Accuracy of models of confined concrete in rectangular columns using different proposals for the prediction of failure of the FRP

Confinement with externally applied fiber reinforced polymers (FRP), such as carbon, glass and aramid-based composites, results in notorious improvement of ductility and strength. Several constitutive models, regarding stress–strain relationship, have been proposed. However, few models exist for square and rectangular columns confined with FRP when compared with the number of models for circular concrete columns, and even fewer models satisfactorily predict the failure strain of FRP. In this paper, the accuracy of existing models for the prediction of the failure strain of the FRP is evaluated. Comparison of analytical results with experimental test results of concrete columns reported in the literature is presented, focusing different parameters such as strength, maximum strain and strain energy density.info:eu-repo/semantics/publishedVersio

Accuracy of models of concrete in circular columns using different proposals for the prediction of failure of the confining FRP

Confinement of concrete columns with fiber reinforced polymers results in an increase of strength and ductility. For this reason, the use of aramid, carbon and glass-based composites for confinement of reinforced concrete columns has significantly increased over the last decades. Nevertheless, few models adequately predict the failure strain of the fiber reinforced polymer, which has a determinant influence on the computed results. In this paper the accuracy of existing models of confined concrete using different proposals for the prediction of the failure strain of the confining composite is assessed. This is based on the comparison of analytical results with experimental test results of concrete columns with circular cross-section reported in the literature. The comparison focusses on different parameters such as strength, maximum strain and strain energy density.info:eu-repo/semantics/publishedVersio

Dynamical generation of wormholes with charged fluids in quadratic Palatini gravity

The dynamical generation of wormholes within an extension of General Relativity (GR) containing (Planck's scale-suppressed) Ricci-squared terms is considered. The theory is formulated assuming the metric and connection to be independent (Palatini formalism) and is probed using a charged null fluid as a matter source. This has the following effect: starting from Minkowski space, when the flux is active the metric becomes a charged Vaidya-type one, and once the flux is switched off the metric settles down into a static configuration such that far from the Planck scale the geometry is virtually indistinguishable from that of the standard Reissner-Nordstr\"om solution of GR. However, the innermost region undergoes significant changes, as the GR singularity is generically replaced by a wormhole structure. Such a structure becomes completely regular for a certain charge-to-mass ratio. Moreover, the nontrivial topology of the wormhole allows to define a charge in terms of lines of force trapped in the topology such that the density of lines flowing across the wormhole throat becomes a universal constant. To the light of our results we comment on the physical significance of curvature divergences in this theory and the topology change issue, which support the view that space-time could have a foam-like microstructure pervaded by wormholes generated by quantum gravitational effects.Comment: 14 pages, 3 figures, revtex4-1 style. New content added on section VI. Other minor corrections introduced. Final version to appear in Phys. Rev.

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.This work has been partially supported by the EU project iDev40. This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783163. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Germany, Belgium, Italy, Spain, Romania. It has also been supported by the Basque Government (Spain) through the project VIRTUAL (KK-2018/00096), and by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P)

Rank Aggregation for Non-stationary Data Streams

The problem of learning over non-stationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the non-stationarity means that the distribution of preferences changes over time. We propose an algorithm that learns the current distribution of ranking in an online manner. The bottleneck of this process is a rank aggregation problem. We propose a generalization of the Borda algorithm for non-stationary ranking streams. As a main result, we bound the minimum number of samples required to output the ground truth with high probability. Besides, we show how the optimal parameters are set. Then, we generalize the whole family of weighted voting rules (the family to which Borda belongs) to situations in which some rankings are more reliable than others. We show that, under mild assumptions, this generalization can solve the problem of rank aggregation over non-stationary data streams.This work is partially funded by the Industrial Chair “Data science & Artificial Intelligence for Digitalized Industry & Services” from Telecom Paris (France), the Basque Government through the BERC 2018–2021 and the Elkartek program (KK-2018/00096, KK-2020/00049), and by the Spanish Government excellence accreditation Severo Ochoa SEV-2013-0323 (MICIU) and the project TIN2017-82626-R (MINECO). J. Del Ser also acknowledges funding support from the Basque Government (Consolidated Research Gr. MATHMODE, IT1294-19)