What question did this study set out to answer?

To develop a multimodal neural network framework for predicting reinforcement corrosion in reinforced concrete structures using limited data.

March 30, 2026Open Access

Multimodal NDT-integrated neural network with cGAN augmentation for predicting reinforcement corrosion in RC structures

Puntos clave

To develop a multimodal neural network framework for predicting reinforcement corrosion in reinforced concrete structures using limited data.
Integrated material, environmental, and non-destructive testing features into a neural network framework.
Employed a conditional generative adversarial network for data augmentation.
Monitored controlled RC specimens to establish corrosion levels for validation.
Achieved an RMSE of 3.55 and R² of 0.96, outperforming conventional models.
Demonstrated predictive stability using only 30% of real data.
Enabled proactive maintenance planning and resource allocation based on predicted corrosion risk.

Resumen

Reinforcement corrosion is a major deterioration mechanism that compromises the long-term safety of reinforced concrete (RC) structures. However, existing non-destructive testing (NDT) techniques struggle to detect early-stage corrosion and suffer from limited data availability, constraining the reliability of data-driven models. To address these gaps, this study proposes a novel multimodal artificial neural network (ANN) framework that integrates material, environmental, and NDT features while employing a conditional generative adversarial network (cGAN) for data augmentation. This integration allows the model to learn corrosion-related patterns even under data-scarce conditions. Controlled RC specimens exposed to inland and coastal environments were monitored to establish ground-truth corrosion levels. The augmented model achieved a root mean square error (RMSE) of 3.55 and an R² of 0.96, outperforming conventional models by maintaining predictive stability with only 30% of real data. Furthermore, the predicted corrosion probability and deterioration levels derived from the proposed multimodal ANN + cGAN framework can be directly linked to infrastructure maintenance decision-making. Structures or components with a higher predicted corrosion risk can be prioritized for detailed inspection or preventive repair, while low-risk areas can be scheduled for routine monitoring. This AI-based prioritization enables more efficient allocation of inspection resources and maintenance budgets, supporting condition-based maintenance rather than traditional time-based inspection cycles. ● Multimodal dataset combining material, environmental, and NDT features was developed. ● ANN accurately predicted reinforcement corrosion with RMSE as low as 2.55 and R² = 0.98. ● cGAN-based augmentation preserved cross-modal correlations in synthetic datasets. ● Augmented training achieved an RMSE of 3.55 with only 30% of the original data. ● Framework supports data-driven corrosion assessment and proactive maintenance planning.

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Cite This Study

Lee et al. (Sat,) studied this question.

synapsesocial.com/papers/69c9c5e2f8fdd13afe0bdf07 https://doi.org/https://doi.org/10.1016/j.conbuildmat.2026.146058

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