Fault Trees, Decision Trees, and Binary Decision Diagrams:A systematic comparison

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Automatic inference of fault tree models via multi-objective evolutionary algorithms

Fault tree analysis is a well-known technique in reliability engineering and risk assessment, which supports decision-making processes and the management of complex systems. Traditionally, fault tree (FT) models are built manually together with domain experts, considered a time-consuming process prone to human errors. With Industry 4.0, there is an increasing availability of inspection and monitoring data, making techniques that enable knowledge extraction from large data sets relevant. Thus, our goal with this work is to propose a data-driven approach to infer efficient FT structures that achieve a complete representation of the failure mechanisms contained in the failure data set without human intervention. Our algorithm, the FT-MOEA, based on multi-objective evolutionary algorithms, enables the simultaneous optimization of different relevant metrics such as the FT size, the error computed based on the failure data set and the Minimal Cut Sets. Our results show that, for six case studies from the literature, our approach successfully achieved automatic, efficient, and consistent inference of the associated FT models. We also present the results of a parametric analysis that tests our algorithm for different relevant conditions that influence its performance, as well as an overview of the data-driven methods used to automatically infer FT models

Deterioration modeling of sewer pipes via discrete-time Markov chains: A large-scale case study in the Netherlands

[For the latest version of this repository go to: https://gitlab.utwente.nl/fmt/degradation-models/dtmc_sewer_pipes.git] Sewer pipe network systems are an important part of civil infrastructure, and in order to find a good trade-off between maintenance costs and system performance, reliable sewer pipe degradation models are essential. In this paper, we present a large-scale case study in the city of Breda in the Netherlands. Our dataset has information on sewer pipes built since the 1920s and contains information on different covariates. We also have several types of damage, but we focus our attention on infiltrations, surface damage, and cracks. Each damage has an associated severity index ranging from 1 to 5. To account for the characteristics of sewer pipes, we defined 6 cohorts of interest. Two types of discrete-time Markov chains (DTMC), which we called Chain `Multi' and `Single' (where Chain `Multi'contains additional transitions compared to Chain `Single'), are commonly used to model sewer pipe degradation at the pipeline level, and we want to evaluate which suits better our case study. To calibrate the DTMCs, we define an optimization process using Sequential Least-Squares Programming to find the DTMC parameter that best minimizes the root mean weighted square error. Our results show that for our case study there is no substantial difference between Chain `Multi' and `Single', but the latter has fewer parameters and can be easily trained. Our DTMCs are useful to compare the cohorts via the expected values, e.g., concrete pipes carrying mixed and waste content reach severe levels of surface damage more quickly compared to concrete pipes carrying rainwater, which is a phenomenon typically identified in practice.This research has been partially funded by NWO under the grant PrimaVera (https://primavera-project.com) number NWA.1160.18.238, and has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101008233

Instability of Thin Concrete Walls with a Single Layer of Reinforcement under Cyclic Loading: Numerical Simulation and Improved Equivalent Boundary Element Model for Assessment

Thin reinforced concrete walls may fail due to out-of-plane instability when subjected to seismic loading. While previous numerical studies on wall instability have focused on the behaviour of members with two layers of vertical reinforcement, this work addresses the response of walls with a single layer of rebars. Such walls are particular prone to out-of-plane failure when subjected to cyclic in-plane loading. The numerical investigations herein performed simulate the aforementioned local behaviour and validate it against experimental measurements. A parametric study on the effect of boundary conditions shows that imposing an out-of-plane displacement or a rotation at the storey height increases the vulnerability to instability. It is also seen that the storey height itself is an influencing variable. The second part of this study proposes an improved equivalent boundary element model for the assessment of wall instability. Existing mechanical models, based on pinned-pinned boundary conditions, represent the boundary element over the height of the plastic hinge. This work shows that such models often underestimate the critical tensile strain triggering out-of-plane failure. A new equivalent boundary element model is proposed where a bilinear axial displacement profile defined a priori is applied. The latter is shown to satisfactorily approximate the vertical strain profile in wall boundary elements and to lead to better estimates of the critical strain triggering out-of-plane failure