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Predictive modeling...
Predictive modeling for depth of wear of concrete modified with fly ash : A comparative analysis of genetic programming-based algorithms
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- Khan, Adil (författare)
- Department of Advanced Civil and Structural Engineering, University of Bradford, Bradford, West Yorkshire BD7 1DP, UK
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- Khan, Majid (författare)
- Department of Civil Engineering, COMSATS University, Islamabad, Abbottabad Campus 22060, Pakistan
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- Ali, Mohsin (författare)
- School of Civil Engineering, Southeast University Nanjing, China
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- Khan, Murad (författare)
- School of Civil Engineering, Tianjin University, Tianjin 300354, China
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- Khan, Asad Ullah (författare)
- Department of Civil Engineering, COMSATS University, Islamabad, Abbottabad Campus 22060, Pakistan
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- Shakeel, Muhammad (författare)
- Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan
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- Fawad, Muhammad (författare)
- Silesian University of Technology Poland, Poland; Budapest University of Technology and Economics Hungary, Hungary
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- Najeh, Taoufik (författare)
- Luleå tekniska universitet,Drift, underhåll och akustik
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- Gamil, Yaser (författare)
- Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia
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(creator_code:org_t)
- Elsevier, 2024
- 2024
- Engelska.
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Ingår i: Case Studies in Construction Materials. - : Elsevier. - 2214-5095. ; 20
- Relaterad länk:
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https://doi.org/10.1...
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Infrastrukturteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Infrastructure Engineering (hsv//eng)
Nyckelord
- Fly ash
- Abrasion resistance
- Gene expression programming
- Multi-expression programming
- SHAP
- Operation and Maintenance Engineering
- Drift och underhållsteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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