The influence of spatial variation on the design of foundations of immersed tunnels: Advanced probabilistic analysis

Immersed tunnels are positive buoyant structures during installation and negative buoyant after installation. A tunnel is composed of sequential immersed elements that are coupled to each other in joints. Tunnel elements consist of segments which are compressed to each other by longitudinal post-tensioning. After immersion the tunnel is...

Development of a framework to estimate the condition of mechanical, electrical and plumbing systems with Bayesian Networks

As the Western European building stock ages, attention is increasingly allocated to the maintenance of building components, particularly mechanical, electrical and plumbing (MEP) systems. Although the latter are essential in ensuring the correct operation of a building and the safety of its occupants, they remain the crafts where the most...

PyBanshee version (1.0): A Python implementation of the MATLAB toolbox BANSHEE for Non-Parametric Bayesian Networks with updated features

In this paper we discuss PyBanshee, which is a Python-based open-source implementation of the MATLAB toolbox BANSHEE. PyBanshee constitutes the first fully open-source package to quantify, visualize and validate Non-Parametric Bayesian Networks (NPBNs). The architecture of PyBanshee is heavily based on its MATLAB predecessor. It presents the...

Sequential decision making using Bayesian networks and reinforcement learning: to determine the hydrocarbon prospect exploration strategy

In the process of drilling wells to produce hydrocarbons, an exploration strategy is used to determine which wells should be drilled and in which order. This strategy is vital, as a suboptimal drilling sequence will lead to more expenses and fewer gains.<br/>Furthermore, the wells considered in most exploration strategies are geologically<br/...

Day Ahead Market price scenario generation using a Combined Quantile Regression Deep Neural Network and a Non-parametric Bayesian Network: A framework for risk-based Demand Response

In this manuscript we propose a methodology to generate electricity price scenarios from probabilistic forecasts. Using a Combined Quantile Regression Deep Neural Network, we forecast hourly marginal price distribution quantiles for the DAM on which we fit parametric distributions. A Non-parametric Bayesian Network (BN) is applied to sample from...

Rapid assessment tool to quantify the spatial influence of surfaces on heat stress: Estimating the Physical Equivalent Temperature in urban regions using dependence modelling

Climate change causes cities to deal with increased temperatures and more frequent weather extremes. Heat waves will occur more often, becoming a more prevalent issue in especially urban areas. The quantification of heat stress is a first step to define mitigation measures. For that purpose, a standardised method to assess the spatial influence...

Probabilistic Reliability Assessment for Non-Linear Finite Element Analysis of Reinforced Concrete Beams

Non-linear finite element analysis (NLFEA) is a powerful numerical solution method that can enhance accurate determination of the structural resistance for a more efficient design. However, the implementation of NLFEA for the design of reinforced concrete structures is lagging behind as related uncertain- ties have not been quantified adequately...

A Non-Parametric Bayesian Network Hydrologic Model: A Case Study of a Lowland Catchment

The past decades, the increasing availability of data has paved the way for a new, data-driven generation of models. This research proposes a non-parametric Bayesian network (NPBN) to model hydrologic processes. The Bayesian network (BN) is a directed, acyclic graph in which the variables are represented by the nodes, and the conditional...

Flood Risk Prediction under Global Vegetated Hydrodynamics: A Bayesian Network

The increasing frequency and intensity of extreme events due to global warming and climate change is increasing flood risk. To act, rather than react, nature-based solutions (NBS) involving vegetation and wetlands are being explored on top of conventional solutions like dikes. WHY? There was a dire need for global study quantifying the potential...

Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis

Cliff coasts are dynamic environments that can retreat very quickly. However, the<br/>short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored with the use of terrestrial laser scanner technology over a...

Modelling citation performance of Canadian researchers using Bayesian Networks

This thesis is about modelling citation performance using Bayesian networks (BN). As an approximation for citation performance, we will use the proportion of papers by an author that resulted in being one of the top 10% most cited papers. Using 15 predicting variables such as year of first publication and average number of authors per paper, we...

Permeability Estimation Using a Non-Parametric Bayesian Network

This thesis addresses the problem of permeability estimation of an oil reservoir using oil production data, a process known in the oil industry as computer-assisted history matching or data assimilation. Zilko (2012) compared the well-known Ensemble Kalman Filter method for data assimilation with the Non-Parametric Bayesian Beliefs Network (NPBN...

Non-Parametric Bayesian Networks (NPBNs) versus Ensemble Kalman Filter (EnKF) in Reservoir Simulation with non-Gaussian Measurement Noise

Lately, the objective of reservoir engineering is to optimize hydrocarbon recovery from a reservoir. To achieve that goal, a good knowledge of the subsurface properties is crucial. The author is concerned with estimating one of the properties of the field: the permeability of a reservoir. To characterize the fluid flow, a two phase (oil-water)...