Automated processing of large point clouds for structural health monitoring of masonry arch bridges

Abstract Laser scanning technology is gaining popularity in a wide range of applications due to the increasing accuracy and speed at which data can be collected and an increase in laser scan data processing tools. Nevertheless, manual operations for specific applications are time consuming and can require high-performance computers to produce suitable models for further operations. Thus, laser scan data are underused in the civil engineering community. New procedures that automate the data processing for specific but repetitive infrastructure typologies are required to make full use of the technology as a basic tool for infrastructure assessment and asset management. This paper presents a new method for fully automated point cloud segmentation of masonry arch bridges. The method efficiently creates segmented, spatially related and organized point clouds, which each contain the relevant geometric data for a particular component (pier, arch, spandrel wall, etc.) of the structure. The segmentation is based in the combination of a heuristic approach and image processing tools adapted to voxel structures. The proposed methodology provides the essential processed data required for structural health monitoring of masonry arch bridges based on geometric anomalies. The method was validated using a representative sample of masonry arch bridges. The results demonstrate that this tool can provide data for further structural operations without requiring neither training in laser scanning technology nor high-performance computers for such data processing.

[1]  Pedro Arias,et al.  Automatic Morphologic Analysis of Quasi‐Periodic Masonry Walls from LiDAR , 2016, Comput. Aided Civ. Infrastructure Eng..

[2]  Jacques Heyman,et al.  The safety of masonry arches , 1969 .

[3]  Belén Riveiro,et al.  A novel approach to evaluate masonry arch stability on the basis of limit analysis theory and non-destructive geometric characterization , 2013 .

[4]  Debra F. Laefer,et al.  Combining an Angle Criterion with Voxelization and the Flying Voxel Method in Reconstructing Building Models from LiDAR Data , 2013, Comput. Aided Civ. Infrastructure Eng..

[5]  Henrique Lorenzo,et al.  Ancient Stone Bridge Surveying by Ground-Penetrating Radar and Numerical Modeling Methods , 2014 .

[6]  Paulo B. Lourenço,et al.  Geometric issues and ultimate load capacity of masonry arch bridges from the northwest Iberian Peninsula , 2010 .

[7]  Debra F. Laefer,et al.  Reliability of Crack Detection Methods for Baseline Condition Assessments , 2010 .

[8]  Pedro Arias,et al.  Photogrammetric 3D modelling and mechanical analysis of masonry arches: An approach based on a discontinuous model of voussoirs , 2011 .

[9]  P. Morer,et al.  The effect of geometry on the structural capacity of masonry arch bridges , 2012 .

[10]  Pedro Arias,et al.  Terrestrial laser scanning and limit analysis of masonry arch bridges , 2011 .

[11]  R. Livesley.  Limit analysis of structures formed from rigid blocks , 1978 .

[12]  Falko Kuester,et al.  Terrestrial Laser Scanning-Based Structural Damage Assessment , 2010, J. Comput. Civ. Eng..

[13]  Hanan Samet,et al.  A general approach to connected-component labeling for arbitrary image representations , 1992, JACM.

[14]  Nicolas David,et al.  Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge , 2013 .

[15]  Hanan Samet,et al.  Efficient Component Labeling of Images of Arbitrary Dimension Represented by Linear Bintrees , 1988, IEEE Trans. Pattern Anal. Mach. Intell..