Zobrazeno 1 - 10
of 662
pro vyhledávání: '"STRAUB, DANIEL"'
The digital twin approach has gained recognition as a promising solution to the challenges faced by the Architecture, Engineering, Construction, Operations, and Management (AECOM) industries. However, its broader application across AECOM sectors rema
Externí odkaz:
http://arxiv.org/abs/2412.09432
Sequential directional importance sampling (SDIS) is an efficient adaptive simulation method for estimating failure probabilities. It expresses the failure probability as the product of a group of integrals that are easy to estimate, wherein the firs
Externí odkaz:
http://arxiv.org/abs/2410.21350
Branch and bound algorithms have been developed for reliability analysis of coherent systems. They exhibit a set of advantages; in particular, they can find a computationally efficient representation of a system failure or survival event, which can b
Externí odkaz:
http://arxiv.org/abs/2410.22363
We present a new surrogate model for emulating the behavior of complex nonlinear dynamical systems with external stochastic excitation. The model represents the system dynamics in state space form through a sparse Kriging model. The resulting surroga
Externí odkaz:
http://arxiv.org/abs/2409.02462
Autor:
Papaioannou, Iason, Straub, Daniel
Global variance-based reliability sensitivity indices arise from a variance decomposition of the indicator function describing the failure event. The first-order indices reflect the main effect of each variable on the variance of the failure event an
Externí odkaz:
http://arxiv.org/abs/2403.12822
Autor:
Kamariotis, Antonios, Chatzi, Eleni, Straub, Daniel, Dervilis, Nikolaos, Goebel, Kai, Hughes, Aidan J., Lombaert, Geert, Papadimitriou, Costas, Papakonstantinou, Konstantinos G., Pozzi, Matteo, Todd, Michael, Worden, Keith
Publikováno v:
DCE 5 (2024) e27
To maximize its value, the design, development and implementation of Structural Health Monitoring (SHM) should focus on its role in facilitating decision support. In this position paper, we offer perspectives on the synergy between SHM and decision-m
Externí odkaz:
http://arxiv.org/abs/2402.00021
We propose a novel Deep Reinforcement Learning (DRL) architecture for sequential decision processes under uncertainty, as encountered in inspection and maintenance (I&M) planning. Unlike other DRL algorithms for (I&M) planning, the proposed +RQN arch
Externí odkaz:
http://arxiv.org/abs/2312.14824
We employ the Bayesian improved cross entropy (BiCE) method for rare event estimation in static networks and choose the categorical mixture as the parametric family to capture the dependence among network components. At each iteration of the BiCE met
Externí odkaz:
http://arxiv.org/abs/2309.12490
Rare event simulation and rare event probability estimation are important tasks within the analysis of systems subject to uncertainty and randomness. Simultaneously, accurately estimating rare event probabilities is an inherently difficult task that
Externí odkaz:
http://arxiv.org/abs/2308.04971
Autor:
Arcieri, Giacomo, Hoelzl, Cyprien, Schwery, Oliver, Straub, Daniel, Papakonstantinou, Konstantinos G., Chatzi, Eleni
Partially Observable Markov Decision Processes (POMDPs) can model complex sequential decision-making problems under stochastic and uncertain environments. A main reason hindering their broad adoption in real-world applications is the lack of availabi
Externí odkaz:
http://arxiv.org/abs/2307.08082