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The compressive strength (fc) and Young’s modulus (Ec) of concretes are properties of great importance in civil engineering problems. To this day, despite the relevance of the subject, concretes are still designed based on charts and empirical formulae. This scenario is even more imprecise for lightweight aggregate concretes (LWAC), which contain less design methodologies and case studies available in the literature. In this sense, the present work presents a numerical simulation for predicting the properties of LWAC’s specimens using the Finite Element Method. The material was considered as biphasic, comprising lightweight aggregates and the enveloping mortar. Each phase was modelled with its own compressive strength, tensile strength and Young’s modulus. The achieved numerical results for fc and Ec were compared with their experimental counterparts, obtained from the literature. In total, 48 concrete formulations were assessed. Numerical results showed fair agreement with the experimental data. In general, the Mean Absolute Percentage Error (MAPE) was lower for the shale aggregates for both Young's modulus (1.75% versus 4.21% of expanded clay) and compressive strength (4.19% versus 9.89% of expanded clay). No clear trend of error was identified in relation to the aggregate proportion or to the mortar types, in which the MAPE varied from 2.36% to 8.13%. In conclusion, the simplification to spherical aggregates has shown satisfactory results, as has the adoption of a 2D model, which require less computational resources. Results encourage further applications with more complex geometrical aspects to improve the mix design and safety of LWAC.

This work presents a numerical approach for the automatic adjustment of parameters of the Mazars Damage Model, applied to a thermohydro- mechanical modeling of concrete structures. The procedure is based on a Scientific Workflow System (SWS) that addresses the combinatorial universe of adjustable parameters by minimizing the number of simulations required for optimized results. Not only does SWS improve efficiency, by also makes the strategy easier when compared to manual procedures. The adopted algorithm is developed in an intuitive script language and employs a distributed computational environment. Comparison to experimental data indicates that the proposed methodology was efficient and effective in improving the analysis, by minimizing errors and saving processing time. Este trabajo presenta una estrategia numérica para el ajuste automático de parámetros del Modelo de Daño de Mazars, aplicado a un modelado termohidro- mecánico de estructuras de concreto. El procedimiento se basa en un sistema de flujo de trabajo científico (SWS) que aborda el universo combinatorio de parámetros ajustables al minimizar el número de simulaciones requeridas para obtener resultados optimizados. SWS no sólo mejora la eficiencia, sino que también facilita la estrategia en comparación con los procedimientos manuales. El algoritmo adoptado se desarrolla en un lenguaje de escritura intuitivo y emplea un ambiente computacional distribuido. La comparación con los datos experimentales indica que la metodología propuesta fue eficiente y efectiva para mejorar el análisis, minimizando los errores y ahorrando tiempo de procesamiento.

Compressive strength and Young’s modulus are the main properties used in the design of concrete structures. They are responsible for the cost, safety and dimensioning of the structure, and are generally measured in expensive and time-demanding tests. This fact encourages researches for fast and cost-effective methods to investigate the concrete’s properties. Among the concrete types, structural Lightweight Aggregate Concrete (LWAC) is one of the most employed worldwide, but it presents limited studies and mix design techniques. Thus, this work evaluates and compares the performances of two methods to predict the compressive strength of LWAC samples: Support Vector Machine and Finite Element Method. To this end, both strategies use the LWAC’s mix proportions and the Young’s modulus, and the compressive strength of mortars and aggregates obtained from an experimental program from the literature. The results encourage further researches towards the development of a numerical tool that may assist engineers for practical purposes, since both methods show good agreement with the validation data.

CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior O concreto feito com agregados leves, ou concreto leve estrutural, é considerado um material de construção versátil, bastante usado em todo o mundo, em diversas áreas da construção civil, tais como, edificações pré-fabricadas, plataformas marítimas, pontes, entre outros. Porém, a modelagem das propriedades mecânicas deste tipo de concreto, tais como o módulo de elasticidade e a resistência a compressão, é complexa devido, principalmente, à heterogeneidade intrínseca aos componentes do material. Um modelo de predição das propriedades mecânicas do concreto de agregado leve pode ajudar a diminuir o tempo e o custo de projetos ao prover dados essenciais para os cálculos estruturais. Para esse fim, este trabalho visa desenvolver uma estratégia computacional para a avaliação de propriedades mecânicas do concreto de agregado leve, por meio da combinação da modelagem computacional do concreto via MEF (Método de Elementos Finitos), do método de inteligência computacional via SVR (Máquina de vetores suporte com regressão, do inglês Support Vector Regression) e via RNA (Redes Neurais Artificiais). Além disso, com base na abordagem de workflow científico e many-task computing, uma ferramenta computacional foi desenvolvida com o propósito de facilitar e automatizar a execução dos experimentos científicos numéricos de predição das propriedades mecânicas. Concrete made from lightweight aggregates, or lightweight structural concrete, is considered a versatile construction material, widely used throughout the world, in many areas of civil construction, such as prefabricated buildings, offshore platforms, bridges, among others. However, the modeling of the mechanical properties of this type of concrete, such as the modulus of elasticity and the compressive strength, is complex due mainly to the intrinsic heterogeneity of the components of the material. A predictive model of the mechanical properties of lightweight aggregate concrete can help reduce project time and cost by providing essential data for structural calculations. To this end, this work aims to develop a computational strategy for the evaluation of mechanical properties of lightweight concrete by combining the concrete computational modeling via Finite Element Method, the computational intelligence method via Support Vector Regression, and via Artificial Neural Networks. In addition, based on the approachs scientific workflow and many-task computing, a computational tool will be developed with the purpose of facilitating and automating the execution of the numerical scientific experiments of prediction of the mechanical properties.