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Volume 55 Issue 7
Jul.  2025
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Article Contents
LIAN Saisai, LIU Xiaolan, WANG Jian, LU Peng, DING Daiwei. Development Status of Detection and Evaluation Techniques for Hidden-Frame Glass Curtain Walls in Existing Buildings[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 79-86. doi: 10.3724/j.gyjzG25031704
Citation: LIAN Saisai, LIU Xiaolan, WANG Jian, LU Peng, DING Daiwei. Development Status of Detection and Evaluation Techniques for Hidden-Frame Glass Curtain Walls in Existing Buildings[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 79-86. doi: 10.3724/j.gyjzG25031704

Development Status of Detection and Evaluation Techniques for Hidden-Frame Glass Curtain Walls in Existing Buildings

doi: 10.3724/j.gyjzG25031704
  • Received Date: 2025-03-17
    Available Online: 2025-09-12
  • Glass curtain walls are widely used in modern buildings, but they are prone to safety hazards such as structural adhesive aging and debonding due to long-term exposure to wind loads and temperature changes. To this end, the representative results of domestic and foreign hidden frame glass curtain wall safety testing technology and analysis methods and evaluation system are systematically sorted out. For detection, vibration analysis identifies adhesive damage through changes in natural frequency, ultrasonic testing evaluates damage severity using nonlinear coefficients, and infrared inspection detects thermal anomalies in adhesives despite its sensitivity to environmental interference. In analysis, test data can be used to assess the safety of glass curtain walls, while finite element models can locate debonding damage through modal curvature. For evaluation, a fuzzy comprehensive method integrates multiple indicators, and neural networks classify safety levels based on dynamic parameters. Aiming to address the existing problems of insufficient detection accuracy and poor model adaptability, future research should focus on integrating vibration, ultrasonic, and infrared technologies, investigating adhesive aging in relation to environmental factors for long-term performance prediction, and creating intelligent real-time monitoring systems to improve early-warning capabilities for potential hazards.
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