scholarly journals Global Sensitivity Analysis of Attenuation Zones of 2D Periodic Foundations

2021 ◽  
Author(s):  
Xinnan Liu ◽  
Yuan Tian ◽  
Yihe Wang ◽  
Yiqiang Ren ◽  
Xiaoruan Song
Keyword(s):  
Sensitivity Analysis ◽  
Lower Bound ◽  
Initial Stress ◽  
Upper Bound ◽  
Filling Ratio ◽  
Sensitivity Analyses ◽  
Relative Importance ◽  
Global Sensitivity ◽  
Input Parameters ◽  
Periodic Constant

In this paper, global sensitivity analyses of attenuation zones of 2D periodic foundations are conducted. Global sensitivity analyses of upper bound frequency and lower bound frequency of the 1st attenuation zone of 2D periodic foundation are conducted considering four input parameters, i.e., initial stress ratio, filling ratio of core, filling ratio of resonator and periodic constant. Interactions and relative importance of input parameters are calculated.

scholarly journals Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution Models

2017 ◽  
Author(s):  
Christopher J. Skinner ◽  
Tom J. Coulthard ◽  
Wolfgang Schwanghart ◽  
Marco J. Van De Wiel ◽  
Greg Hancock
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Global Sensitivity Analysis ◽  
Landscape Evolution ◽  
Sensitivity Analyses ◽  
Large Set ◽  
Sediment Yields ◽  
Global Sensitivity ◽  
Environmental Models ◽  
Morris Method

Abstract. Landscape Evolution Models have a long history of use as exploratory models, providing greater understanding of the role large scale processes have on the long-term development of the Earth’s surface. As computational power has advanced so has the development and sophistication of these models. This has seen them applied at increasingly smaller scale and shorter-term simulations at greater detail. However, this has not gone hand-in-hand with more rigorous verifications that are commonplace in the applications of other types of environmental models- for example Sensitivity Analyses. This can be attributed to a paucity of data and methods available in order to calibrate, validate and verify the models, and also to the extra complexity Landscape Evolution Models represent – without these it is not possible to produce a reliable Objective Function against which model performance can be judged. To overcome this deficiency, we present a set of Model Functions – each representing an aspect of model behaviour – and use these to assess the relative sensitivity of a Landscape Evolution Model (CAESAR-Lisflood) to a large set of parameters via a global Sensitivity Analysis using the Morris Method. This novel combination of behavioural Model Functions and the Morris Method provides insight into which parameters are the greatest source of uncertainty in the model, and which have the greatest influence over different model behaviours. The method was repeated over two different catchments, showing that across both catchments and across most model behaviours the choice of Sediment Transport formula was the dominate source of uncertainty in the CAESAR-Lisflood model, although there were some differences between the two catchments. Crucially, different parameters influenced the model behaviours in different ways, with Model Functions related to internal geomorphic changes responding in different ways to those related to sediment yields from the catchment outlet. This method of behavioural sensitivity analysis provides a useful method of assessing the performance of Landscape Evolution Models in the absence of data and methods for an Objective Function approach.

scholarly journals Deeper understanding of non-linear geodetic data inversion using a quantitative sensitivity analysis

2005 ◽  
Vol 12 (3) ◽  
pp. 373-379 ◽  
Author(s):  
C. Tiede ◽  
K. Tiampo ◽  
J. Fernández ◽  
C. Gerstenecker
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Unknown Input ◽  
Volcanic Area ◽  
Unknown Parameters ◽  
Linear Inversion ◽  
Reliable Determination ◽  
Global Sensitivity ◽  
Non Linear ◽  
Input Parameters

Abstract. A quantitative global sensitivity analysis (SA) is applied to the non-linear inversion of gravity changes and displacement data which measured in an active volcanic area. The common inversion of this data is based on the solution of the generalized Navier equations which couples both types of observation, gravity and displacement, in a homogeneous half space. The sensitivity analysis has been carried out using Sobol's variance-based approach which produces the total sensitivity indices (TSI), so that all interactions between the unknown input parameters are taken into account. Results of the SA show quite different sensitivities for the measured changes as they relate to the unknown parameters for the east, north and height component, as well as the pressure, radial and mass component of an elastic-gravitational source. The TSIs are implemented into the inversion in order to stabilize the computation of the unknown parameters, which showed wide dispersion ranges in earlier optimization approaches. Samples which were computed using a genetic algorithm (GA) optimization are compared to samples in which the results of the global sensitivity analysis are integrated by a reweighting of the cofactor matrix in the objective function. The comparison shows that the implementation of the TSI's can decrease the dispersion rate of unknown input parameters, producing a great improvement the reliable determination of the unknown parameters.

scholarly journals Development of Smart Pavement Design Sensitivity Analysis Software for Asset Management System

2020 ◽  
Vol 5 (7) ◽  
pp. 56
Author(s):  
Byungkyu Moon ◽  
Jungyong “Joe” Kim ◽  
Hosin “David” Lee
Keyword(s):  
Sensitivity Analysis ◽  
Management System ◽  
Design Sensitivity ◽  
Pavement Management ◽  
Pavement Design ◽  
Design Sensitivity Analysis ◽  
Sensitivity Analyses ◽  
Analysis Software ◽  
Input Parameters ◽  
Pavement Thickness

There are a number of pavement management systems, but most of them are limited in providing pavement design and pavement design sensitivity information. This paper presents efforts towards the integrated pavement design and management system, by developing smart pavement design sensitivity analysis software. In this paper, the sensitivity analyses of critical design input parameters have been performed to identify input parameters which have the most significant impacts on the pavement thickness. Based on the existing pavement design procedures and their sensitivity analysis results, a smart pavement design sensitivity analysis (PDSA) software package was developed, to allow a user to retrieve the most appropriate pavement thickness and immediately perform pavement design sensitivity analysis. The PDSA software is a useful tool for managing pavements, by allowing a user to instantaneously retrieve a pavement design for a given condition from the database and perform a design sensitivity analysis without running actual pavement design programs. The proposed smart PDSA software would result in the most efficient pavement management system, by incorporating the optimum pavement thickness as part of the pavement management process.

A simple sensitivity analysis of the turbulence-induced flocculation model of cohesive sediment

2019 ◽  
Vol 79 (6) ◽  
pp. 1144-1151 ◽  
Author(s):  
Zhongfan Zhu
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Fractal Dimension ◽  
Cohesive Sediment ◽  
Sensitivity Analyses ◽  
Model Output ◽  
Global Sensitivity ◽  
Two Parameters ◽  
Breakup Parameter

Abstract In this study, the local and global sensitivity analyses of the Winterwerp model to the input parameters have been carried out using the Garson algorithm and the PaD2 method by virtue of an artificial neural network. The main results of the sensitivity analyses are that the model is most sensitive to the breakup parameter and that only two parameters (the floc aggregation and breakup parameters) are significant. The result that the model output is less sensitive to the choice of fractal dimension seems to imply that the modification work on the fractal dimension might be unnecessary.

Compressive Strength of Mortars Admixed with Wollastonite and Microsilica

2017 ◽  
Vol 890 ◽  
pp. 415-418 ◽  
Author(s):  
Hosein Naderpour ◽  
Masoomeh Mirrashid
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Ordinary Portland Cement ◽  
Building Blocks ◽  
Relative Importance ◽  
Input Parameters ◽  
Micro Silica ◽  
High Range

Mortar can be defined as a material which is used to hold building blocks that include bricks, stone or concrete together. In this paper, a special type of mortar which is mixed with Wollastonite and also micro-silica is considered and the goal was predicting the compressive strength of mortar in terms of artificial neural networks. For this purpose, the input parameters were assumed to be: the weight of microsilica, wollastonite, ordinary Portland cement, high-range water reducer, and also age of the mortar. Finally based on a sensitivity analysis, the relative importance of input parameters were determined.

scholarly journals Inverse sensitivity analysis of mathematical models avoiding the curse of dimensionality

2018 ◽  
Author(s):  
Ben Lambert ◽  
David J. Gavaghan ◽  
Simon Tavener
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Mathematical Models ◽  
Biological Systems ◽  
Sensitivity Analyses ◽  
Deterministic Models ◽  
Deterministic Systems ◽  
Input Parameters ◽  
Insight Into

1AbstractBiological systems have evolved a degree of robustness with respect to perturbations in their environment and this capability is essential for their survival. In applications ranging from therapeutics to conservation, it is important to understand not only the sensitivity of biological systems to changes in their environments, but which features of these systems are necessary to achieve a given outcome. Mathematical models are increasingly employed to understand these mechanisms. Sensitivity analyses of such mathematical models provide insight into the responsiveness of the system when experimental manipulation is difficult. One common approach is to seek the probability distribution of the outputs of the system corresponding to a known distribution of inputs. By contrast, inverse sensitivity analysis determines the probability distribution of model inputs which produces a known distribution of outputs. The computational complexity of the methods used to conduct inverse sensitivity analyses for deterministic systems has limited their application to models with relatively few parameters. Here we describe a novel Markov Chain Monte Carlo method we call “Contour Monte Carlo”, which can be used to invert systems with a large number of parameters. We demonstrate the utility of this method by inverting a range of frequently-used deterministic models of biological systems, including the logistic growth equation, the Michaelis-Menten equation, and an SIR model of disease transmission with nine input parameters. We argue that the simplicity of our approach means it is amenable to a large class of problems of practical significance and, more generally, provides a probabilistic framework for understanding the inversion of deterministic models.2Author summaryMathematical models of complex systems are constructed to provide insight into their underlying functioning. Statistical inversion can probe the often unobserved processes underlying biological systems, by proceeding from a given distribution of a model’s outputs (the aggregate “effects”) to a distribution over input parameters (the constituent “causes”). The process of inversion is well-defined for systems involving randomness and can be described by Bayesian inference. The inversion of a deterministic system, however, cannot be performed by the standard Bayesian approach. We develop a conceptual framework that describes the inversion of deterministic systems with fewer outputs than input parameters. Like Bayesian inference, our approach uses probability distributions to describe the uncertainty over inputs and outputs, and requires a prior input distribution to ensure a unique “posterior” probability distribution over inputs. We describe a computational Monte Carlo method that allows efficient sampling from the posterior distribution even as the dimension of the input parameter space grows. This is a two-step process where we first estimate a “contour volume density” associated with each output value which is then used to define a sampling algorithm that yields the requisite input distribution asymptotically. Our approach is simple, broadly applicable and could be widely adopted.

Global Sensitivity Analysis of Optimal Climate Policies

2020 ◽  
Author(s):  
Alena Miftakhova
Keyword(s):  
Sensitivity Analysis ◽  
High Efficiency ◽  
Global Sensitivity Analysis ◽  
Economic Modeling ◽  
Sensitivity Analyses ◽  
Global Sensitivity ◽  
Computational Costs ◽  
Climate Policies ◽  
Careful Treatment ◽  
Polynomial Chaos Expansions

<p><span>A major tool that supports climate policy decisions, integrated assessment models are highly vulnerable to their initial assumptions and calibrations. Despite the broad literature rich in both single-model and multi-model sensitivity analyses, universal, well-established practices are still missing in this field. This paper endorses structured global sensitivity analysis (GSA) as an indispensable routine in climate–economic modeling. An application of a high-efficiency GSA method based on polynomial chaos expansions to DICE provides two insights. First, only global and comprehensive—as opposed to local or selective—sensitivity analysis delivers a trustworthy picture of the uncertainty propagated through the model. Second, careful treatment of the model’s structure throughout the analysis reconciles the results with established analytical insights—enhancing these insights with more details. The efficient GSA method provides a comprehensive decomposition of the uncertainty in a model’s output while minimizing computational costs, and is hence potentially applicable to models of higher complexity.</span></p>

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