Uncertainty and sensitivity analysis applied to a rammed earth wall: evaluation of the discrepancies between experimental and numerical data

Due to the environmental impact of building materials, researches on sustainable materials, such as bio-based and earth materials, are now widespread. These materials offer numerous qualities such as their availability, recyclability and their ability to dampen the indoor relative humidity variations due to their hygroscopicity. As these materials can absorb large amount of humidity, numerical and experimental studies of their hygrothermal behaviour are crucial to assess their durability. To validate a hygrothermal model, numerical and experimental data have to be confronted. Such confrontation must take into consideration the uncertainties related to the experimental protocol, but also to the model. Statistical tools such as uncertainty and global sensitivity analysis are essential for this task. The uncertainty analysis estimates the robustness of the model, while the global sensitivity analysis identifies the most influential input(s) responsible for this robustness. However, these methods are not commonly used because of the complexity of hygrothermal models, and therefore the prohibitive simulation cost. This study presents a methodology for comparing the numerical and experimental data of a rammed earth wall subjected to varying temperature and relative humidity conditions. The main objectives are the investigation of the uncertainties impact, the estimation of the model robustness, and finally the identification of the input(s) responsible for the discrepancies between numerical and experimental data. To do so, a recent and low-cost global variance-based sensitivity method, named RBD-FAST, is applied. First, the uncertainty propagation through the model is calculated, then the sensitivity indices are estimated. They represent the part of the output variability related to each input variability. The output of interest is the vapour pressure in the middle of the wall to confront it to the experimental measurement. Good agreement is obtained between the experimental and numerical results. It is also highlighted that the sorption isotherm is the main factor influencing the vapour pressure in the material.

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Global Sensitivity Analysis Procedure Accounting for Effect of Available Experimental Data

Industrial & Engineering Chemistry Research ◽

10.1021/ie101283g ◽

2011 ◽

Vol 50 (3) ◽

pp. 1294-1304 ◽

Cited By ~ 3

Author(s):

Yunfei Chu ◽

Zuyi Huang ◽

Juergen Hahn

Keyword(s):

Experimental Data ◽

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

Analysis Procedure ◽

Global Sensitivity

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Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models

10.1101/294884 ◽

2018 ◽

Cited By ~ 1

Author(s):

Olivia Eriksson ◽

Alexandra Jauhiainen ◽

Sara Maad Sasane ◽

Andrei Kramer ◽

Anu G Nair ◽

...

Keyword(s):

Experimental Data ◽

Sensitivity Analysis ◽

Bayesian Analysis ◽

Uncertainty Quantification ◽

Posterior Distribution ◽

Global Sensitivity Analysis ◽

Parameter Estimates ◽

Model Parameters ◽

Individual Parameter ◽

Global Sensitivity

AbstractMotivationDynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as give guidance on parameters that are essential in distinguishing different qualitative output behaviours.ResultsWe used approximate Bayesian computation (ABC) to estimate the model parameters from experimental data, as well as to quantify the uncertainty in this estimation (inverse uncertainty quantification), resulting in a posterior distribution for the parameters. This parameter uncertainty was next propagated to a corresponding uncertainty in the predictions (forward uncertainty propagation), and a global sensitivity analysis was performed on the prediction using the posterior distribution as the possible values for the parameters. This methodology was applied on a relatively large and complex model relevant for synaptic plasticity, using experimental data from several sources. We could hereby point out those parameters that by themselves have the largest contribution to the uncertainty of the prediction as well as identify parameters important to separate between qualitatively different predictions.This approach is useful both for experimental design as well as model building.

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