Using recycled aggregates in the production of concrete has been a viable alternative for sustainable development. Notwithstanding advanced information on this material at room temperature, its behavior when exposed to fire is still incipient. Thus, based on experimental analyses, the objective of this article is to evaluate the behavior of concrete produced with recycled aggregates for thermal insulation of steel elements, as well as to verify the physical and mechanical properties of these mixtures. For this purpose, eight prototypes, one made of steel and the others coated with different types of concrete, conventional and with recycled aggregates, were inserted in a horizontal oven and heated for 2 h. Based on experimental tests, numerical models were proposed and tested using the ABAQUS computational code, with consistent results when coherent thermal properties were adopted. The experimental results show that recycled aggregate concrete (RAC) has great thermal insulation potential and sustainable benefits, considering that the steel elements coated with this type of material, with the exception of those that underwent spalling, presented temperatures close to or below compared with concrete with natural aggregates. In this regard, it is observed that the thermal conductivity of RACs was inferior to conventional concrete, indicating that this material is a promising strategy for thermal insulation of steel structures.
RESUMO: Os resultados de ensaios de caracterização de madeiras e derivados são dados de uma variável aleatória cuja população segue uma distribuição em geral desconhecida. Assim, para estimar os parâmetros de interesse, é necessário identificar a distribuição de probabilidade com melhor aderência aos dados experimentais. Nessa identificação, podem ser utilizados os métodos gráficos, sendo os gráficos de probabilidade os mais usados. Os pontos deste gráfico são determinados utilizando uma combinação dos métodos paramétricos e não paramétricos. A linha estimada por este gráfico é uma representação dos percentis dos dados experimentais, obtidos usando estatística de ordem, estimadores de máxima verossimilhança da distribuição de probabilidade com melhor aderência aos dados experimentais, e a função inversa da distribuição acumulada. Os resultados obtidos mostram que a combinação dos métodos paramétricos e não-paramétricos, permitem identificar a distribuição de probabilidade mais adequada, o que possibilita uma estimação mais precisa dos parâmetros, principalmente do valor característico que é de fundamental importância na área de madeira e derivados. Além disso, com os métodos apresentados é possível estimar um intervalo de confiança para este valor. ABSTRACT:Test results in wood and wood products characterization are data of a random variable which population distribution in general is unknown. Therefore, is necessary to identify the probability distribution that better goodness-of-fit the experimental data, to estimate the parameters of interest. In that identification, the graphic methods can be used, being the probability plot the most common. The points of this graph are determined using a combining parametric and non-parametric method. The fitted line for this graphical is the representation of the experimental data percentiles, which are obtained using order statistics, maximum likelihood estimate of probability distribution with better goodness-of-fit to the experimental data and the inverse cumulative distribution function. The obtained results show that combining parametric and non-parametric method, allow to identify which is the more appropriate probability distribution, that makes possible a more accuracy of the parameters, mainly of the characteristic value that is the fundamental importance in wood and wood products area. Moreover, with the presented methods is possible to estimate a confidence intervals for this value.
Timber-concrete composite beams are formed by the union of timber beams to reinforced concrete slabs through of shear connectors. When timber-concrete composite floors are compared to timber floors or reinforced concrete floors it is possible to highlight some advantages, including good performance in fire situations. When subjected to thermal actions, structural elements suffer strength and stiffness reductions, being, therefore, necessary to know the modifications suffered by each of its components, which for the case studied are: timber, concrete and shear connectors. Thus, it is developed a numerical modeling strategy using the computational program ABAQUS, which is based on the finite element method, for the study of timber-concrete composite beams in fire situation. In the first stage of the research it was carried out a numerical modeling of timber beam and timber-concrete composite beam at room temperature, finding good correlation between the force versus displacement curves in the middle of the span obtained numerically and through tests available in the literature. Then, it was carried out the calibration of the thermal and mechanical properties of the Brazilian wood, reaching numerical results close to the experimental ones, either in relation to the temperatures of the analyzed element or in relation to the vertical displacement curve as a function of the fire time. Finally, the thermo-structural modeling strategy developed for the timber-concrete composite beam provided a vertical displacement curve as a function of the fire time similar to the curve obtained through an analytical model available in the literature. Through of the elaborated model it was possible to observe that the load level increase reduces the resistance fire time of the structural element and that the thermal protection of the concrete is essential to increase the rupture time of the beam.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.