1,721,178 research outputs found
Interoperability between BIM and FEM for vibration-based model updating of a pedestrian bridge
No full textFinite Element Model (FEM) updating is the procedure of minimizing errors between the experimental measurements and response simulated by FEMs. It can lead to more accurate and representative models useful to perform forecast analysis or detect initial damage thresholds for structures and infrastructure. The paper investigates the potentialities to carry out an automatic model updating through the interoperability between FEMs, Building Information Modeling (BIM), and experimentally vibration-based information. Indeed, these latter possess details and data (geometrical or mechanical) that could be automatically transferred in a numerical environment for structural modeling. The ability of this exchange is assessed by a methodology applied to a pedestrian walkway. The first path utilizes the geometrical data coming from a BIM model of the walkway to achieve three different levels of meshing. Consequently, three accurate finite element modeling have been pursued based on the achieved discretization. For each model, the accuracy and cost analysis has been evaluated considering the minimal distance between the main experimental modal parameters, identified from output-only dynamic tests, and the numerical ones, obtained after manual model updating. Additionally, a second path attempts to realize an automatic model updating through a simplified representative numerical system of the walkway implemented in Matlab. To this end, first, an opportune algorithm has been developed capable of processing the data and information from both BIM and experimental identification. Second, once the numerical model is realized, the potentiality of a modified Particle Swarm Optimization for improving the structural representativeness has been assessed. In particular, the usefulness of this approach could be related to a smart management system of the structures and infrastructure through a corresponding digital twin model
Instantaneous Brain-to-Heart Functional Assessment using Inhomogeneous Point-process Models: a Proof of Concept Study
No full textWe introduce a new computational model for estimating the directional brain-heart interplay (BHI) in an instantaneous fashion using inhomogeneous point processes. Brain dynamics is considered as the exogenous input to a bivariate model predicting the first-order moment of an inverse-Gaussian function characterizing heartbeat dynamics continuously. Transfer entropy using brain- and heartbeat-related parameters finally quantifies the functional interplay from the brain to the heart. Here, we preliminarily evaluate our framework by studying heart rate variability (HRV) and electroencephalographic (EEG) series from 12 healthy subjects undergoing a cold-pressor test. Results suggest that cortical dynamics regulates heartbeat with specific time delays in the 30-60s and 90-120s ranges
Base Metal Ion Extraction and Stripping from WEEE Leachate by Liquid-liquid Extraction
Full text linkAn Inhomogeneous Point-process Model for the Assessment of the Brain-to-Heart Functional Interplay: A Pilot Study
No full textWe propose a novel computational framework for the estimation of functional directional brain-to-heart interplay in an instantaneous fashion. The framework is based on inhomogeneous point-process models for human heartbeat dynamics and employs inverse-Gaussian probability density functions characterizing the timing of R-peak events. The instantaneous estimation of the functional directional coupling is based on the definition of point-process transfer entropy, which is here retrieved from heart rate variability (HRV) and Electroencephalography (EEG) power spectral series gathered from 12 healthy subjects undergoing significant sympathovagal changes induced by a cold-pressor test. Results suggest that EEG oscillations dynamically influence heartbeat dynamics with specific time delays in the 30-60s and 90-120s ranges, and through a functional activity over specific cortical regions
Identification of Natural and Forcing Frequencies through Noisy Measurements Acquired in Operational Conditions on a Hospital Building
Full text linkOperational modal analysis is a robust and practical approach to structural health monitoring which assumes white noise as input. Therefore, the accuracy of this method can be compromised when dealing with colored unknown excitations, in which, for example, harmonic loads induced by the operation of mechanical equipment, may affect the modal parameter estimation. This study aims to address the challenge of identifying both natural and forcing frequencies of a complex building by exploiting the potentiality of both the spectral kurtosis analysis and stochastic subspace identification technique. The first one is based on the evaluation of a statistical quantity characterized by low values when data are stationary and Gaussian and high values when specific frequencies and nonstationarity are present in the signals. It allows the detection of harmonics, transients, and repetitive impulses in the frequency domain. Its combined use with the stochastic subspace identification technique enables us to effectively identify and separate harmonic-induced vibrations from structural response to ambient white noise. This approach can lead to a more accurate modal parameter estimation that has been investigated in this work through numerical and experimental analyses carried out on the Cardinal Massaia hospital building in Asti, Italy. An experimental daily dynamic campaign has been carried out to acquire accelerations in operational conditions including disturbances due to machinery like elevators and air conditioners. The combined use of kurtosis analysis and stochastic subspace identification techniques has been used to process a large dataset of noisy measurements acquired in operational conditions. Five different measurement setups have been implemented, each one composed of 14 sensors. Notwithstanding the complexity of the case study under investigation for both the structural configuration and difficulties in the experimental data acquisition, this approach allowed to distinguish natural from forcing frequencies, highlighting its accuracy and robustness
Time-Resolved Brain-to-Heart Probabilistic Information Transfer Estimation Using Inhomogeneous Point-Process Models
Full text linkObjective: The quantification of functional brain-heart interplay through the dynamics of the central and autonomic nervous systems may provide effective biomarkers for cognitive, emotional, and autonomic state changes. Despite several computational models were proposed to this end, none provides a directional estimation of such interplay in a time-resolved and probabilistic fashion. Methods: In this study, a multivariate inhomogeneous point-process model for heartbeat dynamics is employed to derive subject-specific, time-resolved, functional estimates of the directional interplay occurring from the brain to the heart, whose activity is represented by electroencephalography and R-peaks intervals series. An inverse-Gaussian probability density function is used to predict heartbeat events as a function of neural dynamics, which is modeled as an exogenous input to the autoregressive cardiac dynamics. Results: The performance is evaluated using heart rate variability and electroencephalography series gathered from 24 healthy volunteers undergoing a cold-pressor test, and the modeling goodness-of-fit is assessed through the time-rescaling theorem. The results suggest that cortical dynamics drives heartbeat series with specific time delays in the range of 30s to 60s and 90s to 120s from the peripheral thermal stress onset. Conclusion: The proposed framework provides novel insights in human neurophysiology, exploiting a fully probabilistic definition of the continuous functional brain-heart interplay
Measured hospital building vibrations induced by air conditioning systems and elevators
Full text linkA recent large campaign of vibration measurements has been conducted on a hospital building in Italy to perform an operational modal analysis of the structure utilized for model updating. Subsequently, the updated model has been used to carry out a vulnerability analysis of the hospital structural system. The building is composed of 41 structural subsystems separated by technical joints. At the same time, several subsystems are differently interconnected, especially through a roof sustained by large-span wooden beams. Long acceleration time histories have been measured by wireless sensors located at the top level of the building in two days of testing. During the tests conducted in operational conditions, in different locations of the building air conditioning systems and elevators were functioning and they were affecting the measures in the high-frequency content. This work aims to investigate the structural identification of the complex structure, the effectiveness of technical joints, and the influence of the wooden roof connection on the dynamic structural behavior. In pursuing these goals, the main challenge is considering the influence of air conditioning and elevator systems on the natural frequency identification of the structure. Therefore, based on the location of the sensors, the similarities in the geometry, and the vicinity of the sensors to the mechanical equipment, all the sensors have been classified into different groups to observe the dynamic response. Furthermore, an Artificial Neural Network (ANN), trained on numerical model results, is applied to experimental data to establish a relation between increasing prediction error and the influence of the air conditioning system and elevator frequencies
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The recovery of rare earth metals from WEEE leaching solution via liquid-liquid extraction
No full textThe recovery of rare earth metals (REMs) from end-of-life products, such as Waste Electrical and Electronic Equipment (WEEE), is drawing great attention as an attractive strategy for promoting the sustainable development. The hydrometallurgical technique of solvent extraction has been reported to be one of the most interesting method to recover REMs. However, when applied to WEEE, this process is challenged by the heterogeneous composition of electronic waste, completely different from other solid matrices, and it still has much rooms of improvements. This study investigated the extraction, stripping and recovery of REMs from a WEEE leaching solution using Versatic 10 as carrier in the organic phase and oxalic acid as stripping agent. A factorial design was carried out to evaluate the simultaneous effects of factors as the feed phase pH and the concentrations of both extractant and organic phase modifier in the extraction process. Cerium, lanthanum and yttrium were extracted at high percentages using 200 mM of Versatic 10, loaded by 100 mM of TBP in kerosene at pH 7. Moreover, 750 mM of oxalic acid successfully stripped and recovered 7.63 and 13.82 mg/kg of lanthanum and yttrium, respectively
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