An evolutionary approach to modelling concrete degradation due to sulphuric acid attack

Amir M. Alani, Asaad Faramarzi

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
236 Downloads (Pure)


Concrete corrosion due to sulphuric acid attack is known to be one of the main contributory factors for degradation of concrete sewer pipes. This article proposes to use a novel data mining technique, namely, evolutionary polynomial regression (EPR), to predict degradation of concrete subject to sulphuric acid attack. A comprehensive dataset from literature is collected to train and develop an EPR model for this purpose. The results show that the EPR model can successfully predict mass loss of concrete specimens exposed to sulphuric acid. Parametric studies show that the proposed model is capable of representing the degree to which individual contributing parameters can affect the degradation of concrete. The developed EPR model is compared with a model based on artificial neural network (ANN) and the advantageous of the EPR approach over ANN is highlighted. In addition, based on the developed EPR model and using an optimisation technique, the optimum concrete mixture to provide maximum resistance against sulphuric acid attack has been identified.
Original languageEnglish
Pages (from-to)985-993
JournalApplied Soft Computing
Early online date27 Aug 2014
Publication statusPublished - 1 Nov 2014


  • evolutionary computing
  • genetic algorithm
  • evolutionary polynomial regression
  • optimisation
  • hybrid techniques
  • data mining
  • sulphuric acid attack
  • degradation
  • corrosion
  • sewer pipes


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