Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Search: WFRF:(Muhammad Khan Asad) > Intelligent predict...

Details
MARC

Khan, MajidCOMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan (author)

Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms

Article/chapterEnglish2023

Publisher, publication year, extent ...

Cell Press,2023
electronicrdacarrier

Numbers

LIBRIS-ID:oai:DiVA.org:ltu-103262
https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-103262URI
https://doi.org/10.1016/j.heliyon.2023.e23375DOI

Supplementary language notes

Language:English
Summary in:English

Part of subdatabase

SwePubSwePub

Classification

Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

Notes

Validerad;2024;Nivå 2;2024-03-15 (signyg);Full text license: CC BY-NC-ND
Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Samhällsbyggnadsteknik Infrastrukturteknik hsv//swe
ENGINEERING AND TECHNOLOGY Civil Engineering Infrastructure Engineering hsv//eng
Fiber-reinforced polymers
Flexural capacity
Machine learning
Gene expression programming
Multi-expression programming
Operation and Maintenance Engineering
Drift och underhållsteknik

Added entries (persons, corporate bodies, meetings, titles ...)

Khan, AdilDepartment of Civil and Structural Engineering, University of Bradford, Bradford, West Yorkshire, BD7 1DP, UK (author)
Khan, Asad UllahDepartment of Civil Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan (author)
Shakeel, MuhammadDepartment of Civil Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan (author)
Khan, KhalidDepartment of Civil Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan (author)
Alabduljabbar, HishamDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia (author)
Najeh, TaoufikLuleå tekniska universitet,Drift, underhåll och akustik(Swepub:ltu)taonaj (author)
Gamil, YaserDepartment of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia (author)
COMSATS University Islamabad, Abbottabad Campus, 22060, PakistanDepartment of Civil and Structural Engineering, University of Bradford, Bradford, West Yorkshire, BD7 1DP, UK (creator_code:org_t)

Related titles

In:Heliyon: Cell Press102405-8440

Internet link

Find in a library

Heliyon (Search for host publication in LIBRIS)

To the university's database

Find more in SwePub

By the author/editor: Khan, Majid; Khan, Adil; Khan, Asad Ullah; Shakeel, Muhamma ...; Khan, Khalid; Alabduljabbar, H ...; show more...; Najeh, Taoufik; Gamil, Yaser; show less...

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Civil Engineerin ...; and Infrastructure E ...

Articles in the publication: Heliyon

By the university: Luleå University of Technology

Search outside SwePub

Extend your search to:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se