EEMCS final report for the causal modeling for air transport safety (CATS) project

This document reports on the work realized by the DIAM in relation to the completion of the CATS model as presented in Figure 1.6 and tries to explain some of the steps taken for its completion. The project spans over a period of time of three years. Intermediate reports have been presented throughout the project’s progress. These are presented in Appendix 1. In this report the continuous‐discrete distribution‐free BBNs are briefly discussed. The human reliability models developed for dealing with dependence in the model variables are described and the software application UniNet is presente

A 2-dimension dynamic Bayesian network for large-scale degradation modelling with an application to a bridges network

Modeling the stochastic evolution of a large-scale fleet or network generally proves to be challenging. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph-based models (e.g., Bayesian networks) have been developed recently to describe the behavior of single assets, one can find significantly fewer approaches addressing a fully integrated network. It is proposed an extension to the standard dynamic Bayesian network (DBN) by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates that translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution-free mathematical framework to parameterize the transition probabilities without previous data. This is achieved by borrowing from Cooke\u27s method for structured expert judgment and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate DBN where the focus is given on two specific types of configurations. The model is applied to a real-world example of steel bridge network in the Netherlands. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information

A 2-dimension dynamic Bayesian network for large-scale degradation modelling with an application to a bridges network

Modeling the stochastic evolution of a large-scale fleet or network generally proves to be challenging. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph-based models (e.g., Bayesian networks) have been developed recently to describe the behavior of single assets, one can find significantly fewer approaches addressing a fully integrated network. It is proposed an extension to the standard dynamic Bayesian network (DBN) by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates that translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution-free mathematical framework to parameterize the transition probabilities without previous data. This is achieved by borrowing from Cooke\u27s method for structured expert judgment and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate DBN where the focus is given on two specific types of configurations. The model is applied to a real-world example of steel bridge network in the Netherlands. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information

Bayesian Belief Nets and Vines in Aviation Safety and other Applications

Applied mathematicsElectrical Engineering, Mathematics and Computer Scienc

Cracking PwdHash: A Bruteforce Attack on Client-side Password Hashing

PwdHash is a widely-used tool for client-side password hashing. Originally released as a browser extension, it replaces the user’s password with a hash that combines both the password and the website’s domain. As a result, while the user only remembers a single secret, the passwords received are all unique for each site. We demonstrate how the hashcat password recovery tool can be extended to allow passwords generated using PwdHash to be identified and recovered, revealing the user’s master password. A leak from a single website can therefore compromise a user’s account on other sites where PwdHash was used. We describe the changes made to hashcat to support our approach, and explore the impact this has on speed of recovery.David Llewellyn-Jones thanks the European Research Council for funding this research through grant StG 307224 (Pico). Graham Rymer thanks the Cabinet Office/OCSIA for their financial support

Micropiling Two Bridges in Construction in the Motorway M-410 in Madrid (Spain)

International audienceMarkov-based models for predicting deterioration for civil infrastructures are widely recognized as suitable tools addressing this mechanism. The objective of this paper is to provide insights regarding a network of orthotropic steel bridges in terms of degradation. Consequently, a model combining a dynamic Bayesian network and a Markov chain is first introduced that builds up the network in a concise way. In an attempt to represent a network composed of two general classes of orthotropic steel bridges, the classical method of structured expert judgment is carried out as a quantification procedure. The first objective is to elicit indirectly transition probabilities for a Markov chain that describes how each bridge type deteriorates in time. Second, experts are asked to provide estimates on required conditional probabilities related to the Bayesian network. An in-depth analysis of the results is presented so that remarks and observations are subsequently pointed out and, finally conclusions are drawn

Maintenance decision model for steel bridges:a case in the Netherlands

A probabilistic model is developed to investigate the crack growth development in welded details of orthotropic bridge decks. Bridge decks may contain many of these vulnerable details and bridge reliability cannot always be guaranteed upon the attainment of a critical crack. Therefore, insight into the crack growth development is crucial in guaranteeing bridge reliability and scheduling efficient maintenance schemes. The probabilistic nature of the crack growth development model and the dependence of this model on many interdependent random variables result in significant uncertainties regarding model outcome. To reduce some of these uncertainties, the probabilistic model is combined with a monitoring system installed on a part of the bridge. In addition, a Bayesian network is used to determine the dependence structure between the different details (monitored and non-monitored) of the bridge. This dependence structure enables us to make more accurate crack growth predictions for all details of the bridge while monitoring only a limited number of those details and updating the remaining uncertainties

Expert judgment in life-cycle degradation and maintenance modelling for steel bridges

\u3cp\u3eMarkov-based models for predicting deterioration for civil infrastructures are widely recognized as suitable tools addressing this mechanism. The objective of this paper is to provide insights regarding a network of orthotropic steel bridges in terms of degradation. Consequently, a model combining a dynamic Bayesian network and a Markov chain is first introduced that builds up the network in a concise way. In an attempt to represent a network composed of two general classes of orthotropic steel bridges, the classical method of structured expert judgment is carried out as a quantification procedure. The first objective is to elicit indirectly transition probabilities for a Markov chain that describes how each bridge type deteriorates in time. Second, experts are asked to provide estimates on required conditional probabilities related to the Bayesian network. An in-depth analysis of the results is presented so that remarks and observations are subsequently pointed out and, finally conclusions are drawn.\u3c/p\u3

HANZE: Historical Analysis of Natural Hazards in Europe - Database documentation

This document describes the HANZE collection and its datasets in detail