This paper uses point cloud-based analysis to map and interpret long-term deformations in skewed masonry arch bridges, linking observed geometry to underlying kinematic mechanisms and challenging assumptions of rigid boundary conditions in skewed arch bridge assessment.
In this paper the authors introduce a machine learning alternative to computationally intensive optimisation in shell form-finding, enabling efficient design under both vertical and horizontal loads, with significantly reduced computation time.
This library contains point cloud geometric data for 16 ageing masonry arch bridges and viaducts in the UK, obtained using FARO Focus 3D laser scanners.
This 'ArchImageLib' dataset contains image data for 13 masonry arch railway bridges and viaducts in the UK, a valuable resource for the training and validation of algorithms for automated inspection and damage classification.
This paper presents an enhanced method, grounded in membrane equilibrium analysis (MEA) and the static theorem of limit analysis, to address a key gap in conventional analysis methods for compression-only shells and vaults.
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