MODELLING OF CARBONATION OF REINFORCED CONCRETE STRUCTURES IN INTRAMUROS, MANILA USING ARTIFICIAL NEURAL NETWORK

Authors

  • Richard M. De Jesus
  • Joshua A. M. Collado
  • Jemison L. Go
  • Mike A. Rosanto
  • John L. Tan

Keywords:

Carbonation, Artificial Neural Network (ANN), model C421, reinforced concrete

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.

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Published

2016-12-27

How to Cite

Richard M. De Jesus, Joshua A. M. Collado, Jemison L. Go, Mike A. Rosanto, & John L. Tan. (2016). MODELLING OF CARBONATION OF REINFORCED CONCRETE STRUCTURES IN INTRAMUROS, MANILA USING ARTIFICIAL NEURAL NETWORK. GEOMATE Journal, 13(35), 87–92. Retrieved from https://geomatejournal.com/geomate/article/view/1278

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