Learning Reduced-Order Mappings between Functions: An Investigation of Suitable Inputs and Outputs

Data-driven approaches are a promising new addition to the list of available strategies for solving Partial Differential Equations (PDEs). One such approach, the Principal Component Analysis-based Neural Network PDE solver, can be used to learn a mapping between two function spaces, corresponding to a PDE. However, the practical limitations of...

Probabilistic classification of soils based on Local Average Subdivision method and CPT data

A random field generator based on Local Average Subdivision (LAS) method is proposed in this study in order to achieve probabilistic soil classification and quantify the uncertainty of the generated most probable geological cross section. CPT data and Robertson’s soil classification chart (1990) are adopted to classify the soil. The sole...

Spatiotemporal Gaussian random fields using stochastic partial differential equations

In various scientific disciplines, measurement data is collected across space and over time with the aim of inferring information regarding an underlying stochastic spatiotemporal phenomenon. The high computational costs of current methods render this task intractable for the large datasets typically encountered in spatiotemporal statistics. <br...

Probabilistic analysis of slope stability in spatially variable soils using Monte Carlo simulation

Soil properties are spatially variable due to the natural deposition process. Because of this inherent spatial variability, a slope can actually fail along any potential slip surface. A single value of Factor of safety cannot account for this variation dominated the slope stability problem. Probabilistic analysis considering the spatial...

A probabilistic approach to sheet pile driveability predictions by vibro hammers

During the design phase of a foundation installation aspects are easily overlooked. When this aspect is overlooked a foundation element risks not reaching its design depth or getting damaged during the installation process. As a result significant delays and/or costs could occur. A driveability study gives insight in the installation aspects of...

Reliability assessment of corroded reinforced concrete structures: The effect of using random fields and nonlinear finite element analysis

One of the most severe deteriorations in reinforced concrete structures is associated with reinforcement corrosion, where the corrosion distribution shows considerable spatial variability. In the existing investigations of spatial variability of corrosion on the reliability of reinforced concrete structures, symbolic expressions are used for the...

Model-based rare category detection for temporal data

Rare category detection is the task of discovering rare classes in unlabelled and imbalanced datasets. Existing algorithms focus almost exclusively on static data in which instances are assumed to be independent. In this thesis we propose an algorithm that is designed for temporal data. Specifically, we are interested in data with temporal...

Random Fields for Non-Linear Finite Element Analysis of Reinforced Concrete

With more advanced methods and increasing computational power, the simulation of reinforced concrete in a Finite Element Analysis (FEA) has become more and more realistic. In a non-linear analysis of reinforced concrete, cracking behaviour and the maximum load-capacity can be determined. Such analyses sometimes suffer from unstable behaviour,...

Conditional simulation for characterising the spatial variability of sand state

Properties of soils are spatially variable and to describe the behaviour of soils as a response to loading, this variability appears crucial in giving the correct range of possible solutions for structure response. Because site investigation techniques only provide exact information at a limited part of the site, random field simulations are...