Title: Uncertainty Modeling of Carbon Fiber-Reinforced Polymer- Confined Concrete in Acid-Induced Damage
Author(s): Yail J. Kim, Yongcheng Ji, and Troy Butler
Publication: Structural Journal
Appears on pages(s): 97-108
Keywords: fiber-reinforced polymer (FRP); inverse problem; mathematical modeling; stochastic; sulfuric acid; uncertainty quantification
This paper presents a novel modeling approach to predict the response of parameters constituting the strength of concrete cylinders confined with carbon fiber-reinforced polymer (CFRP) sheets under an acidic environment (pre-conditioned concrete is strengthened). Contrary to conventional modeling that requires specific parameter values to solve for the strength of the confined concrete, the stochastic inverse method mathematically infers individual parameter values without prior information based on a known confined strength. Accordingly, the implications of each parameter on the strength development of the confined concrete are quantified. The modeling approach is validated against a previously conducted experimental program and is employed for parametric investigations with various geometric and material properties as well as with variable sulfuric acid exposure periods. The cylinder diameter affects the surety of the strength variation in terms of occurrence probability. The rate of strength decrease in the confined concrete is pronounced when the core concrete has been initially exposed to sulfuric acid, while the rate slows down as the exposure time progresses. High-strength CFRP materials noticeably increase the strength of the confined concrete; however, the efficiency of enhancement diminishes with the CFRP strength. The functionality of the concrete confined by multiple CFRP layers is examined. Upon assessing the empirically calibrated factors of existing design guidelines, new factors are proposed for strength prediction of the confined concrete. To substantiate the distinct effects of the sulfuric acid exposure time, the strength of the confined concrete is characterized by a Euclidean distance-based clustering technique. The uncertainty associated with the CFRP-confinement is elucidated and contributing attributes are identified.