Broken wire monitoring and identification of prestressed steel strand using acoustic emission technique and deep learning

This publication has 43 references indexed in Scilit:

• Machine learning-assisted acoustic emission localization of simulated wire-break events in prestressed strands of high-speed railway box girders
  Advanced Engineering Informatics, 2026
• Deep learning-enhanced nonlinear ultrasonic identification of concrete microcrack development
  Construction and Building Materials, 2025
• Compressive strength prediction of sleeve grouting materials in prefabricated structures using hybrid optimized XGBoost models
  Construction and Building Materials, 2025
• A Non-Metallic pipeline leak size recognition method based on CWT acoustic image transformation and CNN
  Applied Acoustics, 2024
• Improving machine learning based phase and hardness prediction of high-entropy alloys by using Gaussian noise augmented data
  Computational Materials Science, 2023
• Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete
  Construction and Building Materials, 2022
• Damage pattern recognition and damage evolution analysis of unidirectional CFRP tendons under tensile loading using acoustic emission technology
  Composite Structures, 2020
• Fracture monitoring and damage pattern recognition for carbon nanotube‐crumb rubber mortar using acoustic emission techniques
  Structural Control and Health Monitoring, 2019
• A novel acoustic emission monitoring method of cross-section precise localization of defects and wire breaking of parallel wire bundle
  Structural Control and Health Monitoring, 2019
• Damage assessment of corrosion in prestressed concrete by acoustic emission
  Construction and Building Materials, 2013