Automated diagnosis of bridge expansion joint defects using voiceprint features and deep learning

Key Points

• Automated diagnosis effectively detects defects in bridge expansion joints, demonstrating high accuracy and efficiency.
• The study achieved an impressive 90% accuracy rate while assessing various voiceprint features within datasets.
• Analysis used voiceprint features along with deep learning algorithms to improve diagnosis precision in infrastructure.
• This approach highlights the potential for technology to enhance bridge safety evaluations, prompting further adoption.