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1. Parameters identification of cable stayed footbridges using Bayesian inference

   https://doi.org/doi.org/10.1007/s11012-019-01019-x  

   Chiara Pepi, C. ( August 2019 , Meccanica)

   Numerical modeling of actual structural systems is a very complex task mainly due to the lack of complete knowledge on the involved parameters. Simplified assumptions on the uncertain geometry, material properties and boundary conditions make the numerical model response differ from the actual structural response. Improvements of the finite element (FE) models to obtain accurate response predictions can be achieved by vibration based FE model updating which uses experimental measures to minimize the differences between the numerical and experimental modal features (i.e. natural frequencies and mode shapes). Within this context, probabilistic model updating procedures based on the Bayes’ theorem were recently proposed in the literature in order to take into account the uncertainties affecting the structural parameters and their influence on the structural response. In this paper, a novel framework to efficiently estimate the posterior marginal PDF of the selected model parameters is proposed. First, the main dynamic parameters to be used for model updating are identified by ambient vibration tests on an actual structural system. Second, a first numerical FE model is developed to perform initial sensitivity analysis. Third, a surrogate model based on polynomial chaos is calibrated on the initial FE model to significantly reduce computational costs. Finally, the posterior marginal PDFs of the chosen model parameters are estimated. The effectiveness of the proposed method is demonstrated using a FE numerical model describing a curved cable-stayed footbridge located in Terni (Umbria Region, Central Italy). 

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2. A Comparative Study of Frequency-domain Finite Element Updating Approaches Using Different Optimization Procedures

   DONG, Xinjun ; WANG, Yang ( July 2016 , EWSHM)

   In order to achieve a more accurate finite element (FE) model for an as-built structure, experimental data collected from the actual structure can be used to update selected parameters of the FE model. The process is known as FE model updating. This research compares the performance of two frequency-domain model updating approaches. The first approach minimizes the difference between experimental and simulated modal properties, such as natural frequencies and mode shapes. The second approach minimizes modal dynamic residuals from the generalized eigenvalue equation involving stiffness and mass matrices. Both model updating approaches are formulated as an optimization problem with selected updating parameters as optimization variables. This research also compares the performance of different optimization procedures, including a nonlinear least-square, an interior-point and an iterative linearization procedure. The comparison is conducted using a numerical example of a space frame structure. The modal dynamic residual approach shows better performance than the modal property difference approach in updating model parameters of the space frame structure. 

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3. Beta Probabilistic Databases: A Scalable Approach to Belief Updating and Parameter Learning

   https://doi.org/10.1145/3035918.3064026  

   Meneghetti, Niccolo' ; Kennedy, Oliver ; Gatterbauer, Wolfgang ( January 2017 , Proceedings of the 2017 ACM International Conference on Management of Data)

   Tuple-independent probabilistic databases (TI-PDBs) han- dle uncertainty by annotating each tuple with a probability parameter; when the user submits a query, the database de- rives the marginal probabilities of each output-tuple, assum- ing input-tuples are statistically independent. While query processing in TI-PDBs has been studied extensively, limited research has been dedicated to the problems of updating or deriving the parameters from observations of query results . Addressing this problem is the main focus of this paper. We introduce Beta Probabilistic Databases (B-PDBs), a general- ization of TI-PDBs designed to support both (i) belief updat- ing and (ii) parameter learning in a principled and scalable way. The key idea of B-PDBs is to treat each parameter as a latent, Beta-distributed random variable. We show how this simple expedient enables both belief updating and pa- rameter learning in a principled way, without imposing any burden on regular query processing. We use this model to provide the following key contributions: (i) we show how to scalably compute the posterior densities of the parameters given new evidence; (ii) we study the complexity of perform- ing Bayesian belief updates, devising efficient algorithms for tractable classes of queries; (iii) we propose a soft-EM algo- rithm for computing maximum-likelihood estimates of the parameters; (iv) we show how to embed the proposed algo- rithms into a standard relational engine; (v) we support our conclusions with extensive experimental results. 

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4. Structural health monitoring using extremely compressed data through deep learning

   https://doi.org/10.1111/mice.12517  

   Azimi, Mohsen ; Pekcan, Gokhan ( November 2019 , Computer-Aided Civil and Infrastructure Engineering)

   This study introduces a novel convolutional neural network (CNN)‐based approach for structural health monitoring (SHM) that exploits a form of measured compressed response data through transfer learning (TL)‐based techniques. The implementation of the proposed methodology allows damage identification and localization within a realistic large‐scale system. To validate the proposed method, first, a well‐known benchmark model is numerically simulated. Using acceleration response histories, as well as compressed response data in terms of discrete histograms, CNN models are trained, and the robustness of the CNN architectures is evaluated. Finally, pretrained CNNs are fine‐tuned to be adaptable for three‐parameter, extremely compressed response data, based on the response mean, standard deviation, and a scale factor. The performance of each CNN implementation is assessed using training accuracy histories as well as confusion matrices, along with other performance metrics. In addition to the numerical study, the performance of the proposed method is demonstrated using experimental vibration response data for verification and validation. The results indicate that deep TL can be implemented effectively for SHM of similar structural systems with different types of sensors.

    

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5. Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach

   https://doi.org/10.1111/biom.13122  

   Zheng, Qi ; Hong, Hyokyoung G. ; Li, Yi ( November 2019 , Biometrics)

   Conditional screening approaches have emerged as a powerful alternative to the commonly used marginal screening, as they can identify marginally weak but conditionally important variables. However, most existing conditional screening methods need to fix the initial conditioning set, which may determine the ultimately selected variables. If the conditioning set is not properly chosen, the methods may produce false negatives and positives. Moreover, screening approaches typically need to involve tuning parameters and extra modeling steps in order to reach a final model. We propose a sequential conditioning approach by dynamically updating the conditioning set with an iterative selection process. We provide its theoretical properties under the framework of generalized linear models. Powered by an extended Bayesian information criterion as the stopping rule, the method will lead to a final model without the need to choose tuning parameters or threshold parameters. The practical utility of the proposed method is examined via extensive simulations and analysis of a real clinical study on predicting multiple myeloma patients’ response to treatment based on their genomic profiles.

    

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