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This paper presents a Hierarchical Bayesian model updating framework to account for the effects of ambient temperature and excitation amplitude.
Both the structural and the acoustic parameters addressing the stiffness as well as the damping modeling inaccuracies need to be considered simultaneously in the model updating framework in order to obtain an accurate estimate of these parameters.
To apply the resource model updating framework, preparation of input data is required (Fig. 1).
As mentioned earlier, the first data set required to apply the resource model updating framework is a collection of the resource model realizations, also called the prior model.
The aim is to improve the knowledge over the coal deposit and increase the process performance by applying a resource model updating framework.
First, it aims to test the performance of the resource model updating framework while the sensor is observing a blend of coal resulting from multiple excavators.
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In the following paper a real lignite mining case study is presented in order to compare the updating frameworks' performance with a prior model based on conditional simulation and a prior model based on a short-term model.
The proposed framework synthesizes the buffeting analysis with distributed wind loads and the multi-scale modelling and model updating of long-span bridges.
The proposed framework makes use of the advantages of the Bayesian model updating and the VBDIT, and compensates for their drawbacks.
In this work, a damage identification methodology is presented which allows combining static and dynamic measurements within a model updating procedure posed in a multi-objective framework solved by using evolutionary algorithms.
The use of longitudinal follow-up data in conjunction with model updating procedures, such as the Bayesian estimation procedure described here, will allow an analytical framework to achieve this objective.
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