Machine learning-based methods in structural reliability analysis: A review
Structural Reliability analysis (SRA) is one of the prominent fields in civil and mechanical
engineering. However, an accurate SRA in most cases deals with complex and costly …
engineering. However, an accurate SRA in most cases deals with complex and costly …
[HTML][HTML] Geotechnical uncertainty, modeling, and decision making
Modeling only constitutes one aspect of decision making. The prevailing limitation of
applying modeling to practice is the absence of explicit consideration of uncertainties. This …
applying modeling to practice is the absence of explicit consideration of uncertainties. This …
Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review
S Kabir, Y Papadopoulos - Safety science, 2019 - Elsevier
Abstract System safety, reliability and risk analysis are important tasks that are performed
throughout the system life-cycle to ensure the dependability of safety-critical systems …
throughout the system life-cycle to ensure the dependability of safety-critical systems …
Subset simulation with adaptable intermediate failure probability for robust reliability analysis: An unsupervised learning-based approach
Y Zhao, Z Wang - Structural and Multidisciplinary Optimization, 2022 - Springer
Subset simulation (SS) was known for its computational efficiency in estimating small failure
probabilities as well as reducing emulation demands. The main idea behind SS lies in …
probabilities as well as reducing emulation demands. The main idea behind SS lies in …
Sampling methods for solving Bayesian model updating problems: A tutorial
This tutorial paper reviews the use of advanced Monte Carlo sampling methods in the
context of Bayesian model updating for engineering applications. Markov Chain Monte …
context of Bayesian model updating for engineering applications. Markov Chain Monte …
State-of-the-art review on Bayesian inference in structural system identification and damage assessment
Bayesian inference provides a powerful approach to system identification and damage
assessment for structures. The application of Bayesian method is motivated by the fact that …
assessment for structures. The application of Bayesian method is motivated by the fact that …
Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
In the context of modern engineering, environmental, and societal concerns, there is an
increasing demand for methods able to identify rational management strategies for civil …
increasing demand for methods able to identify rational management strategies for civil …
On the value of monitoring information for the structural integrity and risk management
S Thöns - Computer‐Aided Civil and Infrastructure Engineering, 2018 - Wiley Online Library
This article introduces an approach and framework for the quantification of the value of
structural health monitoring (SHM) in the context of the structural risk and integrity …
structural health monitoring (SHM) in the context of the structural risk and integrity …
[HTML][HTML] Modelling of spatial variability of soil undrained shear strength by conditional random fields for slope reliability analysis
Conditional random field model can make best use of limited site investigation data to
properly characterize the spatial variation of soil properties. This paper aims to propose a …
properly characterize the spatial variation of soil properties. This paper aims to propose a …
Efficient probabilistic back analysis of spatially varying soil parameters for slope reliability assessment
The probability distributions of soil parameters can be updated with limited site-specific
information via probabilistic back analyses. The updated probability distributions can be …
information via probabilistic back analyses. The updated probability distributions can be …