Application of machine learning in predicting workability for alkali-activated materials
Alkali-activated materials (AAMs) have been extensively studied for their superior performance and eco-friendliness. While previous researches have primarily focused on the hardened properties of AAMs, the assessment of their fresh properties has often been overlooked. The preparation process of AAM...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-07-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509523003534 |
| _version_ | 1827920515447128064 |
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| author | Y.K. Kong Kiyofumi Kurumisawa |
| author_facet | Y.K. Kong Kiyofumi Kurumisawa |
| author_sort | Y.K. Kong |
| collection | DOAJ |
| description | Alkali-activated materials (AAMs) have been extensively studied for their superior performance and eco-friendliness. While previous researches have primarily focused on the hardened properties of AAMs, the assessment of their fresh properties has often been overlooked. The preparation process of AAMs involves key factors in mix design that significantly impact their workability. This study comprehensively evaluates the workability of different types of AAMs, analyzing a total of 402 mixtures extracted from 26 individual papers. The examination focuses on key factors in AAM mix design, specifically the precursors, alkali activator, and aggregate phases. Finally, a mathematical model for predicting the workability of AAMs was constructed based on the LightGBM (LGBM) algorithm. In this model, the reactivity of precursors, alkali activator, geopolymer paste volume, superplasticizer content, and aggregate were set as the inputs, and the flowability was set as the output. Additionally, the predictive efficiency of LGBM model was evaluated and compared to the multi-linear regression model. Meantime, a validation experiment for proving its accuracy was also conducted. This study largely advanced the understanding of the workability of AAMs by providing practical guidelines on AAM mix design with high workability and consistency. |
| first_indexed | 2024-03-13T04:11:41Z |
| format | Article |
| id | doaj.art-9f65a2c8e1354da2ad58d286caf1827b |
| institution | Directory Open Access Journal |
| issn | 2214-5095 |
| language | English |
| last_indexed | 2024-03-13T04:11:41Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj.art-9f65a2c8e1354da2ad58d286caf1827b2023-06-21T06:54:47ZengElsevierCase Studies in Construction Materials2214-50952023-07-0118e02173Application of machine learning in predicting workability for alkali-activated materialsY.K. Kong0Kiyofumi Kurumisawa1Division of Sustainable Resources Engineering, Graduate School of Engineering, Hokkaido University, JapanDivision of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido University, Japan; Corresponding author.Alkali-activated materials (AAMs) have been extensively studied for their superior performance and eco-friendliness. While previous researches have primarily focused on the hardened properties of AAMs, the assessment of their fresh properties has often been overlooked. The preparation process of AAMs involves key factors in mix design that significantly impact their workability. This study comprehensively evaluates the workability of different types of AAMs, analyzing a total of 402 mixtures extracted from 26 individual papers. The examination focuses on key factors in AAM mix design, specifically the precursors, alkali activator, and aggregate phases. Finally, a mathematical model for predicting the workability of AAMs was constructed based on the LightGBM (LGBM) algorithm. In this model, the reactivity of precursors, alkali activator, geopolymer paste volume, superplasticizer content, and aggregate were set as the inputs, and the flowability was set as the output. Additionally, the predictive efficiency of LGBM model was evaluated and compared to the multi-linear regression model. Meantime, a validation experiment for proving its accuracy was also conducted. This study largely advanced the understanding of the workability of AAMs by providing practical guidelines on AAM mix design with high workability and consistency.http://www.sciencedirect.com/science/article/pii/S2214509523003534Alkali-activated materialsLightGBMWorkabilityMix designPrediction |
| spellingShingle | Y.K. Kong Kiyofumi Kurumisawa Application of machine learning in predicting workability for alkali-activated materials Case Studies in Construction Materials Alkali-activated materials LightGBM Workability Mix design Prediction |
| title | Application of machine learning in predicting workability for alkali-activated materials |
| title_full | Application of machine learning in predicting workability for alkali-activated materials |
| title_fullStr | Application of machine learning in predicting workability for alkali-activated materials |
| title_full_unstemmed | Application of machine learning in predicting workability for alkali-activated materials |
| title_short | Application of machine learning in predicting workability for alkali-activated materials |
| title_sort | application of machine learning in predicting workability for alkali activated materials |
| topic | Alkali-activated materials LightGBM Workability Mix design Prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2214509523003534 |
| work_keys_str_mv | AT ykkong applicationofmachinelearninginpredictingworkabilityforalkaliactivatedmaterials AT kiyofumikurumisawa applicationofmachinelearninginpredictingworkabilityforalkaliactivatedmaterials |