Artificial Neural Network Approach to Predict Carbonation Depth in Metakaolin, Brick Powder and Calcined Sediments-Modified Mortars
This research uses Artificial Neural Network (ANN) as a soft computing technique to predict the carbonation depth and service life of cementitious materials with low clinker content. For this purpose, different mortars were prepared with 0, 10, 15, 20, 25 and 30% replacement levels of cement by meta...
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2023-06-01
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| Series: | Modelling in Civil Environmental Engineering |
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| Online Access: | https://doi.org/10.2478/mmce-2023-0009 |
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| author | Benamar Souheyla Kameche Zine El Abidine Mamoune Sidi Mohamed Aissa Siad Hocine Houmadi Youcef |
| author_facet | Benamar Souheyla Kameche Zine El Abidine Mamoune Sidi Mohamed Aissa Siad Hocine Houmadi Youcef |
| author_sort | Benamar Souheyla |
| collection | DOAJ |
| description | This research uses Artificial Neural Network (ANN) as a soft computing technique to predict the carbonation depth and service life of cementitious materials with low clinker content. For this purpose, different mortars were prepared with 0, 10, 15, 20, 25 and 30% replacement levels of cement by metakaolin (MK), brick powder (BP) and calcined sediments (CS). The experimental results of the carbonation depth were obtained under natural and accelerated carbonation conditions for exposure periods of 12 months and 28 days respectively. ANN was utilized taking into account the main influential factors on mortars carbonation, including mix proportions and environmental conditions. For the ANN model, seven datasets were considered as inputs, covering mineral admixture content, cement content, curing time, CO2 concentration, relative humidity, temperature and CO2 exposure time, in addition to one output parameter which is the carbonation depth. The results show that the resistance to carbonation of the mortars decreases with the increase of cement substitution by MK, BP or CS. The network model gives good performance values in the validation and testing set with a lower mean square error (MSE) and a higher determination coefficient (R). The predicted carbonation depths are in good agreement with the experimental measurements of carbonation depths, confirming the efficiency of the developed ANN model to be applied to correctly estimate the carbonation depth of cementitious materials with low clinker content. |
| format | Article |
| id | doaj-art-6ccbf21406e54f14a689ebe5c5894bed |
| institution | OA Journals |
| issn | 2784-1391 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Modelling in Civil Environmental Engineering |
| spelling | doaj-art-6ccbf21406e54f14a689ebe5c5894bed2025-08-20T02:36:23ZengSciendoModelling in Civil Environmental Engineering2784-13912023-06-01182355510.2478/mmce-2023-0009Artificial Neural Network Approach to Predict Carbonation Depth in Metakaolin, Brick Powder and Calcined Sediments-Modified MortarsBenamar Souheyla0Kameche Zine El Abidine1Mamoune Sidi Mohamed Aissa2Siad Hocine3Houmadi Youcef41PhD Student, Smart Structures Laboratory (SSL), Dept. of Civil Engineering & Public Works, University of Aïn-Témouchent, P.B.284, Aïn-Témouchent - 46000 -Algeria2Lecturer, Smart Structures Laboratory (SSL), Dept. of Civil Engineering & Public Works, University of Aïn-Témouchent, P.B.284, Aïn-Témouchent - 46000 - Algeria3Lecturer, Smart Structures Laboratory (SSL), Dept. of Civil Engineering & Public Works, University of Aïn-Témouchent, P.B.284, Aïn-Témouchent - 46000 - Algeria4Lecturer, Dept. of Civil Engineering, Toronto Metropolitan University. 350, Victoria St., Toronto, ON M5B 2K3, Canada5Lecturer, Smart Structures Laboratory (SSL), Aïn-Témouchent University, Department of Civil Engineering, Tlemcen University, P. Box : 119, Tlemcen - 13000 - AlgeriaThis research uses Artificial Neural Network (ANN) as a soft computing technique to predict the carbonation depth and service life of cementitious materials with low clinker content. For this purpose, different mortars were prepared with 0, 10, 15, 20, 25 and 30% replacement levels of cement by metakaolin (MK), brick powder (BP) and calcined sediments (CS). The experimental results of the carbonation depth were obtained under natural and accelerated carbonation conditions for exposure periods of 12 months and 28 days respectively. ANN was utilized taking into account the main influential factors on mortars carbonation, including mix proportions and environmental conditions. For the ANN model, seven datasets were considered as inputs, covering mineral admixture content, cement content, curing time, CO2 concentration, relative humidity, temperature and CO2 exposure time, in addition to one output parameter which is the carbonation depth. The results show that the resistance to carbonation of the mortars decreases with the increase of cement substitution by MK, BP or CS. The network model gives good performance values in the validation and testing set with a lower mean square error (MSE) and a higher determination coefficient (R). The predicted carbonation depths are in good agreement with the experimental measurements of carbonation depths, confirming the efficiency of the developed ANN model to be applied to correctly estimate the carbonation depth of cementitious materials with low clinker content.https://doi.org/10.2478/mmce-2023-0009mortarcarbonation depth predictionartificial neural networkmetakaolinbrick powdercalcined sediments |
| spellingShingle | Benamar Souheyla Kameche Zine El Abidine Mamoune Sidi Mohamed Aissa Siad Hocine Houmadi Youcef Artificial Neural Network Approach to Predict Carbonation Depth in Metakaolin, Brick Powder and Calcined Sediments-Modified Mortars Modelling in Civil Environmental Engineering mortar carbonation depth prediction artificial neural network metakaolin brick powder calcined sediments |
| title | Artificial Neural Network Approach to Predict Carbonation Depth in Metakaolin, Brick Powder and Calcined Sediments-Modified Mortars |
| title_full | Artificial Neural Network Approach to Predict Carbonation Depth in Metakaolin, Brick Powder and Calcined Sediments-Modified Mortars |
| title_fullStr | Artificial Neural Network Approach to Predict Carbonation Depth in Metakaolin, Brick Powder and Calcined Sediments-Modified Mortars |
| title_full_unstemmed | Artificial Neural Network Approach to Predict Carbonation Depth in Metakaolin, Brick Powder and Calcined Sediments-Modified Mortars |
| title_short | Artificial Neural Network Approach to Predict Carbonation Depth in Metakaolin, Brick Powder and Calcined Sediments-Modified Mortars |
| title_sort | artificial neural network approach to predict carbonation depth in metakaolin brick powder and calcined sediments modified mortars |
| topic | mortar carbonation depth prediction artificial neural network metakaolin brick powder calcined sediments |
| url | https://doi.org/10.2478/mmce-2023-0009 |
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