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Selection for the Best Less-Parameter Carbonation Depth Model
Current Issue
Volume 2, 2015
Issue 6 (November)
Pages: 167-179   |   Vol. 2, No. 6, November 2015   |   Follow on         
Paper in PDF Downloads: 64   Since Dec. 9, 2015 Views: 1537   Since Dec. 9, 2015
Mishuk Bhattacharjee, Department of Civil Engineering, Rajshahi University of Engineering & Technology (RUET), Rajshahi, Bangladesh.
Md. Arifur Rahman, Department of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh.
Arafat Sharif Munna, Department of Civil Engineering, Rajshahi University of Engineering & Technology (RUET), Rajshahi, Bangladesh.
Md. Ashrafuzzaman, Department of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh.
Md. Ayanul Huq Chowdhury, Department of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh.
Shovon Barua, Department of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh.
Temperature, relative humidity water-cement ratio, cement contents, strength of concrete etc. and many other parameters, conduct a carbonation model and it is really costly and time consuming to generate a model which is conducted by more parameters. But for maximum structural arrangements, it is not possible to allocate ample resources and time to continue this process. That is why, it is highly required to make sure a less parameter based model or to identify the best one for all performance. This paper work is based on that idea to identify the best model for concrete carbonation, which enables the solution makers to adopt the best situation easily. Two carbonation models were selected for determining the best model between the two models and also the accuracy of each model was checked. Error distribution method was implicated for checking the models. In the end, it was found that the less number of parameters concerned, the more ease and accuracy would be achieved in predicting carbonation depth.
Carbonation Depth, Diffusive Coefficient, Constant Parameters, Variable Parameters, Carbonation Depth-Time Relationship Curve, Error Distribution Curve
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