Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures

Area of Science:

• Civil Engineering
• Computer Vision
• Structural Health Monitoring (SHM)

Background:

• Continuous monitoring of civil infrastructure is vital for public safety and structural longevity.
• Image-processing technologies are increasingly important for effective structural health monitoring (SHM).

Purpose of the Study:

• To provide a comprehensive review of image processing in SHM.
• To discuss advancements, applications, and challenges in this field.
• To highlight the potential of image-based approaches for researchers and professionals.

Main Methods:

• Review of various imaging techniques: satellite imagery, LiDAR, optical cameras, and non-destructive testing.
• Exploration of image processing applications: damage detection, crack identification, deformation monitoring.
• Investigation of AI and machine learning integration with image processing for SHM.

Main Results:

• Image processing offers powerful tools for SHM, enabling detailed structural assessment.
• Integration with AI/ML enhances automation and accuracy in damage detection and monitoring.
• Diverse imaging techniques contribute to a holistic approach to structural health.

Conclusions:

• Image-based SHM is a rapidly advancing field with significant potential.
• Further integration of AI/ML will drive innovation and efficiency in infrastructure monitoring.
• This review consolidates current knowledge, guiding future research and practice in image-based SHM.