Comparative use of different AI methods for the prediction of concrete compressive strength
Concrete mix design requires specialized knowledge and techniques for characterization. However, this process is time-consuming, and the mechanical properties, such as strength, can vary due to factors like cement type, water content, aggregates, and curing time. Additionally, analytical mathematica...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Cleaner Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772397625000085 |
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| author | Mouhamadou Amar |
| author_facet | Mouhamadou Amar |
| author_sort | Mouhamadou Amar |
| collection | DOAJ |
| description | Concrete mix design requires specialized knowledge and techniques for characterization. However, this process is time-consuming, and the mechanical properties, such as strength, can vary due to factors like cement type, water content, aggregates, and curing time. Additionally, analytical mathematical models are often used to estimate concrete characteristics. However, accurately determining concrete properties without laboratory testing is challenging, especially when nontraditional materials, such as certain supplementary cementitious materials, are involved. Recently, artificial intelligence has become a powerful resource that enables machine learning-based forecasting using available data. This study utilized RapidMiner® software to design models capable of analyzing various types of tagged data and performing machine learning predictions. These models were applied to over 5,373 concrete formulations compiled from 137 literature sources. The simulations used artificial neural networks or deep learning, generalized linear, decision tree, random forest, support vector machine, and gradient-boosted tree models to predict the compressive strength of 8 concrete mix designs containing different SCMs. The accuracy of models was estimated using traditional statistical indices such as R2, MAPE and RMSE. The most accurate model was found to be a gradient-boosted tree followed by deep learning and random forest. Forecasts were validated with high accuracy by comparing experimental results to numerical data. |
| format | Article |
| id | doaj-art-83bd799ce4fe4f1aa45d8383ac6697a8 |
| institution | Kabale University |
| issn | 2772-3976 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cleaner Materials |
| spelling | doaj-art-83bd799ce4fe4f1aa45d8383ac6697a82025-02-08T05:01:38ZengElsevierCleaner Materials2772-39762025-03-0115100299Comparative use of different AI methods for the prediction of concrete compressive strengthMouhamadou Amar0Univ. Lille IMT Nord Europe Univ. Artois Yncrea Hauts-de-France France; ULR 4515 - LGCgE, Laboratoire de Gé nie Civil et géo-Environnement F-59000 Lille, France; Corresponding author at: Univ. Lille, IMT Nord Europe, Univ. Artois, Yncrea Hauts-de-France, France.Concrete mix design requires specialized knowledge and techniques for characterization. However, this process is time-consuming, and the mechanical properties, such as strength, can vary due to factors like cement type, water content, aggregates, and curing time. Additionally, analytical mathematical models are often used to estimate concrete characteristics. However, accurately determining concrete properties without laboratory testing is challenging, especially when nontraditional materials, such as certain supplementary cementitious materials, are involved. Recently, artificial intelligence has become a powerful resource that enables machine learning-based forecasting using available data. This study utilized RapidMiner® software to design models capable of analyzing various types of tagged data and performing machine learning predictions. These models were applied to over 5,373 concrete formulations compiled from 137 literature sources. The simulations used artificial neural networks or deep learning, generalized linear, decision tree, random forest, support vector machine, and gradient-boosted tree models to predict the compressive strength of 8 concrete mix designs containing different SCMs. The accuracy of models was estimated using traditional statistical indices such as R2, MAPE and RMSE. The most accurate model was found to be a gradient-boosted tree followed by deep learning and random forest. Forecasts were validated with high accuracy by comparing experimental results to numerical data.http://www.sciencedirect.com/science/article/pii/S2772397625000085Mix designConcreteSupplementary cementitious materialsMachine learningCompressive strengthPrediction |
| spellingShingle | Mouhamadou Amar Comparative use of different AI methods for the prediction of concrete compressive strength Cleaner Materials Mix design Concrete Supplementary cementitious materials Machine learning Compressive strength Prediction |
| title | Comparative use of different AI methods for the prediction of concrete compressive strength |
| title_full | Comparative use of different AI methods for the prediction of concrete compressive strength |
| title_fullStr | Comparative use of different AI methods for the prediction of concrete compressive strength |
| title_full_unstemmed | Comparative use of different AI methods for the prediction of concrete compressive strength |
| title_short | Comparative use of different AI methods for the prediction of concrete compressive strength |
| title_sort | comparative use of different ai methods for the prediction of concrete compressive strength |
| topic | Mix design Concrete Supplementary cementitious materials Machine learning Compressive strength Prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2772397625000085 |
| work_keys_str_mv | AT mouhamadouamar comparativeuseofdifferentaimethodsforthepredictionofconcretecompressivestrength |