Autonomous damage segmentation of post-fire reinforced concrete structural components

Abstract

The existing methods for detecting fire damage in reinforced concrete (RC) structures rely heavily on manual visual inspection, which is time-consuming and challenging due to poor visibility and safety concerns. To address this, an automatic damage segmentation network at the pixel level is proposed. Using a dataset of 403 high-resolution images of fire-damaged RC components, the study employed MobileNetv3 as the encoder in a UNet model, enhanced with three average pooling layers. Among four submodules tested, SPPF demonstrated the best performance in the MB-SPPF-UNet network, achieving an MIoU of 82%, a 7% improvement over the Baseline-UNet. While OCRNet showed the best performance among ten state-of-the-art networks, its MIoU was still 8% lower than MB-SPPF-UNet. The generalization capabilities of the networks were evaluated using four crack datasets, with MB-SPPF-UNet showing a performance decrease of 5%-10%. However, this is acceptable given its primary application in segmenting post-fire concrete spalling and exposed reinforcement. The study aims to establish an efficient damage segmentation network using deep learning for quick assessment and reference in firefighting, rescue, and structural repair efforts.