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Massive Point Cloud Processing for Efficient Construction Quality Inspection and Control
Zhansheng Liu1,2, Zehong Liu1,2, Zhe Sun1,3
1College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel, Switzerland)
|November 9, 2024
Summary
Optimizing civil engineering data processing involves comparing artificial intelligence algorithms for spatiotemporal data. Selecting appropriate point cloud sampling, filtering, and registration methods enhances efficiency and accuracy in construction quality control.
Area of Science:
- Civil Engineering
- Geospatial Data Science
- Artificial Intelligence in Construction
Background:
- Large-scale civil infrastructure projects generate massive spatiotemporal datasets for management and control.
- Current AI-based data processing requires extensive manual parameter tuning by engineers, proving inefficient for large datasets.
- A lack of unified datasets hinders comparative analysis of different data processing algorithms.
Purpose of the Study:
- To propose a framework and evaluation system for comparing data processing policies for massive spatiotemporal data in construction quality control.
- To evaluate the performance of combined algorithms for processing large-scale point cloud data.
- To identify optimal point cloud processing strategies based on registration accuracy and data fidelity.
Main Methods:
- Development of a novel framework and evaluation system for comparing data processing strategies.
- Comparative analysis of various combinations of point cloud algorithms (sampling, filtering, registration).
- Performance evaluation based on registration accuracy and data fidelity metrics.
Main Results:
- Demonstrated that strategic combinations of point cloud sampling, filtering, and registration algorithms significantly boost processing efficiency.
- Validated that optimized strategies meet engineering requirements for data accuracy and completeness.
- The proposed framework effectively evaluates and compares different data processing policies.
Conclusions:
- The selection of appropriate point cloud processing algorithm combinations is crucial for efficient and accurate handling of massive spatiotemporal data in civil engineering.
- The developed framework provides a systematic approach for optimizing data processing methods in construction quality control.
- This research offers a scalable solution for processing large volumes of point cloud data and selecting optimal processing techniques.
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