Modeling the capacity of engineered cementitious composites for self-healing using AI-based ensemble techniques Article Swipe
Hisham Alabduljabbar
,
Kaffayatullah Khan
,
Hamad Hassan Awan
,
Rayed Alyousef
,
Abdeliazim Mustafa Mohamed
,
Sayed M. Eldin
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.cscm.2022.e01805
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.cscm.2022.e01805
Engineered cementitious composite (ECC) is a special material that, when continuously hydrated, can considerably aid in self-healing. It is necessary to look at the capacity of ECC for self-healing – as measured by crack-widthafter (CWA) – when process of healing has completed, gauge the severity of the cracking, and foresee the extent of the healing. However, modeling and forecasting capacity of ECC for self-healing is a challenging task. Prediction of self-healing is a notably uncommon application of machine learning (ML), which has been applied to forecast a range of concrete properties. To estimate the capacity of ECC for self-healing, this study used three different the ensemble ML algorithms namely AdaBoost regressor (AR), decision tree (DT), and bagging regressor (BR). In addition, k-fold cross-validation method is utilized to assess the model effectiveness. With an R2 value of 0.974, the BR model was more successful in predicting outcomes when compared to the DT and AR models. Improved model performance was shown for ensemble models with smaller MAE (AR = 3.40, and BR = 1.89), MSE (AR = 27.09, and BR = 10.40), and RMSE (AR = 5.21, and BR = 3.23) values and larger R2 (AR = 0.933, and BR = 0.974) values as compared to DT (MAE = 4.29, MSE = 43.28, RMSE = 6.58, R2 = 0.894). Eventually this study will lead to savings in time, effort, and money, and the use of ML approaches to predict CWA will advance the field of civil engineering. It is also advised to investigate the crack-healing properties of additional cementitious materials such rice husk ash, wheat straw ash, and pumice powder subject to modeling their crack-healing properties using ML approaches.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.cscm.2022.e01805
- OA Status
- gold
- Cited By
- 38
- References
- 112
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312221648
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312221648Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.cscm.2022.e01805Digital Object Identifier
- Title
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Modeling the capacity of engineered cementitious composites for self-healing using AI-based ensemble techniquesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-12-24Full publication date if available
- Authors
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Hisham Alabduljabbar, Kaffayatullah Khan, Hamad Hassan Awan, Rayed Alyousef, Abdeliazim Mustafa Mohamed, Sayed M. EldinList of authors in order
- Landing page
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https://doi.org/10.1016/j.cscm.2022.e01805Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.cscm.2022.e01805Direct OA link when available
- Concepts
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Mean squared error, Self-healing, Computer science, Ensemble forecasting, Ensemble learning, Mean absolute error, Predictive modelling, Composite number, Artificial intelligence, Machine learning, Materials science, Mathematics, Statistics, Algorithm, Medicine, Alternative medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
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38Total citation count in OpenAlex
- Citations by year (recent)
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2025: 16, 2024: 11, 2023: 11Per-year citation counts (last 5 years)
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112Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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