Forecasting and increasing corrosion resistance of concrete in aggressive environments: integration of modern approaches and artificial intelligence

Abstract

Corrosion of reinforced concrete structures, especially under the influence of aggressive environmental factors (sea water, sulfates, chlorides), is one of the most pressing problems of modern construction. Current research focuses on structural, physical-chemical and digital methods for predicting and minimizing corrosion. The main structural parameters that influence the resistance of concrete to corrosion are density, porosity and composition of binders. This article presents an overview of developments in the field of increasing the corrosion resistance of concrete through the use of mineral additives, accelerated testing methods, diffusion process modeling, and the introduction of artificial intelligence technologies. Practical and theoretical aspects are considered, including the determination of the chloride threshold, the development of peridynamic models, machine learning methods and approaches to the design of structural durability under climate change. The review is based on modern sources, reflecting both fundamental and applied aspects of increasing the durability of concrete. To summarize scientific data, the method of systematic literature analysis was used.