Structural damage detection and safety assessment method based on machine vision and machine learning.

ABSTRACT: Structural damage detection and health assessment are crucial for maintaining infrastructure safety and durability. This study presents a novel multi-scale vision-based framework that combines deep learning and machine learning for accurate and interpretable structural safety evaluation. Specifically, we integrate ResNet-50 and SegFormer models to jointly achieve coarse-level damage classification and fine-grained pixel-level segmentation. Seven key damage parameters are quantitatively extracted from high-resolution images-such as crack length, spalling area, and rebar exposure-and serve as interpretable features for safety assessment. A Random Forest (RF) model is developed to establish a nonlinear mapping from these visual features to structural safety levels. Experimental results demons

SUBMITTER: Wang S 

PROVIDER: S-EPMC12863553 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature