Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete

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Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete

Alyami, Mana (författare): Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia

Khan, Majid (författare): Civil Engineering Department, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan

Javed, Muhammad Faisal (författare): Civil Engineering Department, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan

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Ali, Mujahid (författare): Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019, Katowice, Poland

Alabduljabbar, Hisham (författare): Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia

Najeh, Taoufik (författare): Luleå tekniska universitet,Drift, underhåll och akustik

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)

2024
2024
Engelska.
Ingår i: Developments in the Built Environment. - 2666-1659. ; 17

Relaterad länk:: https://doi.org/10.1...; visa fler...; https://ltu.diva-por... (primary) (Raw object); https://urn.kb.se/re...; https://doi.org/10.1...; visa färre...

Tidskriftsartikel (refereegranskat)

Abstract Ämnesord

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In recent years, the construction industry has been striving to make production faster and handle more complex architectural designs. Waste reduction, geometric freedom, lower construction costs, and speedy construction make the 3D-printed fiber-reinforced concrete (3DPFRC) alternative for future construction. However, achieving the optimum mixture composition for 3DPFRC remains a daunting task, entailing the consideration of multiple variables and necessitating an extensive trial-and-error experimental process. Therefore, this study investigated the application of different metaheuristic optimization algorithms to predict the compressive strength (CS) of 3DPFRC. A database of 299 data samples with 16 different input features was compiled from the experimental studies in the literature. Six metaheuristic algorithms, such as human felicity algorithm (HFA), differential evolution algorithm (DEA), nuclear reaction optimization (NRO), Harris hawks optimization (HHO), lightning search algorithm (LSA), and tunicate swarm algorithm (TSA) were applied to identify the optimal hyperparameter combination for the random forest (RF) model in predicting the CS of 3DPFRC. Different statistical metrics and 10-fold cross-validation were used to evaluate the accuracy of the models. The TSA-RF model exhibited superior performance compared to other models, achieving correlation (R), mean absolute error (MAE), and root mean square error (RMSE) values of 0.99, 2.10 MPa, and 3.59 MPa, respectively. The LSA-RF model also performed well, with R, MAE, and RMSE values of 0.99, 2.93 MPa, and 6.23 MPa, respectively. SHapley Additive exPlanation (SHAP) interpretability elucidates the intricate relationships between features and their effects on the CS, thereby offering invaluable insights for the performance-based mix proportion design of 3DPFRC.

Ämnesord

TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Annan samhällsbyggnadsteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Other Civil Engineering (hsv//eng)

Nyckelord

Additive manufacturing
3D-printed concrete
Compressive strength
Fiber-reinforced concrete
Metaheuristic algorithms
Random forest
Operation and Maintenance Engineering
Drift och underhållsteknik

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