Work

Data‐Driven Prognostic Framework for Chloride‐Induced Corrosion in Aging Concrete Structures: Integrating Minimal Field Data With Mix‐Design Inference

Year: 2026

Type: article

Abstract: Accurate evaluation of chloride‐induced corrosion in reinforced concrete (RC) structures is essential for effective maintenance, yet conventional diagnostic approaches require full‐depth coring and laboratory testing to determine chloride profiles and diffusion coefficients. These procedures are costly and invasive for large‐scale routine inspection. This study proposes a data‐driven framework that enables corrosion initiation assessment using only surface chloride measurements combined with machine‐learning (ML)–based estimation of chloride diffusion coefficients. To address the large uncertainty associated with unknown mix proportions in existing structures, regional mix‐design statistics were incorporated to constrain ML model inputs and reduce prediction variability. The resulting diffusion coefficient distributions were then integrated into a probabilistic chloride ingress model based on fib Model Code 2010, allowing explicit propagation of uncertainties in material properties, cover depth, and environmental exposure through Monte Carlo simulation. Field validation was conducted using concrete cores extracted from roadside barriers and bridge components located in inland and coastal environments. Comparison between predicted and measured chloride profiles demonstrated that the proposed approach successfully captures the range and depth of chloride penetration observed in practice. The results indicate that this minimally invasive method can support early‐stage depassivation probability screening, optimize locations requiring detailed testing, and enhance decision‐making in infrastructure asset management. It should be noted that this approach assesses the risk of corrosion initiation (i.e., depassivation onset) and not the actual condition or section loss of the embedded reinforcement.

Source: Structural Control and Health Monitoring

DOI: 10.1155/stc/7479965

Authors: Jiyoung Min, Woldeamanuel Minwuye Mesfin, Jaehwan Kim, Ki-Tae Park, Gun Kim, Hyeong-Ki Kim

Institutions: Korea Institute of Civil Engineering and Building Technology, Chosun University, Nanyang Technological University, Kookmin University

Language: English

Topic: Concrete Corrosion and Durability

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,490