This paper presents the modeling and monitoring work related to the mediaeval bridge Azzone Visconti in Lecco (the "old bridge"). Metric information was acquired with different instruments and techniques for an integrated digital documentation that combines observations with variable precision: ±0.1 mm for the analysis of small vertical movements and subcentimeter precision for the geometrical reconstruction. The complexity of the bridge, with irregular constructive elements and different materials, required a detailed geometrical survey with laser scanning and photogrammetric techniques for the generation of plans and sections along with an accurate 3D parametric representation based on Building Information Modeling. A strategy based on NURBS curves and surfaces was implemented to overcome the lack of BIM libraries for historical constructions. Modeling was carried out at a metric scale 1:50 by assembling BIM objects following the logic of construction of the bridge. A semi-Automated strategy that incorporates point clouds was developed to use information gathered by other data sources, such as historical analysis, destructive and non-destructive testing, and existing documentation. The monitoring project aimed at revealing vertical movements during loading tests. The measurement phase was carried out with a high-precision geometric leveling network made up of 47 benchmarks, resulting in redundant measurements adjusted via least squares techniques. The combined use of (i) photogrammetry and laser scanning for the 3D modeling project and (ii) geometric leveling for the analysis of vertical displacements allowed one to capture the metric information needed not only for specialists in the field of surveying, monitoring and modeling, but also for the other specialists (architects, engineers, etc.) involved in the project.

Integrated modeling and monitoring of the medieval bridge azzone visconti

BARAZZETTI, LUIGI;BANFI, FABRIZIO;BRUMANA, RAFFAELLA;PREVITALI, MATTIA;RONCORONI, FABIO
2016-01-01

Abstract

This paper presents the modeling and monitoring work related to the mediaeval bridge Azzone Visconti in Lecco (the "old bridge"). Metric information was acquired with different instruments and techniques for an integrated digital documentation that combines observations with variable precision: ±0.1 mm for the analysis of small vertical movements and subcentimeter precision for the geometrical reconstruction. The complexity of the bridge, with irregular constructive elements and different materials, required a detailed geometrical survey with laser scanning and photogrammetric techniques for the generation of plans and sections along with an accurate 3D parametric representation based on Building Information Modeling. A strategy based on NURBS curves and surfaces was implemented to overcome the lack of BIM libraries for historical constructions. Modeling was carried out at a metric scale 1:50 by assembling BIM objects following the logic of construction of the bridge. A semi-Automated strategy that incorporates point clouds was developed to use information gathered by other data sources, such as historical analysis, destructive and non-destructive testing, and existing documentation. The monitoring project aimed at revealing vertical movements during loading tests. The measurement phase was carried out with a high-precision geometric leveling network made up of 47 benchmarks, resulting in redundant measurements adjusted via least squares techniques. The combined use of (i) photogrammetry and laser scanning for the 3D modeling project and (ii) geometric leveling for the analysis of vertical displacements allowed one to capture the metric information needed not only for specialists in the field of surveying, monitoring and modeling, but also for the other specialists (architects, engineers, etc.) involved in the project.
2016
8th European Workshop on Structural Health Monitoring, EWSHM 2016
9781510827936
9781510827936
BIM; Bridge; Modeling; Monitoring; Surveying; Health Information Management; Computer Science Applications1707 Computer Vision and Pattern Recognition
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1006437
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