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

被引:33
作者
Alyami, Mana [1 ]
Khan, Majid [2 ]
Javed, Muhammad Faisal [2 ]
Ali, Mujahid [3 ]
Alabduljabbar, Hisham [4 ]
Najeh, Taoufik [5 ]
Gamil, Yaser [6 ]
机构
[1] Najran Univ, Coll Engn, Dept Civil Engn, Najran, Saudi Arabia
[2] COMSATS Univ Islamabad, Civil Engn Dept, Abbottabad Campus, Islamabad 22060, Pakistan
[3] Silesian Tech Univ, Fac Transport & Aviat Engn, Dept Transport Syst, Traff Engn & Logist, Krasinskiego 8 St, PL-40019 Katowice, Poland
[4] Prince Sattam Bin Abdulaziz Univ, Coll Engn Al Kharj, Dept Civil Engn, Al Kharj 11942, Saudi Arabia
[5] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, Operat Maintenance & Acoust, Lulea, Sweden
[6] Monash Univ Malaysia, Sch Engn, Dept Civil Engn, Jalan Lagoon Selatan, Bandar Sunway 47500, Selangor, Malaysia
关键词
Additive manufacturing; 3D-printed concrete; Compressive strength; Fiber-reinforced concrete; Metaheuristic algorithms; Random forest; MECHANICAL-PROPERTIES; CONSTRUCTION; PERFORMANCE; BEHAVIORS; SINGLE;
D O I
10.1016/j.dibe.2023.100307
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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 hyper parameter 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.
引用
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页数:22
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