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Experimental study and development of machine learning model using random forest classifier on shear strength prediction of RC beam with externally bonded GFRP composites
The reduced life span of RC structures is mainly due to the corrosion of the steel reinforcement. Hence, there is a need to consider alternate materials. In the present study, GFRP composites are used as external bonding shear reinforcement of the RC beams. They were cast with C30 grade concrete with steel/GFRP shear reinforcements for experimental analysis. For the external bonding work, chopped strand mat (CSM) and woven roving (WR) GFRP composites were attached to both sides as ‘U’ wrap on the shear zone of the RC beams. Two point loads were applied at 150 mm spacing on all the beam specimens, and the experimental results of the RC beams with GFRP composites are compared with that of the control beams. The major outcome of the present study is that the RC beam externally bonded with GFRP WR ‘U’ wrap on full shear area performs well. The failure loads are 20.93, 6.57 and 18.94% and these are more than that of the beams externally bonded with normal ‘U’ wrap strips on the shear area, sides of the full shear area and strips on sides, respectively. Also, theoretical analysis was carried out to determine the shear resistance of RC beams, and the results are compared with the experimental results. In addition to that, the random forest classifier was used to develop a machine learning (ML) model to predict the failure load, mid-span deflection and type of failure. The predicted ML results are validated, and it proved that the experimental results are well in line with the developed ML model in terms of performance and accuracy.
Experimental study and development of machine learning model using random forest classifier on shear strength prediction of RC beam with externally bonded GFRP composites
The reduced life span of RC structures is mainly due to the corrosion of the steel reinforcement. Hence, there is a need to consider alternate materials. In the present study, GFRP composites are used as external bonding shear reinforcement of the RC beams. They were cast with C30 grade concrete with steel/GFRP shear reinforcements for experimental analysis. For the external bonding work, chopped strand mat (CSM) and woven roving (WR) GFRP composites were attached to both sides as ‘U’ wrap on the shear zone of the RC beams. Two point loads were applied at 150 mm spacing on all the beam specimens, and the experimental results of the RC beams with GFRP composites are compared with that of the control beams. The major outcome of the present study is that the RC beam externally bonded with GFRP WR ‘U’ wrap on full shear area performs well. The failure loads are 20.93, 6.57 and 18.94% and these are more than that of the beams externally bonded with normal ‘U’ wrap strips on the shear area, sides of the full shear area and strips on sides, respectively. Also, theoretical analysis was carried out to determine the shear resistance of RC beams, and the results are compared with the experimental results. In addition to that, the random forest classifier was used to develop a machine learning (ML) model to predict the failure load, mid-span deflection and type of failure. The predicted ML results are validated, and it proved that the experimental results are well in line with the developed ML model in terms of performance and accuracy.
Experimental study and development of machine learning model using random forest classifier on shear strength prediction of RC beam with externally bonded GFRP composites
Asian J Civ Eng
Al Mamari, Amna Hamed Salim (author) / Al Ghafri, Rawan Said Humaid Hammad (author) / Aravind, N. (author) / Dhandapani, Ragavesh (author) / Al Hatali, Eman Muhye Adeen Muhye (author) / Pandian, Rani (author)
Asian Journal of Civil Engineering ; 24 ; 267-286
2023-01-01
20 pages
Article (Journal)
Electronic Resource
English
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