Terrestrial laser scanning for the comprehensive structural health assessment of the Baptistery di San Giovanni in Florence, Italy: an integrative methodology for repeatable data acquisition, visualization and analysis

Abstract

The ravages of time, natural and man-made disasters, pollution, fatigue, overexposure, mismanagement, and the unintended consequences of efforts to preserve our cultural patrimony, have all taken a major toll on historical structures. Structural health assessment is the first pivotal step towards creating a strategy for long-term life-cycle management. Historical structures provide an abundance of unique challenges that when combined serve as a great qualifying test for the study of as-built structures. This paper explores the diagnostic value of terrestrial laser scanning for the structural health assessment of the Baptistery di San Giovanni in Florence, Italy and proposes an integrative methodology for repeatable data acquisition, processing, visualization and analysis. The presented study proves that even under challenging circumstances, efficient documentation of entire structures is possible. The case study at the Baptistery demonstrates that even when objectives are not formed prior to the survey, comprehensive data sets of high quality and reliability will enable meaningful structural health assessments. With a reliable comprehensive baseline model in place, it can be annotated, qualitatively analyzed and recurring surveys can be conducted to track changes and damages throughout time.

Keywords:

• Baptistery di San Giovanni
• three-dimensional models
• 3D model
• three-dimensional analysis
• structural analysis
• damage assessment
• remote sensing

Acknowledgements

The authors would like to thank Maurizio Seracini, the Opera di Santa Maria del Fiore and President Franco Lucchesi for the opportunity to study the Baptistery di San Giovanni. Opinions, findings, and conclusions from this study are those of the authors and do not necessarily reflect the opinions of the research sponsors.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Science Foundation under award #DGE-0966375, ‘Training, Research and Education in Engineering for Cultural Heritage Diagnostics,’ and award #CNS-1338192, ‘MRI: Development of Advanced Visualization Instrumentation for the Collaborative Exploration of Big Data.’ Additional support was provided by the Kinsella Fund, the Qualcomm Institute at UC San Diego, the Friends of CISA3, and the World Cultural Heritage Society.