The deterioration of reinforced concrete (RC) structures is a significant global concern. Demolishing degraded structures is costly, time-consuming, and impacts the integrity of connected members. Implementing jackets, including fiber-reinforced polymer (FRP), fiber-reinforced cementitious matrix (FRCM), and ferro-cement, for the retrofitting and rehabilitation of existing structural members presents an advanced alternative to the deteriorated RC structures. As compared to FRP and FRCM, the ferro-cement is an economical solution that improves the mechanical strength of the structural members, service life, and offers fire resistance, durability, and versatility. This research evaluated the axial load-carrying capacity (ALCC) of ferro-cement confined RC columns using existing analytical models and machine learning (ML) algorithms. A database of 151 RC column specimens from literature was utilized. Four ML models (Artificial Neural Network, Decision Tree, Linear Regression, and Support Vector Machine) were developed to predict the axial capacity of ferro-cement confined RC columns. The ANN model performed the best out of all the ML models. The developed model was precise, user-friendly, and cost-effective. The ANN-based mathematical model can be easily used by structural engineers and ferro-cement jacketing designers to estimate the axial capacity of the ferro-cement strengthened RC columns.
Prediction of Axial Capacity of RC Columns Reinforced with Ferro-cement Jacketing: A Data-driven Machine Learning Strategy
KSCE J Civ Eng
KSCE Journal of Civil Engineering ; 28 , 9 ; 3835-3844
2024-09-01
10 pages
Article (Journal)
Electronic Resource
English
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