Work

Automatic PAUT crack detection and depth identification framework based on inspection robot and deep learning method

Year: 2024

Type: article

Abstract: Orthotropic steel bridge decks (OSD) are widely acclaimed for their lightweight, high load-carrying capacity, and adaptability, making them a popular choice in steel structure bridges. However, the complex nature of their structure makes them susceptible to fatigue cracking, posing significant safety concerns. To address the issues above, this study employs a robot equipped with an ultrasonic phased array probe to automate the detection of internal cracks within Orthotropic Steel Decks (OSD). A Deep Convolutional Generative Adversarial Network (DCGAN) is utilized to augment the training dataset of Phased Array Ultrasonic Testing (PAUT) images. The YOLO series algorithms are applied and compared for crack localization, with YOLO v7-tiny exhibiting the highest accuracy and speed. Integrating attention mechanisms into the YOLO v7-tiny algorithm to facilliate rapid and high-precision crack detection. Analyzing the echo region with an echo intensity bar enabled the identification of crack depth, with an identification error within 5%.

Source: Journal of Infrastructure Intelligence and Resilience

DOI: 10.1016/j.iintel.2024.100113

Authors: Fei Hu, Hong-ye Gou, Hao-zhe Yang, Huan Yan, Yi-qing Ni, You-wu Wang

Institutions: Southwest Jiaotong University, China Railway Major Bridge Reconnaissance & Design Institute (China), Hong Kong Polytechnic University

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,600