Publication: Machine Learning and Regression Models for Evaluating Ultimate Performance of Cotton Rope-Confined Recycled Aggregate Concrete
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0
Issued Date
2025-01-01
Resource Type
eISSN
20755309
Scopus ID
2-s2.0-85214496197
Journal Title
Buildings
Volume
15
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Buildings Vol.15 No.1 (2025)
Suggested Citation
Rodsin K., Ejaz A., Wang H., Saingam P., Joyklad P., Khaliq W., Hussain Q., Boonmee C. Machine Learning and Regression Models for Evaluating Ultimate Performance of Cotton Rope-Confined Recycled Aggregate Concrete. Buildings Vol.15 No.1 (2025). doi:10.3390/buildings15010064 Retrieved from: https://hdl.handle.net/20.500.14740/20217
Corresponding Author(s)
Other Contributor(s)
Abstract
This study investigates the use of cotton ropes (CRs) as a sustainable and cost-effective substitute for synthetic fiber-reinforced polymers for concrete confinement, offering significant environmental benefits such as lower CO2 emissions and reduced energy consumption. The work evaluates the effectiveness of CR strips for confining concrete, including scenarios with recycled concrete aggregates (ReCA). Compressive strength improvements varied among specimens, with Specimen I-3F showing a 140.52% increase and Specimen II-3F achieving a 46.67% improvement. Strip configurations for Type I recycled aggregate concrete (RAC) outperformed full wraps on Type II RAC, exemplified by Specimen I-3S’s 84.51% improvement. Ultimate strain enhancements ranged from 915% to 4490.91%, driven by the significant rupture strain of cotton rope confinement. For Type I RAC, complete wrapping significantly outperformed strip configurations by 56%, 50%, and 32% in ultimate strength improvement for 1, 2, and 3 layers, respectively. The confinement ratio, varying from 0.10 to 0.70, greatly influenced the compressive behavior, with compressive strength normalized by unconfined strength increasing consistently with the confinement ratio. A minimum confinement ratio of roughly 0.40 is required to achieve an increasing second part in the compressive behavior. The initial parabolic branch was modeled using Popovics’ formulation, revealing an elastic modulus approximately 20% lower than ACI 318-19 predictions. The second branch was described using a linear approximation, and nonlinear regression analysis produced expressions for key points on the idealized compressive curve, enhancing model accuracy for CR-confined RAC. The (Formula presented.) values for the nonlinear regression analysis performed on experimental results were greater than 0.90. This study highlights the effectiveness of neural network expressions to predict the compressive strength of CR-confined concrete. A strength reduction (ratio of full wrap and strip wrap height CRs) factor of 0.67 was proposed and used for strip-wrapped specimens. It was seen that the neural network models also predicted the compressive strength of partially wrapped specimens with reasonable accuracy using the strength reduction factor.