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  • Alyami, ManaDepartment of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia (författare)

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

  • Artikel/kapitelEngelska2024

Förlag, utgivningsår, omfång ...

  • 2024
  • electronicrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:ltu-103741
  • https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-103741URI
  • https://doi.org/10.1016/j.dibe.2023.100307DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • Validerad;2024;Nivå 2;2024-01-25 (signyg);Funder: Najran University (NU/DRP/SERC/12/10);Full text license: CC BY-NC-ND
  • 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 och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Khan, MajidCivil Engineering Department, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan (författare)
  • Javed, Muhammad FaisalCivil Engineering Department, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan (författare)
  • Ali, MujahidDepartment of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019, Katowice, Poland (författare)
  • Alabduljabbar, HishamDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia (författare)
  • Najeh, TaoufikLuleå tekniska universitet,Drift, underhåll och akustik(Swepub:ltu)taonaj (författare)
  • Gamil, YaserDepartment of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia(Swepub:ltu)yasgam (författare)
  • Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi ArabiaCivil Engineering Department, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Developments in the Built Environment172666-1659

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