Modelling of carbonation of reinforced concrete structures in intramuros, manila using artificial neural network

Abstract

Corrosion is a perennial problem in reinforced concrete structures, and is a serious concern due to the deterioration that it causes to reinforced concrete members. Though regarded as having a minor influence to corrosion compared to chloride-induced corrosion, carbonation is becoming a serious threat due to continuous development of cities like Manila. Expectedly, as Manila continues to develop, carbon emission shoots up to alarming proportions, calling out for studies to investigate and mitigate its effect to human health and structures. Artificial Neural Network (ANN) is known for establishing relationships among parameters with unknown dependency towards another variable, similar to the case of carbonation's dependency with age, temperature, relative humidity, and moisture content. Utilizing field-gathered secondary 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 performance of Model C421 was further checked by comparing its prediction with a different set of field-gathered secondary data and results confirmed good agreement between prediction and measured values.