A Deep Learning Framework for Corrosion Assessment of Steel Structures Using Inception v3 Model

Corrosion detection plays a crucial role in the effective lifecycle management of steel structures, significantly impacting maintenance strategies and operational performance. This study presents a machine vision-based approach for classifying corrosion levels in Q235 steel, providing valuable insights for lifecycle assessment and decision-making. Accelerated salt spray tests were performed to simulate corrosion progression over multiple cycles, resulting in a comprehensive dataset comprising surface images and corresponding eight loss measurements. A comparative evaluation with other architectures, namely, AlexNet, ResNet, and VggNet, demonstrated that the Inception v3 model achieved superior classification accuracy, exceeding 95%. This method offers an effective and precise solution for corrosion evaluation, supporting proactive maintenance planning and optimal resource allocation throughout the lifecycle of steel structures. By leveraging advanced deep learning techniques, the approach provides a scalable and efficient framework for enhancing the sustainability and safety of steel infrastructure.

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