Prediction of axial capacity of corrosion-affected RC columns strengthened with inclusive FRP

Abstract

Abstract The primary cause behind the degradation of reinforced concrete (RC) structures is the propagation of corrosion in the steel-RC structures. Nowadays, numerous retrofitting techniques are available in the construction sector. Fiber-reinforced polymer (FRP) is one of the efficient rehabilitation measures that can be implemented on corroded structures to enhance structural capacities. However, the estimation of axial strength of FRP-strengthened columns affected by corrosion has been a challenging and tedious task in the laboratory as well as on the site. Considering such shortcomings, the prediction of axial capacity can be done using various analytical methods and artificial intelligence (AI) techniques. In this study, a comprehensive dataset of circular columns was extracted from the literature to predict the axial strength of FRP-wrapped and unstrengthened RC corroded columns. The laboratory results from the assembled dataset were compared to corresponding values estimated using relevant design codes provided by American Concrete Institute (ACI 440.2R-17 and ACI 318-19), and Bureau of Indian Standard (IS 456:2000). Five machine learning models were employed on columns to predict the axial load carrying capacity of FRP-strengthened and un-strengthened RC corroded columns. The results discovered that the extreme gradient boosting (XGBoost) model achieves superior accuracy with the least errors and could be used by the scientific community and FRP applicators to forecast the axial performance of corroded columns strengthened with and without FRP. The findings from the design codes revealed that prediction errors were available in high margins. Furthermore, feature importance analysis was conducted using the Shapley Additive exPlanation algorithm to know the contribution and influence of each input parameter on axial capacity. The feature analysis found that unconfined compressive strength of concrete plays an important role in deciding the axial capacity of columns. Moreover, to enhance the precision of axial capacity computation and improving the overall efficacy in engineering practice, a web-based user-friendly interface was developed for FRP applicators and engineers to simplify the process.

Keywords

• RC column
• FRP
• Corrosion
• Machine learning
• XGBoost
• Strengthening