Case Studies in Construction Materials (Dec 2024)
Investigating chloride-induced corrosion in reinforced concrete structures using laser-induced breakdown spectroscopy
Abstract
Reinforced concrete structures face significant annual expenditures in combating rebar corrosion, with chloride penetration identified as a major contributor. This study employs laser-induced breakdown spectroscopy (LIBS) to quantitatively predict the chloride-induced corrosion rate in such structures. Eighty samples, characterized by known chloride content (ranging from 0 % to 1.0 %), underwent controlled corrosion processes with predetermined degrees measured as bar weight loss. The investigation included two cement types, Type I and Type V, along with samples incorporating pozzolanic materials (FA, SF, and GGBFS,) in addition to plain OPC samples. LIBS was subsequently employed to detect chlorine presence in each concrete sample, recording the corresponding intensity of the chlorine line. After the initial data acquisition, peak analysis was performed to identify the dominant spectral peaks, ensuring that the most relevant signals were isolated. Following this, the intensity of the chloride emission line from the LIBS spectrum was recorded and correlated with its corresponding chloride signal, establishing a quantitative relationship between the LIBS output and chloride presence. A model was then developed to link the LIBS signal intensity to its corresponding chloride concentration in the sample. In parallel, the corrosion rate associated with each specific chloride concentration was measured and then linked to the LIBS-derived chloride concentrations, creating a framework that ties the LIBS output to the actual rebar degradation within the concrete. Comparisons with previous studies demonstrated superior conformity of the developed models. The study also presents optimized parameters for the LIBS setup. Notably, results indicated a high correlation between LIBS intensity and chloride concentration, with the developed model accurately predicting the degree of corrosion.
