Modelos preditivos de dano aplicados a estruturas de concreto atacadas por reação álcali-sílica: uma revisão sistemática da literatura

• Autor(a) principal: Gomes, Geovanne Caetano
• Data de Publicação: 2017
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFG
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/7560
• topic: Concreto; Durabilidade; Reação álcali-agregado; Reação álcali-sílica; Modelo; Previsão de dano; Concrete; Durability; Alkali-aggregate reaction; Alkali-silica reaction; Model; Damage prediction; ENGENHARIAS

Among the several deleterious actions may attack concrete elements, is the alkali-aggregate reaction (AAR), which affects, mainly, structures of dams, bridges and foundations, where the alkali-silica reaction (ASR) is the most common. One of the main challenges regarding the prediction of this phenomenon is the development of models that may predict damage specific for this reaction, which constitute the theme of this research. Firstly, a systematic literature review was conducted with respect to the models developed, with the organization and classification of the data found, presenting a clear and detailed state-of-art. Therefore, the studies published in journals in the last five years (2012-2016) were selected, in order to conduct their categorization regarding the scale and nature of the analysis, type of modeling, and the software necessary to execute the simulations, besides the summarizing, grouping and analysis of the information concerning the input data necessary to the execution of each modeling, as well as the results generated by each one of them. The models which do not predict damage, i.e. general models that simulate the ASR, were investigated to verify their contribution to a better understanding of the chemical and physical processes that occur in the concrete affect by the reaction. Finally, it was verified that the models analyzed are based on different theories and methods of analyses, demanding distinct input data and generating heterogeneous output data, which are meticulously explained in this paper.