Construction Steel Structure Inspection Method Based on Deep Learning and Digital Twin Technology
To address the issue of low efficiency in traditional methods for detecting defects in building steel structures,this research improves the Fast Region Convolutional Neural Network(Fast R-CNN)by using a Feature Pyramid Network(FPN)and element-wise addition.By integrating this with digital twin technology,a detection method for building steel structures based on deep learning and digital twin technology is proposed.The results show that the detection accuracy for different sizes is 0.78,0.81,and 0.82,respectively,proving its high reliability.This indicates that the designed method can accurately identify defects in steel components.The research findings can be applied in the field of structural health monitoring of buildings,providing strong technical support for risk warning and maintenance of steel structuresa.
Building Steel StructureDefect DetectionFast Region Convolutional Neural NetworkDigital Twin Technology
万方数据
维普
