Clustering-Based Active-Learning Kriging Reliability Analysis of FRP-Strengthened RC Beams with Random Finite-Element to Model Spatial Variability

Abstract

This paper presents a framework for assessing the reliability of fiber reinforced polymer (FRP)–strengthened reinforced concrete (RC) beams in flexure using stochastic nonlinear finite-element (SNFE) analysis and k-w-means clustering, based on active-learning kriging Monte Carlo simulation (AK-MCS), in which spatial variations in the concrete and bond material properties are considered. A computer algorithm was developed to augment commercially available nonlinear finite-element (FE) analysis software and automate the process for conducting the SNFE clustering-based AK-MCS analysis. The k-w-means clustering was based on the U learning function to provide multipoint enrichment to improve convergence of the stopping criteria by allowing parallel computation of the SNFE models. Parametric analysis indicated the accuracy of the reliability prediction of the examined member and proved the efficiency of the proposed analysis in reducing the number of calls to SNFE models compared with data in the existing literature, when using probability-based stopping criteria. The quality of the FRP-to-concrete bond is affected by the integrity of the concrete at the interface, which varies across the dimensions of the strengthened member, causing added uncertainty in predicting the structural response, and hence the reliability of the FRP-strengthened member. This study proposes a computationally efficient approach to assess the reliability of FRP-strengthened concrete members by considering the spatial variation in the concrete properties (compressive strength, tensile strength, bulk modulus) and the quality of the FRP-to-concrete bond (shear and normal bond strength) by using an adaptive machine-learning technique. The proposed framework may be utilized by engineers to design FRP-strengthening systems for concrete members experiencing variation in the concrete properties due to poor quality control or active deterioration.