Experimental and numerical study on FRP-rehabilitated RC beam-column joints at high temperature with artificial neural network

Guardado en:
Detalles Bibliográficos
Publicado en:Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 30016-30042
Autor principal: Prakash, R. Surya
Otros Autores: Parthasarathi, N.
Publicado:
Nature Publishing Group
Materias:
Acceso en línea:Citation/Abstract
Full Text
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

MARC

LEADER 00000nab a2200000uu 4500
001 3240185196
003 UK-CbPIL
022 |a 2045-2322 
024 7 |a 10.1038/s41598-025-16055-9  |2 doi 
035 |a 3240185196 
045 2 |b d20250101  |b d20251231 
084 |a 274855  |2 nlm 
100 1 |a Prakash, R. Surya  |u Department of Civil Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, Chengalpattu Dt, Tamil Nadu, India (ROR: https://ror.org/050113w36) (GRID: grid.412742.6) (ISNI: 0000 0004 0635 5080) 
245 1 |a Experimental and numerical study on FRP-rehabilitated RC beam-column joints at high temperature with artificial neural network 
260 |b Nature Publishing Group  |c 2025 
513 |a Journal Article 
520 3 |a This research explores the structural integrity and thermal durability of reinforced concrete beam—column joints rehabilitated with fiber-reinforced polymer (FRP) laminates under elevated temperatures reaching 800 °C. The work addresses a significant knowledge deficiency by combining computational, experimental, and machine learning methodologies to thoroughly assess FRP performance under thermal stress, a subject inadequately explored in previous literature. A total of 36 models, including 9 conventional and 27 rehabilitated configurations, were analyzed through coupled thermo-mechanical simulations using finite element software. The novelty lies in the pre-experimental identification of critical regions and optimal materials via numerical analysis. At 500 °C, CFRP-rehabilitated joints reduced deflection by up to 42.8% and stress by 37.2% compared to GFRP. In contrast, AFRP showed over 60% higher deflection. The most vulnerable area was identified as the joint core, especially on the column face adjacent to the beam. Experimental tests confirmed CFRP’s superiority; with specimens showing a 28.5% higher load capacity and 31.6% lower core temperature at failure than GFRP-enhanced specimens. Artificial neural network (ANN) regression models were developed to predict deflection and nodal temperature based on input parameters. These models yielded high accuracy (R2 > 0.99), closely matching experimental and numerical results. However, generalizing predictions beyond the studied range may introduce over fitting risks, and the model remains sensitive to data quality. In summary, CFRP demonstrated optimal performance, particularly at 400 °C before rehabilitation and 500 °C afterward, making it the most effective choice for high-temperature FRP-based RC joint rehabilitation. This integrated methodology presents an all comprehensive structure for performance-oriented FRP restoration of reinforced concrete joints. 
653 |a Load 
653 |a Regression analysis 
653 |a Concrete 
653 |a Optimization techniques 
653 |a Numerical analysis 
653 |a High temperature 
653 |a Laminates 
653 |a Epoxy resins 
653 |a Stress concentration 
653 |a Performance evaluation 
653 |a Reinforced concrete 
653 |a Deflection 
653 |a Polymers 
653 |a Machine learning 
653 |a Tensile strength 
653 |a Thermal stress 
653 |a Temperature 
653 |a Neural networks 
653 |a Rehabilitation 
653 |a Environmental 
700 1 |a Parthasarathi, N.  |u Department of Civil Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, Chengalpattu Dt, Tamil Nadu, India (ROR: https://ror.org/050113w36) (GRID: grid.412742.6) (ISNI: 0000 0004 0635 5080) 
773 0 |t Scientific Reports (Nature Publisher Group)  |g vol. 15, no. 1 (2025), p. 30016-30042 
786 0 |d ProQuest  |t Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3240185196/abstract/embedded/2AXJIZYYTBW5RQEH?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3240185196/fulltext/embedded/2AXJIZYYTBW5RQEH?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3240185196/fulltextPDF/embedded/2AXJIZYYTBW5RQEH?source=fedsrch