Variación de las características mecánicas de armaduras de alta ductilidad B500SD en función de su grado de corrosión / Mechanical properties variation of B500SD high ductility reinforcement regarding its corrosion degree

Los efectos de la corrosión sobre las armaduras se manifiestan por la pérdida de sección y la variación de las propiedades mecánicas relacionadas con la ductilidad. En este trabajo se han ensayado a tracción 96 barras de acero B500SD que previamente se han sometido a niveles variables de corrosión. Los resultados muestran que los alargamientos de las barras disminuyen y el cociente entre la tensión máxima y el límite elástico aumenta conforme el nivel de corrosión avanza. A partir del estudio del efecto de entalla y de la distinta constitución metalográfica del acero a nivel de sección debido a su procedimiento de fabricación se pueden explicar los fenómenos anteriores.

Exploring differences between phonetic classes in Sleep Apnoea Syndrome Patients using automatic speech processing techniques

This work is part of an on-going collaborative project between the medical and signal processing communities to promote new research efforts on automatic OSA (Obstructive Apnea Syndrome) diagnosis. In this paper, we explore the differences noted in phonetic classes (interphoneme) across groups (control/apnoea) and analyze their utility for OSA detection

Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains.

Diffusion Gradient Temporal Difference for Cooperative Reinforcement Learning with Linear Function Approximation

We introduce a diffusion-based algorithm in which multiple agents cooperate to predict a common and global statevalue function by sharing local estimates and local gradient information among neighbors. Our algorithm is a fully distributed implementation of the gradient temporal difference with linear function approximation, to make it applicable to multiagent settings. Simulations illustrate the benefit of cooperation in learning, as made possible by the proposed algorithm.

Design equations for reinforced concrete members strengthened in shear with external FRP reinforcement formulated in an evolutionary multi-objective framework

Methods for predicting the shear capacity of FRP shear strengthened RC beams assume the traditional approach of superimposing the contribution of the FRP reinforcing to the contributions from the reinforcing steel and the concrete. These methods become the basis for most guides for the design of externally bonded FRP systems for strengthening concrete structures. The variations among them come from the way they account for the effect of basic shear design parameters on shear capacity. This paper presents a simple method for defining improved equations to calculate the shear capacity of reinforced concrete beams externally shear strengthened with FRP. For the first time, the equations are obtained in a multiobjective optimization framework solved by using genetic algorithms, resulting from considering simultaneously the experimental results of beams with and without FRP external reinforcement. The performance of the new proposed equations is compared to the predictions with some of the current shear design guidelines for strengthening concrete structures using FRPs. The proposed procedure is also reformulated as a constrained optimization problem to provide more conservative shear predictions.

The online case study development for the teaching of building structures reinforcement

The Reinforcement of Building Structures is one of the topics of the Master in Building Innovation Technology (MBIT) of Universidad Politécnica de Madrid (UPM). Since the beginning of the delivery of this master, case studies have been chosen as the teaching methodology. For the 2011-2012 course the online education of this subject was implemented, instead of the classical learning based on attendance. Through ICT’s (Information and Communication Technologies) students are provided with much more and more selective information than through the classical learning. ICT’s can be used for search, enquiries and reporting. Using the online tools has been proved, through the results obtained and based on the surveys made amongst students, to be a successful experience.

An experimental and numerical study of the pattern of cracking of concrete due to steel reinforcement corrosion

In this work, cracking of concrete due to steel reinforcement corrosion is experimentally and numerically studied. The tests combined accelerated corrosion—to generate the cracks—with impregnation under vacuum with resin containing fluorescein—to enhance their visibility under ultraviolet light. In parallel, a model—called expansive joint element—was developed to simulate the expansion of the oxide and finite elements with an embedded adaptable cohesive crack were used to describe concrete cracking. The results show that a good agreement exists between the experimental and numerical crack patterns, which constitutes promising progress towards a comprehensive understanding of corrosion-induced cracking in reinforced concrete.

Evaluating the reinforcement of inorganic fullerene-like nanoparticles in thermoplastic matrices by depth-sensing indentation

The reinforcing effect of inorganic fullerene-like tungsten disulfide (IF-WS2) nanoparticles in two different polymer matrices, isotactic polypropylene (iPP) and polyphenylene sulfide (PPS), has been investigated by means of dynamic depth-sensing indentation. The hardness and elastic modulus enhancement upon filler addition is analyzed in terms of two main contributions: changes in the polymer matrix nanostructure and intrinsic properties of the filler including matrix-particle load transfer. It is found that the latter mainly determines the overall mechanical improvement, whereas the nanostructural changes induced in the polymer matrix only contribute to a minor extent. Important differences are suggested between the mechanisms of deformation in the two nanocomposites, resulting in a moderate mechanical enhancement in case of iPP (20% for a filler loading of 1%), and a remarkable hardness increase in case of PPS (60% for the same filler content). The nature of the polymer amorphous phase, whether in the glassy or rubbery state, seems to play here an important role. Finally, nanoindentation and dynamic mechanical analysis measurements are compared and discussed in terms of the different directionality of the stresses applied.