MODELLING OF CARBONATION OF REINFORCED CONCRETE STRUCTURES IN INTRAMUROS, MANILA USING ARTIFICIAL NEURAL NETWORK
2017; Volume: 13; Issue: 35 Linguagem: Inglês
10.21660/2017.35.6683
ISSN2186-2990
Autores Tópico(s)Infrastructure Maintenance and Monitoring
ResumoCorrosion is a perennial problem in reinforced concrete structures, and is a serious concerndue to the deterioration that it causes to reinforced concrete members. Though regarded as having a minorinfluence to corrosion compared to chloride-induced corrosion, carbonation is becoming a serious threat dueto continuous development of cities like Manila. Expectedly, as Manila continues to develop, carbonemission shoots up to alarming proportions, calling out for studies to investigate and mitigate its effect tohuman health and structures. Artificial Neural Network (ANN) is known for establishing relationships amongparameters with unknown dependency towards another variable, similar to the case of carbonation’sdependency with age, temperature, relative humidity, and moisture content. Utilizing field-gatheredsecondary data as training and testing parameter for back propagation algorithm, an ANN model is proposed.Prediction of carbonation depth using ANN Model C421 showed reliable results. Validation of performanceof Model C421 was further checked by comparing its prediction with a different set of field-gatheredsecondary data and results confirmed good agreement between prediction and measured values.
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