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TLS data for cracks detection in building walls.
Paulina Stałowska1, Czesław Suchocki1
1Faculty of Civil Engineering, Environmental and Geodetic Sciences, Koszalin University of Technology, Śniadeckich 2, Koszalin 75-453, Poland.
Data in Brief
|May 23, 2022
Summary
Terrestrial laser scanning (TLS) can detect building cracks by analyzing 3D coordinates and laser reflection intensity. This study develops a methodology for improved crack detection in civil engineering applications.
Area of Science:
- Civil Engineering
- Geomatics
- Non-destructive Testing
Background:
- Building deterioration is a significant concern in civil engineering.
- Terrestrial Laser Scanning (TLS) is a popular non-destructive remote sensing technique.
- TLS can capture 3D spatial coordinates and radiometric intensity data.
Purpose of the Study:
- To explore the full potential of TLS for detecting building wall cracks.
- To investigate the influence of scanner technical parameters and measurement conditions on crack detection accuracy.
- To develop an optimized methodology for crack detection using TLS.
Main Methods:
- Utilized TLS to capture point cloud data, including 3D coordinates and intensity values.
- Analyzed both spatial and radiometric data for enhanced crack detection.
- Examined the impact of scanner parameters and geometric conditions on measurement results.
- Collected datasets from sample objects with cracks and real buildings in poor condition.
Main Results:
- Combined analysis of 3D coordinates and intensity data improves crack detection reliability.
- Identified key technical and geometric factors affecting TLS performance for crack detection.
- Demonstrated the possibilities and limitations of TLS in assessing building wall integrity.
- Validated the developed methodology on real-world structures.
Conclusions:
- TLS is a valuable tool for non-destructive detection of building cracks.
- Integrating intensity data with 3D coordinates enhances detection accuracy.
- A refined measurement methodology optimizes TLS application for structural health monitoring.
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