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

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

Alani, Amir M. and Faramarzi, Asaad (2014) An evolutionary approach to modelling concrete degradation due to sulphuric acid attack. Applied Soft Computing, 24. pp. 985-993. ISSN 1568-4946 (doi:https://doi.org/10.1016/j.asoc.2014.08.044)

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Abstract

Concrete corrosion due to sulphuric acid attack is known to be one of the main contributory factors for degradation of concrete sewer pipes. This paper 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.

Item Type: Article
Additional Information: [1] The Author's Accepted Manuscript version is attached to this record. Please cite this article as: A.M. Alani, An evolutionary approach to modelling concrete degradation due to sulphuric acid attack, Applied Soft Computing Journal (2014), http://dx.doi.org/10.1016/j.asoc.2014.08.044. [2] Note from Publisher (Elsevier): This is a PDF file of an unedited manuscript that has been accepted for publication. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. [3] Acknowledgements (funding): This research was funded by a grant from the UK Engineering and Physical Sciences Research Council (EPSRC) grant number EP/1032150/1 (Assessing Current State of Buried Sewer Systems and Their Remaining Safe Life).
Uncontrolled Keywords: evolutionary computing, genetic algorithm, evolutionary polynomial regression, optimisation, hybrid techniques, data mining, sulphuric acid attack, degradation, corrosion, sewer pipes
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Related URLs:
Last Modified: 14 Dec 2016 13:17
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/12075

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