Probabilistic sensitivity analysis to understand the influence of micromechanical properties of wood on its macroscopic response

Hristov, P.O.; DiazDelaO, F.A.; Flores, Erick I. Saavedra; Guzmán, Carlos Felipe; Farooq, U.

doi:10.1016/j.compstruct.2017.08.105

Cited by 8 publications

(13 citation statements)

References 19 publications

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“…where η(x) is the simulator output as a function of the input parameters, x, h(x) T is a vector of basis functions of the inputs, B is a vector of corresponding regression coefficients, and Z(x) is a Gaussian process with zero mean and covariance [10,12]. h(x) and B define the overall form and trend of the emulator output.…”

Section: Gaussian Process Emulationmentioning

confidence: 99%

“…In Dakota the trend can be set as either constant, linear, reduced quadratic, or quadratic. It is common to select a constant trend and leave the Gaussian process (Z(x)) to capture higher order complexity in the η function [12]. The parameters that define the Gaussian process are called hyperparameters to prevent confusion with model input parameters.…”

Section: Gaussian Process Emulationmentioning

confidence: 99%

“…A global sensitivity analysis relies on a number of points scattered evenly through the input parameter space. If a simulator is deterministic, the variance of the output variable is solely the result of uncertainty in the input variables [12]. In an ideal world, the exact values of the input parameters would all be measurable, and the variance of Y (the output) could be reduced to zero.…”

Section: Variance-based Sensitivity Analysismentioning

confidence: 99%

“…However, this method of variance calculation could be strongly affected by the location of the point x i . We can resolve this by taking the average of all these conditional variances over all values of x i [12]. That is:…”

Section: Variance-based Sensitivity Analysismentioning

confidence: 99%

“…To facilitate a richer sampling of the input parameter space we use Dakota [10], an extensive uncertainty quantification and optimisation toolkit, to train a Gaussian process emulator (GPE) with a training dataset of SPH simulation inputs and results. GPEs have been used previously both for LGW studies [11] and in other fields such as materials science [12] and climate modelling [13]. The Gaussian process emulator is tested against additional SPH simulation results to ensure that it is able to predict the simulated outcomes to a satisfactory degree of accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty Quantification of Landslide Generated Waves Using Gaussian Process Emulation and Variance-Based Sensitivity Analysis

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Simulations of landslide generated waves (LGWs) are prone to high levels of uncertainty. Here we present a probabilistic sensitivity analysis of an LGW model. The LGW model was realised through a smooth particle hydrodynamics (SPH) simulator, which is capable of modelling fluids with complex rheologies and includes flexible boundary conditions. This LGW model has parameters defining the landslide, including its rheology, that contribute to uncertainty in the simulated wave characteristics. Given the computational expense of this simulator, we made use of the extensive uncertainty quantification functionality of the Dakota toolkit to train a Gaussian process emulator (GPE) using a dataset derived from SPH simulations. Using the emulator we conducted a variance-based decomposition to quantify how much each input parameter to the SPH simulation contributed to the uncertainty in the simulated wave characteristics. Our results indicate that the landslide’s volume and initial submergence depth contribute the most to uncertainty in the wave characteristics, while the landslide rheological parameters have a much smaller influence. When estimated run-up is used as the indicator for LGW hazard, the slope angle of the shore being inundated is shown to be an additional influential parameter. This study facilitates probabilistic hazard analysis of LGWs, because it reveals which source characteristics contribute most to uncertainty in terms of how hazardous a wave will be, thereby allowing computational resources to be focused on better understanding that uncertainty.

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Section: Gaussian Process Emulationmentioning

confidence: 99%

Section: Gaussian Process Emulationmentioning

confidence: 99%

Section: Variance-based Sensitivity Analysismentioning

confidence: 99%

Section: Variance-based Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty Quantification of Landslide Generated Waves Using Gaussian Process Emulation and Variance-Based Sensitivity Analysis

Snelling

Neethling

Horsburgh

et al. 2020

Water

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Influence of morphology on the effective hygro-elastic properties of softwood (spruce) and hardwood (balsa)

2021

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Wood materials are characterized by complex, hierarchical material structures spanning across various length scales. The present work aims at establishing a relation between the hygro-elastic properties at the mesoscopic cellular level and the effective material response at the macroscopic level, both for softwood (spruce) and hardwood (balsa). The particular aim is to explore the influence on the effective hygro-elastic properties under variations in the meso-scale morphology. The multi-scale framework applied for this purpose uses the method of asymptotic homogenization, which allows to accurately and efficiently obtain the effective response of heterogeneous materials characterized by complex meso-structural geometries. The meso-structural model considered for softwood is based on a periodic, two-dimensional statistically representative volume element that is generated by a spatial repetition of tracheid cells. The tracheid cells are modeled as hexagonal elements characterized by a certain geometrical irregularity. The hardwood meso-structure consists of a region composed of hexagonal cellular fibers with large vessels embedded, which is connected to a ray region that is constructed of ray cells. The hardwood fibers are modeled as hexagonal cellular elements, similar to softwood tracheids. The rays are represented by quadrilateral cells oriented along the radial direction, whereby different arrangements are considered, i.e., the ray cells are either regularly stacked or organized as a staggered configuration. The interface between the fiber and ray regions may also be characterized by a regular or a staggered arrangement. The meso-structural models for softwood and hardwood are discretized by means of plane-strain, finite element models, which describe the hygro-elastic response of the wood material in the radial–tangential plane. For softwood, the sensitivity of the effective elastic and hygro-expansive properties is explored as a function of the geometrical irregularity of the tracheids. For hardwood, the effective properties are studied under a variation of the ray cell arrangement, the type of interface between ray and fiber regions, and the vessel volume fraction. The modeling results agree well with results obtained from other numerical homogenization studies and show to be in reasonable agreement with experimental data taken from the literature.

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Optimal experiment design for a bottom friction parameter estimation problem

Warder

Piggott

2022

Int J Geomath

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Calibration with respect to a bottom friction parameter is standard practice within numerical coastal ocean modelling. However, when this parameter is assumed to vary spatially, any calibration approach must address the issue of overfitting. In this work, we derive calibration problems in which the control parameters can be directly constrained by available observations, without overfitting. This is achieved by carefully selecting the ‘experiment design’, which in general encompasses both the observation strategy, and the choice of control parameters (i.e. the spatial variation of the friction field). In this work we focus on the latter, utilising existing observations available within our case study regions. We adapt a technique from the optimal experiment design (OED) literature, utilising model sensitivities computed via an adjoint-capable numerical shallow water model, Thetis. The OED method uses the model sensitivity to estimate the covariance of the estimated parameters corresponding to a given experiment design, without solving the corresponding parameter estimation problem. This facilitates the exploration of a large number of such experiment designs, to find the design producing the tightest parameter constraints. We take the Bristol Channel as a primary case study, using tide gauge data to estimate friction parameters corresponding to a piecewise-constant field. We first demonstrate that the OED framework produces reliable estimates of the parameter covariance, by comparison with results from a Bayesian inference algorithm. We subsequently demonstrate that solving an ‘optimal’ calibration problem leads to good model performance against both calibration and validation data, thus avoiding overfitting.

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Probabilistic sensitivity analysis to understand the influence of micromechanical properties of wood on its macroscopic response

Cited by 8 publications

References 19 publications

Uncertainty Quantification of Landslide Generated Waves Using Gaussian Process Emulation and Variance-Based Sensitivity Analysis

Uncertainty Quantification of Landslide Generated Waves Using Gaussian Process Emulation and Variance-Based Sensitivity Analysis

Influence of morphology on the effective hygro-elastic properties of softwood (spruce) and hardwood (balsa)

Optimal experiment design for a bottom friction parameter estimation problem

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