Shell Model Reconstruction of Thin-Walled Structures from Point Clouds for Finite Element Modelling of Existing Steel...

Area of Science:

• Civil Engineering
• Computational Mechanics
• Geospatial Data Analysis

Background:

• Digital twin models using point cloud data are crucial for bridge maintenance and performance assessment.
• Existing methods often focus on solid elements, limiting their application to complex structures like bridges that require beam and shell elements.
• There is a need for systematic approaches to generate shell element finite element (FE) models from point cloud data for thin-walled civil structures.

Purpose of the Study:

• To propose a systematic methodology for constructing shell element FE models from point cloud data of thin-walled structural members.
• To validate the accuracy of the generated FE models through experimental and comparative analyses.
• To evaluate the impact of point cloud data quality on FE model geometry and analysis outcomes.

Main Methods:

• Point cloud segmentation using k-means clustering.
• Neutral plane estimation via principal component analysis.
• Geometric structure determination using edge detection based on normal vector variations.

Main Results:

• Validation experiments showed dimensional errors up to 5 mm and angular errors up to 6° for a steel corner specimen.
• Static load analysis yielded good accuracy, with maximum displacement errors within 3.8% and maximum stress errors within 7.7%.
• Point cloud data quality significantly influences FE model accuracy, with density variations reducing neutral plane estimation precision.

Conclusions:

• The proposed methodology provides a systematic approach for generating shell element FE models from point cloud data.
• The study establishes a validation framework for FE models derived from point cloud data, ensuring appropriate abstraction for structural analysis.
• Findings highlight the importance of data quality assessment for reliable digital twin applications in bridge engineering.