risk using a modified Latin hypercube sampling

2006 ◽  
Vol 8 (3) ◽  
pp. 223-234 ◽  
Author(s):  
Husam Baalousha
Keyword(s):  
Sensitivity Analysis ◽  
Groundwater Pollution ◽  
Probabilistic Models ◽  
Latin Hypercube Sampling ◽  
Model Parameters ◽  
Latin Hypercube ◽  
Deterministic Models ◽  
Estimation Errors ◽  
Mean Estimation ◽  
Longitudinal Dispersivity

Characterisation of groundwater modelling involves significant uncertainty because of estimation errors of these models and other different sources of uncertainty. Deterministic models do not account for uncertainties in model parameters, and thus lead to doubtful output. The main alternatives for deterministic models are the probabilistic models and perturbation methods such as Monte Carlo Simulation (MCS). Unfortunately, these methods have many drawbacks when applied in risk analysis of groundwater pollution. In this paper, a modified Latin Hypercube Sampling method is presented and used for risk, uncertainty, and sensitivity analysis of groundwater pollution. The obtained results were compared with other sampling methods. Results of the proposed method have shown that it can predict the groundwater contamination risk for all values of probability better than other methods, maintaining the accuracy of mean estimation. Sensitivity analysis results reveal that the contaminant concentration is more sensitive to longitudinal dispersivity than to velocity.

scholarly journals Modelling gastrointestinal parasitism infection in a sheep flock over two reproductive seasons:in silicoexploration and sensitivity analysis

Parasitology ◽  
2016 ◽  
Vol 143 (12) ◽  
pp. 1509-1531 ◽  
Author(s):  
M. SACCAREAU ◽  
C. R. MORENO ◽  
I. KYRIAZAKIS ◽  
R. FAIVRE ◽  
S. C. BISHOP
Keyword(s):  
Sensitivity Analysis ◽  
Control Strategies ◽  
Real Data ◽  
Published Data ◽  
Model Parameters ◽  
Sheep Flock ◽  
Source Of Infection ◽  
Teladorsagia Circumcincta ◽  
Host Nutrition ◽  
The Impact

SUMMARYIn reproducing ewes, a periparturient breakdown of immunity is often observed to result in increased fecal egg excretion, making them the main source of infection for their immunologically naive lambs. In this study, we expanded a simulation model previously developed for growing lambs to explore the impact of the genotype (performance and resistance traits) and host nutrition on the performance and parasitism of both growing lambs and reproducing ewes naturally infected withTeladorsagia circumcincta. Our model accounted for nutrient-demanding phases, such as gestation and lactation, and included a supplementary module to manage the age structure of the ewe flock. The model was validated by comparison with published data. Because model parameters were unknown or poorly estimated, detailed sensitivity analysis of the model was performed for the sheep mortality and the level of infection, following a preliminary screening step. The parameters with the greatest effect on parasite-related outputs were those driving animal growth and milk yield. Our model enables different parasite-control strategies (host nutrition, breeding for resistance and anthelmintic treatments) to be assessed on the long term in a sheep flock. To optimizein silicoexploration, the parameters highlighted by the sensitivity analysis should be refined with real data.

scholarly journals Sensitivity of Direct Runoff to Curve Number Using the SCS-CN Method

2019 ◽  
Vol 5 (12) ◽  
pp. 2738-2746
Author(s):  
Abdul Ghani Soomro ◽  
Muhammad Munir Babar ◽  
Anila Hameem Memon ◽  
Arjumand Zehra Zaidi ◽  
Arshad Ashraf ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Water Availability ◽  
Curve Number ◽  
Resource Planning ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Direct Runoff ◽  
Wide Range ◽  
The Impact ◽  
Scs Cn

This study explores the impact of runoff curve number (CN) on the hydrological model outputs for the Morai watershed, Sindh-Pakistan, using the Soil Conservation Service Curve Number (SCS-CN) method. The SCS-CN method is an empirical technique used to estimate rainfall-runoff volume from precipitation in small watersheds, and CN is an empirically derived parameter used to calculate direct runoff from a rainfall event. CN depends on soil type, its condition, and the land use and land cover (LULC) of an area. Precise knowledge of these factors was not available for the study area, and therefore, a range of values was selected to analyze the sensitivity of the model to the changing CN values. Sensitivity analysis involves a methodological manipulation of model parameters to understand their impacts on model outputs. A range of CN values from 40-90 was selected to determine their effects on model results at the sub-catchment level during the historic flood year of 2010. The model simulated 362 cumecs of peak discharge for CN=90; however, for CN=40, the discharge reduced substantially to 78 cumecs (a 78.46% reduction). Event-based comparison of water volumes for different groups of CN values—90-75, 80-75, 75-70, and 90-40 —showed reductions in water availability of 8.88%, 3.39%, 3.82%, and 41.81%, respectively. Although it is known that the higher the CN, the greater the discharge from direct runoff and the less initial losses, the sensitivity analysis quantifies that impact and determines the amount of associated discharges with changing CN values. The results of the case study suggest that CN is one of the most influential parameters in the simulation of direct runoff. Knowledge of accurate runoff is important in both wet (flood management) and dry periods (water availability). A wide range in the resulting water discharges highlights the importance of precise CN selection. Sensitivity analysis is an essential facet of establishing hydrological models in limited data watersheds. The range of CNs demonstrates an enormous quantitative consequence on direct runoff, the exactness of which is necessary for effective water resource planning and management. The method itself is not novel, but the way it is proposed here can justify investments in determining the accurate CN before initiating mega projects involving rainfall-runoff simulations. Even a small error in CN value may lead to serious consequences. In the current study, the sensitivity analysis challenges the strength of the results of a model in the presence of ambiguity regarding CN value.

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