118,658 research outputs found
Reliability Updating of Offshore Wind Substructures by Use of Digital Twin information
This paper presents a probabilistic framework for updating the structural reliability of offshore wind turbine substructures based on digital twin information. In particular, the information obtained from digital twins is used to quantify and update the uncertainties associated with the structural dynamics and load modeling parameters in fatigue damage accumulation. The updated uncertainties are included in a probabilistic model for fatigue damage accumulation used to update the structural reliability. The updated reliability can be used as input to optimize decision models for operation and maintenance of existing structures and design of new structures. The framework is exemplified based on two numerical case studies with a representative offshore wind turbine and information acquired from previously established digital twins. In this context, the effect of updating soil stiffness and wave loading, which constitute two highly uncertain and sensitive parameters, is investigated. It is found that updating the soil stiffness significantly affects the reliability of the joints close to the mudline, while updating the wave loading significantly affects the reliability of the joints localized in the splash zone. The increased uncertainty related to virtual sensing, which is employed to update wave loading, reduces structural reliability
Communication Pattern Logic: Epistemic and Topological Views
We propose communication pattern logic. A communication pattern describes how
processes or agents inform each other, independently of the information
content. The full-information protocol in distributed computing is the special
case wherein all agents inform each other. We study this protocol in
distributed computing models where communication might fail: an agent is
certain about the messages it receives, but it may be uncertain about the
messages other agents have received. In a dynamic epistemic logic with
distributed knowledge and with modalities for communication patterns, the
latter are interpreted by updating Kripke models. We propose an axiomatization
of communication pattern logic, and we show that collective bisimilarity
(comparing models on their distributed knowledge) is preserved when updating
models with communication patterns. We can also interpret communication
patterns by updating simplicial complexes, a well-known topological framework
for distributed computing. We show that the different semantics correspond, and
propose collective bisimulation between simplicial complexes
Pre-posterior analysis of inspections incorporating degradation of concrete structures
The framework of pre-posterior decision analysis has a large potential as a decision support tool in structural
engineering. It seems ideally suited to tackle problems related to determining the value of Structural Health
Monitoring and is commonly applied in inspection and maintenance planning. However, the application of this
methodology for integrated life-cycle cost decision making related to monitoring of time-dependent and
spatial degradation phenomena in concrete structures, needs further investigation. In this work, the timedependent
and spatial degradation phenomena will be coupled to the pre-posterior decision making approach
and applied on concrete beams under bending, subjected to corrosion of the reinforcement. A framework is
set up to determine the value of information of inspections enabling adequate decision-making. The
methodology incorporates Bayesian updating based on the uncertain inspection outcomes. The framework
will be illustrated by application on a simply supported reinforced concrete beam
Bayesian update of the parameters of probability distributions for risk assessment in a two-level hybrid probabilistic-possibilistic uncertainty framework
International audienceRisk analysis models describing aleatory (i.e., random) events contain parameters (e.g., probabilities, failure rates, ...) that are epistemically uncertain, i.e., known with poor precision. Whereas probability distributions are always used to describe aleatory uncertainty, alternative frameworks of representation may be considered for describing epistemic uncertainty, depending on the information and data available. In this paper, we use possibility distributions to describe the epistemic uncertainty in the parameters of the (aleatory) probability distributions. We address the issue of updating, in a Bayesian framework, the possibilistic representation of the epistemical-ly-uncertain parameters of (aleatory) probability distributions as new information (e.g., data) becomes availa-ble. A purely possibilistic counterpart of the classical, well-grounded probabilistic Bayes theorem is adopted. The feasibility of the method is shown on a literature case study involving the risk-based design of a flood protection dike
How People Use Social Information to Find out What to Want in the Paradigmatic Case of Inter-temporal Preferences.
The weight with which a specific outcome feature contributes to preference quantifies a person's 'taste' for that feature. However, far from being fixed personality characteristics, tastes are plastic. They tend to align, for example, with those of others even if such conformity is not rewarded. We hypothesised that people can be uncertain about their tastes. Personal tastes are therefore uncertain beliefs. People can thus learn about them by considering evidence, such as the preferences of relevant others, and then performing Bayesian updating. If a person's choice variability reflects uncertainty, as in random-preference models, then a signature of Bayesian updating is that the degree of taste change should correlate with that person's choice variability. Temporal discounting coefficients are an important example of taste-for patience. These coefficients quantify impulsivity, have good psychometric properties and can change upon observing others' choices. We examined discounting preferences in a novel, large community study of 14-24 year olds. We assessed discounting behaviour, including decision variability, before and after participants observed another person's choices. We found good evidence for taste uncertainty and for Bayesian taste updating. First, participants displayed decision variability which was better accounted for by a random-taste than by a response-noise model. Second, apparent taste shifts were well described by a Bayesian model taking into account taste uncertainty and the relevance of social information. Our findings have important neuroscientific, clinical and developmental significance
Updating mining reserves with uncertainty data
In mining operations, the time delay between grade estimations and decision about the scheduling of stopes mining can result in seriously outdated information and, consequently, a substantial mined reserves bias. To mitigate this gap between the grade estimation of an orebody and its exploitation, this paper proposes a new method of speedily updating resources and reserves integrated into the concept of real-time mining. This consists in the continuous and swift update of mine reserves, which requires a continuous and fast stream of the measurements of stopes in an underground mine rather than the chemical lab analysis of core samples or chip/face samples. Here we propose using portable for the swift monitoring of ore grades. However, this “fast” data be highly uncertain. For this reason, the first step consists of creating a bidistribution function between “uncertain” XRF and the corresponding “hard” measurements, based on empirical historical data. Following this, the uncertainty of the XRF measurements is derived from those bi-distributions through the conditional distribution of real values given to the known XRF measurement.The second step involves updating the reserves by integrating this uncertain XRF data, which has been quantified by conditional distributions, in the grade characterization models. For this purpose, a stochastic simulation with point distributions is applied. A case study of a sulphide copper deposit illustrates the proposed methodology
Rational macroeconomic learning in linear expectational models
Abstract: The partial information rational expectations solution to a general linear multivariate expectational macro-model is found when agents are uncertain about the true values of the model’s parameters. Necessary and sufficient conditions for convergence to the full information rational expectations solution are given, and the core of an algorithm for the Bayesian updating of beliefs is provided. In the course of this a new class of full information rational expectations equilibria is described and some of its desirable properties proven.Rational Expectations; Partial information; Bayesian learning; Generalized Schur decomposition; Sunspots; Indeterminacy; Feasible Rational Expectations Equilibria
Bayesian Updating, Model Class Selection and Robust Stochastic Predictions of Structural Response
A fundamental issue when predicting structural response by using mathematical models is how to treat both modeling and excitation uncertainty. A general framework for this is presented which uses probability as a multi-valued
conditional logic for quantitative plausible reasoning in the presence of uncertainty due to incomplete information. The
fundamental probability models that represent the structure’s uncertain behavior are specified by the choice of a stochastic
system model class: a set of input-output probability models for the structure and a prior probability distribution over this set
that quantifies the relative plausibility of each model. A model class can be constructed from a parameterized deterministic
structural model by stochastic embedding utilizing Jaynes’ Principle of Maximum Information Entropy. Robust predictive
analyses use the entire model class with the probabilistic predictions of each model being weighted by its prior probability, or if
structural response data is available, by its posterior probability from Bayes’ Theorem for the model class. Additional robustness
to modeling uncertainty comes from combining the robust predictions of each model class in a set of competing candidates
weighted by the prior or posterior probability of the model class, the latter being computed from Bayes’ Theorem. This higherlevel application of Bayes’ Theorem automatically applies a quantitative Ockham razor that penalizes the data-fit of more
complex model classes that extract more information from the data. Robust predictive analyses involve integrals over highdimensional spaces that usually must be evaluated numerically. Published applications have used Laplace's method of
asymptotic approximation or Markov Chain Monte Carlo algorithms
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