Neuro Graph Temporal Fusion Network for predicting corrosion rate in marine-exposed reinforced concrete structures

Abstract

Reinforced concrete (RC) structures exposed to marine environments are highly susceptible to chloride-induced corrosion, which can cause premature deterioration, cracking, spalling, reduced service life, and increased maintenance costs. While advances in sensor-based systems, non-destructive testing, imaging techniques, and probabilistic life-cycle models have improved early detection, most existing methods rely on single-modality measurements, simplified assumptions, or fail to capture the complex spatio-temporal dynamics of corrosion. To address these limitations, this study proposes the Neuro Graph Temporal Fusion Network (NGTFNet), a unified framework for corrosion prediction in marine RC structures. NGTFNet integrates multimodal sensing data, including optical, thermal, ultrasonic, and electrochemical inputs using Convolutional Neural Networks (CNN)-based feature extraction, graph-based modeling to capture spatial deterioration patterns, and temporal fusion for life-cycle forecasting. The proposed approach enables the accurate estimation of corrosion rates and service life under dynamic marine exposure, representing a significant step toward the resilient and sustainable monitoring of marine RC structures.

Graphical abstract

Keywords

marine environment / RC structures / CNN / NGTFNet / corrosion rate prediction / deterioration

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