Bridge Girder Point Cloud Fusion for Vibration-Robust Geometry Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional detection methods for bridge in-service geometric form, such as the total station, level gauge, and GPS, are inefficient and cannot obtain complete spatial information due to complex vibration noise in point cloud data, making it difficult to accurately recognize the true operation state of bridges.
Innovation Solution
A bridge in-service geometric form recognition method based on multi-point cloud fusion, involving multiple three-dimensional laser scans, coordinate system adjustment, and regional point cloud reconstruction to obtain a continuous and smooth theoretical girder point cloud, using algorithms like the vibration center theory and grid point cloud generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If three-dimensional laser scanning is used to obtain point cloud data, then complete three-dimensional coordinate information is obtained, but complex vibration noise is introduced due to bridge vibration under load
Solution Approach 1:
The patent segments the noisy point cloud data into multiple groups based on spatial proximity and vibration characteristics. By dividing the data into clusters that represent different vibration states, the method can separately analyze and fuse these segments to reconstruct the true geometric form, thereby resolving the contradiction between obtaining complete spatial information and maintaining measurement precision under vibration conditions
Solution Approach 2:
The patent merges multiple point cloud datasets obtained from repeated scanning operations. By combining data from multiple scans that capture different vibration states, the method reconstructs a more accurate representation of the bridge's true geometric form, effectively canceling out vibration-induced noise while preserving complete spatial information
2Measurement precision
If multiple times of three-dimensional laser scanning are performed to reduce vibration noise, then recognition accuracy is improved, but detection time increases
Solution Approach 1:
The patent performs a predetermined number of repeated scans (excessive action) to ensure sufficient data for noise reduction, then processes only the essential portions of this data through efficient clustering algorithms. This approach achieves high recognition accuracy while minimizing unnecessary processing time by focusing computational resources on the most critical data segments
3Device complexity
If conventional detection methods such as total station and level gauge are used, then detection equipment is simple, but only limited single-point information is obtained and complete spatial information cannot be acquired
Solution Approach 1:
The patent uses three-dimensional laser scanning to create a digital point cloud copy of the bridge structure. This digital replica contains complete spatial information without requiring complex physical measurement equipment at every point. The point cloud data serves as a comprehensive digital twin that preserves all geometric details while simplifying the detection process compared to conventional point-by-point measurement methods
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately recognizes the bridge's true operation condition by addressing complex vibration noise, providing high-confidence recognition and improved control accuracy of the bridge's service state.
Implementation Method 1
performing multiple times of three-dimensional laser scanning on a bridge structure
Data Source
AI summary
A bridge in-service geometric form recognition method based on multi-point cloud fusion comprises: defining a bridge in-service geometric form; obtaining multi-point cloud data of a bridge girder in different service periods under the condition of not stopping traffic; converting bridge three-dimensional point cloud data obtained by multiple times of scanning to a same coordinate system; fusing the point cloud data obtained by multiple times of scanning using a regional point cloud fitting algorithm to obtain a continuous and smooth theoretical girder point cloud reflecting a true spatial form of a bridge; and extracting three-dimensional coordinates of all points at any target transverse position in the theoretical girder point cloud in a span extension direction to obtain the bridge in-service geometric form.