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11	Predicción de la resistencia a la compresión del concreto usando redes neuronales artificiales Bernilla Rodriguez, David Henry January 2024 (has links) El concreto es el material de construcción más ampliamente utilizado en la actualidad debido a su excepcional capacidad para resistir fuerzas de compresión, comúnmente denominadas como f'c. La obtención del valor de f'c del concreto involucra la realización de diversos ensayos, siendo el ensayo a compresión simple o uniaxial en probetas de concreto el más comúnmente empleado, evaluando la resistencia a diferentes intervalos de tiempo. Lamentablemente, estas probetas suelen ser desechadas al aire libre, contribuyendo a la contaminación ambiental. En esta investigación, se enfoca en la predicción del valor de f'c del concreto a los 28 días mediante un modelo predictivo basado en redes neuronales artificiales. Los datos de entrada comprenden propiedades de los agregados, tipo de cemento y las proporciones de sus componentes, como agua, cemento y agregados. El único dato de salida es el valor real de f'c obtenido en el ensayo de compresión simple. Estos datos se recopilaron de varios laboratorios en el norte de Perú. La red neuronal se construyó utilizando TensorFlow de Google, con dos capas ocultas que constan de 16 y 8 neuronas respectivamente, y se entrenó durante 450 épocas. Se obtuvo una exactitud en la predicción mayor al 90% en el rango de 210 a 335 kg/cm². / Concrete is currently the most widely used construction material due to its exceptional ability to withstand compressive forces, commonly referred to as CS. Determining the CS value of concrete involves conducting various tests, with the uniaxial or simple compression test on concrete specimens being the most employed, assessing resistance at different time intervals. Unfortunately, these test specimens are often discarded outdoors, contributing to environmental pollution. This research focuses on predicting the CS value of concrete at 28 days using a predictive model based on artificial neural networks. Input data include aggregate properties, cement type, and their component proportions such as water, cement, and aggregates. The only output data is the actual CS value obtained from the simple compression test. This data was collected from multiple laboratories in northern Peru. The neural network was constructed using Google's TensorFlow, with two hidden layers consisting of 16 and 8 neurons, respectively, and trained for 450 epochs. Prediction accuracy exceeded 90% in the range of 210 to 335 kg/ cm². Redes neuronales artificiales Materiales de construcción sostenibles Artificial neural networks Concrete compressive strength Sustainable construction materials
12	Stavební průzkum a hodnocení stavu konstrukce / Building survey and evaluating the state of construction Štěpánek, Jakub January 2018 (has links) This diploma thesis is focused on building survey and evaluating the state of existing constructions. Process of evaluating the state of existing constructions and selected diagnostic methods are described in the theoretical part. The practical part is dealing with performed structural and technical survey of two industrial structures including evaluation of the strength of concrete and verification of the reinforcement. The final part includes assessment of both of the evaluated structures.
13	Stavební průzkum a diagnostika konstrukce / Survey and Diagnostics of Building Construction Řezáč, David January 2021 (has links) The diploma thesis deals with the construction and technical survey of a reinforced concrete structure. The theoretical part describes the tested properties of concrete and the methodology of their tests used in the practical part. The practical part is devoted to the diagnosis of the existing structure, which was determined the strength of concrete using core holes, concrete adhesion by tear tests, and reinforcement of reinforced concrete elements of the structure determined by ground-penetrating radar, electromagnetic indicators and additional chopped probes.
14	Study of Bond Behavior at Rebar and Concrete Interface through Beam-end Specimens with Consideration of Corrosion Hauff, Derek Allen Johnson 01 May 2022 (has links) No description available. Civil Engineering
15	Fracture Characteristics Of Self Consolidating Concrete Naddaf, Hamid Eskandari 07 1900 (has links) Self-consolidating concrete (SCC) has wide use for placement in congested reinforced concrete structures in recent years. SCC represents one of the most outstanding advances in concrete technology during the last two decades. In the current work a great deal of cognizance pertaining to mechanical properties of SCC and comparison of fracture characteristics of notched and unnotched beams of plain concrete as well as using acoustic emission to understand the localization of crack patterns at different stages has been done. An artificial neural network (ANN) is proposed to predict the 28day compressive strength of a normal and high strength of SCC and HPC with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Fracture characteristics of notched and unnotched beams of plain self consolidating concrete using acoustic emission to understand the localization of crack patterns at different stages has been done. Considering this as a platform, further analysis has been done using moment tensor analysis as a new notion to evaluate fracture characteristics in terms of crack orientation, direction of crack propagation at nano and micro levels. Analysis of B-value (b-value based on energy) is also carried out, and this has introduced to a new idea of carrying out the analysis on the basis of energy which gives a clear picture of results when compared with the analysis carried out using amplitudes. Further a new concept is introduced to analyze crack smaller than micro (could be hepto cracks) in solid materials. Each crack formation corresponds to an AE event and is processed and analyzed for crack orientation, crack volume at hepto and micro levels using moment tensor analysis based on energy. Cracks which are tinier than microcracks (could be hepto), are formed in large numbers at very early stages of loading prior to peak load. The volume of hepto and micro cracks is difficult to measure physically, but could be characterized using AE data in moment tensor analysis based on energy. It is conjectured that the ratio of the volume of hepto to that of micro could reach a critical value which could be an indicator of onset of microcracks after the formation of hepto cracks. Concrete - Fracture Mechanics Self-Consolidating Concrete (SCC) Plain Concrete Beams - Fracture Plain Concrete Beams - Acoustic Emission Self-Consolidating Concrete - Properties Artificial Neural Network (ANN) Fly Ash Un-notched Beams Notched Beams Hepto Cracks Microcracks Structural Engineering
16	Studium efektu aplikace celulózových vláken v cementových kompozitech / Study of the effect of the application of cellulose fibers in cementitious composites Dvořák, Richard January 2016 (has links) This diploma thesis is focused on composition and processing of cellulose fibers and their usage in cementitious composites. It describes key attributes of cellulose fibers, which has to be acquired for effective use in cement composites. There are stated effects of cellulose fibers on concrete properties, such as workability, physical and mechanic attributes and freeze-thaw resistance. In experimental part is designed a mixture with portion of cellulose fibers. There has been performed various tests to define the effect of cellulose fibers. There was designed experiment to determine its alkali resistance for fibers itself, and possible changes in microstructure of fibers were analyzed by optic and electron scanning microscope.
17	Stavební průzkum a hodnocení stavu konstrukce / Building survey and evaluating the state of construction Broďák, Marek January 2019 (has links) This diploma thesis is focused on building survey and evaluating the state of existing constructions. In theoretical part are described the process of evaluating the state of existing constructions and selected diagnostic methods. The practical part is dealing with performed structural and technical survey of industrial object including evaluation of the strength of concrete and verification of the reinforcement. The last part is focused on a static assessment of selected supporting element of the structure.
18	Stavební průzkum a hodnocení stavu konstrukce / Building survey and evaluating the state construction Nguyen, Martin January 2017 (has links) This diploma thesis deal with the building survey and evaluating the state of existing constructions. In theoretical part are described the types of civil engineering surveys and diagnostic methods for conducting a survey of reinforced concrete structures. The practical part deal with describes the structural and technical survey of the production hall in the industrial area Fatra Napajedla, evaluating the strength of concrete on the existing structure through donations cores and verification of compliance with the reinforcement according to project documentation. The last part is focused on a static assessment of selected supporting elements of the structure.
19	Vliv provzdušnění a ošetřování na vývoj modulu pružnosti betonu / Influence of Air Entrainment and Curing on Development of Elastic Modulus of Concrete Bartulíková, Radka January 2013 (has links) Master's thesis deals with influence of various factors on the concrete compressive strength and modulus of elasticity. This work concentrates mainly on the influence of early curing and air entrainment of concrete. One part of this work is the evaluation of laboratory measurement, which was carried out on cubes of standard and norm prisms. The development of elastic moduli was monitored in time to 730 days of age with respect to air entrainment and curing conditions. The second part of the experiment deals with a comparison of theoretical creep curves depending on the value of the modulus of elasticity based on different standards. Again, the air entrainment effect and influence of initial conditions are reflected.
20	Application of the Duality Theory Lorenz, Nicole 15 August 2012 (has links) (PDF) The aim of this thesis is to present new results concerning duality in scalar optimization. We show how the theory can be applied to optimization problems arising in the theory of risk measures, portfolio optimization and machine learning. First we give some notations and preliminaries we need within the thesis. After that we recall how the well-known Lagrange dual problem can be derived by using the general perturbation theory and give some generalized interior point regularity conditions used in the literature. Using these facts we consider some special scalar optimization problems having a composed objective function and geometric (and cone) constraints. We derive their duals, give strong duality results and optimality condition using some regularity conditions. Thus we complete and/or extend some results in the literature especially by using the mentioned regularity conditions, which are weaker than the classical ones. We further consider a scalar optimization problem having single chance constraints and a convex objective function. We also derive its dual, give a strong duality result and further consider a special case of this problem. Thus we show how the conjugate duality theory can be used for stochastic programming problems and extend some results given in the literature. In the third chapter of this thesis we consider convex risk and deviation measures. We present some more general measures than the ones given in the literature and derive formulas for their conjugate functions. Using these we calculate some dual representation formulas for the risk and deviation measures and correct some formulas in the literature. Finally we proof some subdifferential formulas for measures and risk functions by using the facts above. The generalized deviation measures we introduced in the previous chapter can be used to formulate some portfolio optimization problems we consider in the fourth chapter. Their duals, strong duality results and optimality conditions are derived by using the general theory and the conjugate functions, respectively, given in the second and third chapter. Analogous calculations are done for a portfolio optimization problem having single chance constraints using the general theory given in the second chapter. Thus we give an application of the duality theory in the well-developed field of portfolio optimization. We close this thesis by considering a general Support Vector Machines problem and derive its dual using the conjugate duality theory. We give a strong duality result and necessary as well as sufficient optimality conditions. By considering different cost functions we get problems for Support Vector Regression and Support Vector Classification. We extend the results given in the literature by dropping the assumption of invertibility of the kernel matrix. We use a cost function that generalizes the well-known Vapnik's ε-insensitive loss and consider the optimization problems that arise by using this. We show how the general theory can be applied for a real data set, especially we predict the concrete compressive strength by using a special Support Vector Regression problem. Konjugierte Dualität konvexe Analysis Fencheldualität Lagrangedualität Regularitätsbedingungen schwache Dualität starke Dualität Portfoliooptimierung innere Punktbedingungen Regularitätsbedingungen Risikomaße Support Vektor Regression Suport Vektor Klassifikation Machinelles Lernen Conjugate duality convex duality theory Fenchel duality Lagrange duality Fenchel-Lagrange duality regularity conditions perturbation functions composed convex optimization problems geometric constraints cone constraints weak and strong duality scalar optimization interior point regularity condition conjugate functions convex risk measures convex deviation measures portfolio optimization optimality conditions stochastic programming chance constraints machine learning Tikhonov regularization loss functions Support Vector Regression Support Vector Classification concrete compressive strength ddc:515 Maschinelles Lernen Konjugierte Dualität konvexe Analysis

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