A new Bayesian approach for managing bathing water quality at river bathing locations vulnerable to short-term pollution

Short-term fecal pollution events are a major challenge for managing microbial safety at recreational waters. Long turn-over times of current laboratory methods for analyzing fecal indicator bacteria (FIB) delay water quality assessments. Data-driven models have been shown to be valuable approaches to enable fast water quality assessments....

Non-destructive strength prediction of composite laminates utilizing deep learning and the stochastic finite element methods

A hybrid methodology based on numerical and non-destructive experimental schemes, which is able to predict the structural level strength of composite laminates is proposed on the current work. The main objective is to predict the strength by substituting the up to failure experiments with non-destructive experiments where the investigated...

Finding Dangerous Waves-Review of Methods to Obtain Wave Impact Design Loads for Marine Structures

Green water and slamming wave impacts can lead to severe damage or operability issues for marine structures. It is therefore essential to consider their probability and loads in design. This is difficult, as impacts are both hydrodynamically complex and relatively rare. The complexity requires high-fidelity modeling (experiments or CFD),...

Neural Maximum-a-Posteriori Beamforming for Ultrasound Imaging

Ultrasound imaging is an attractive imaging modality due to its low-cost and real-time feedback, although it often falls short in image quality compared to MRI and CT imaging. Conventional ultrasound image reconstruction, such as Delay-and-Sum beamforming, is derived from maximum-likelihood estimation. As such, no prior information is exploited...

Nessy: A Neuro-Symbolic System for Label Noise Reduction

Noisy labels represent one of the key issues in supervised machine learning. Existing work for label noise reduction mainly takes a probabilistic approach that infers true labels from data distributions in low-level feature spaces. Such an approach is not only limited by its capability to learn high-quality data representations, but also by...

Editorial: Multivariate Probabilistic Modelling for Risk and Decision Analysis

BANSHEE–A MATLAB toolbox for Non-Parametric Bayesian Networks

Bayesian Networks (BNs) are probabilistic, graphical models for representing complex dependency structures. They have many applications in science and engineering. Their particularly powerful variant – Non-Parametric BNs – are for the first time implemented as an open-access scriptable code, in the form of a MATLAB toolbox “BANSHEE”.<sup>1<...

Probabilistic Assessment of Overtopping of Sea Dikes with Foreshores including Infragravity Waves and Morphological Changes: Westkapelle Case Study

Shallow foreshores in front of coastal dikes can reduce the probability of dike failure due to wave overtopping. A probabilistic model framework is presented, which is capable of including complex hydrodynamics like infragravity waves, and morphological changes of a sandy foreshore during severe storms in the calculations of the probability of...

轨道不平顺概率模型

To efficiently select track irregularity random samples for satisfying the ergodicity requirements of excitation sources in stochastic dynamics and reliability analysis in vehicle-track system, the weak-stationarity and similarity spectral of track irregularities were introduced to propose a track irregularity probabilistic model. Using the...

Safety analysis of passing maneuvers using extreme value theory

The increased availability of detailed trajectory data sets from naturalistic, observational, and simulation-based studies, is a key source for potential improvements in the development of detailed safety models that explicitly account for vehicle conflict interactions and various driving maneuvers. Despite the well-recognized research...

Climate change in asset management of infrastructure: A risk-based methodology applied to disruption of traffic on road networks due to the flooding of tunnels

This paper presents a risk-based method to quantify climate change effects on road infrastructure, as a support for decision-making on interventions. This can be implemented in climate adaptation plans as an element of asset management. The method is illustrated by a specific case in which traffic on a road network is disrupted by the flooding...

A gaming approach to networked infrastructure management

Operational decision-making processes for networked infrastructure management often occur as a multi-actor planning problem, implying these are based on negotiations between different stakeholders in addition to available system quality information. As such, does more accurate data about actual structural condition lead to other or better...

Precision Constrained Optimization by Exponential Ranking

Demonstrative results of a probabilistic constraint handling approach that is exclusively using evolutionary computation are presented. In contrast to other works involving the same probabilistic considerations, in this study local search has been omitted, in order to assess the necessity of this deterministic local search procedure in...

Further Note on the Probabilistic Constraint Handling

A robust probabilistic constraint handling approach in the framework of joint evolutionary-classical optimization has been presented earlier. In this work, the<br/>theoretical foundations of the method are presented in detail. The method is known as bi-objective method, where the conventional penalty function approach is implemented. The present...

Safety analysis of passing maneuvers using extreme value theory

The increased availability of detailed trajectory data sets from naturalistic, observational and simulation-based studies are a key source for potential improvements in the development of detailed safety models that explicitly account for vehicle conflict interactions and the various driving maneuvers. Despite the well-recognized research...

Introducing the Core Probability Framework and Discrete-Element Core Probability Model for efficient stochastic macroscopic modelling

In this contribution the Core Probability Framework (CPF) is introduced with the application of the Discrete-Element Core Probability Model (DE-CPM) as a new DNL for dynamic macroscopic modelling of stochastic traffic flow. The model is demonstrated for validation in a test case and for computational efficiency on two simple networks. The CPF...

Does a more sophisticated storm erosion model improve probabilistic erosion estimates?

The dependency between the accuracy/uncertainty of storm erosion exceedance estimates obtained via a probabilistic model and the level of sophistication of the structural function (storm erosion model) embedded in the probabilistic model is assessed via the application of Callaghan et al.'s (2008) Joint Probability Model (JPM) at Narrabeen beach...

Probabilistic assessment of the performance of combined sewer systems