Predicting Chloride Ingress Profiles and Strength of Marine Concrete Using Data-Driven and Multiphysics Frameworks

Abstract

Chloride diffusion in reinforced concrete is a crucial factor in assessing infrastructure degradation, especially in marine environments where prolonged exposure to chloride-rich seawater accelerates deterioration. However, obtaining accurate time-dependent measurements of chloride concentration in RC presents a significant challenge due to constraints in both available workers and advanced instrumentation; in addition, replicating real-world environmental conditions in a laboratory setting is inherently difficult. The inherent complexity of concrete mixture designs—coupled with the variability of environmental parameters—further complicates the development of a practical model capable of reliably estimating chloride concentration at varying concrete depths. This study develops a data-driven approach to predict chloride concentrations at different depths and chloride diffusion coefficients in marine concrete formulated with common supplementary cementitious materials under varied environmental conditions and exposure durations. Additionally, a transfer learning technique is developed to accurately predict the compressive strength of marine concrete using a limited data set. This approach allows the model to extract broad correlations from a comprehensive concrete database while simultaneously capturing customized patterns specific to marine concrete. By doing so, it enhances the generalizability of existing models while significantly reducing the time required to develop new ones. Furthermore, this study leverages capillary porosity—obtained from thermodynamic simulations—as a critical intermediary for establishing correlations between compressive strength and chloride diffusion coefficients. This feature can be integrated into existing machine learning models for concrete compressive strength to enable prediction of both compressive strength and chloride diffusion coefficients.