Characterization of Cracks in Historical Buildings Using Image-Processing Techniques

Abstract

Cracks are structural pathologies that affect the structural integrity of historical buildings. The methodologies commonly used to detect cracks are based on visual inspections or in intrusive techniques that involve removing external wall layers. The main objective of this study is to develop and validate a semi-automatic and non-destructive tool that helps the user to analyze the position and growth of the cracks in masonry constructions based on a photogrammetry analysis. The developed tool uses image processing to plot a curve of the crack area, and, in case needed, its evolution over time. The tool was validated in laboratory using earthen samples that were subjected to uniaxial compression tests. The research also provides the results of the tool used in a case study of a 16th Century stone masonry church located in the main square of Cusco; Southern Peru. This case study validates the qualitative metrics of the present work, and indicates that the tool provided accurate results when compared to the ground truth, which could be helpful in future research studies in order to automatize crack monitoring.

Full document

References

[1] R. Segre, America Latina en su arquitectura, Ciudad de Mexico, (1983).

[2] M. Blondet, M. Serrano, A. Rubiños y E. Mattson, La Experiencia de Capacitación de una Comunidad Andina en Construcción Sismorresistente con Adobe» de Seminario Iberoamericano de Arquitectura y Construccion con Tierra, Cuenca, 2015.

[3] D. Inaudi, A. Rufenacht, B. von Arx, H. P. Noher, S. Vurpillot y B. Glisic, «Monitoring of a concrete arch bridge during construction,» Smart Structures and Materials, 2002

[4] X. W. Ye, C. Z. Dong y T. Liu, «A review of Machine Vision-Based Structural Health Monitoring: Methogologies and Applications,» Journal of Sensors, 2016.

[5] T. Yamaguchi y S. Hashimoto, «Fast crack detection method for large-size concrete surface images using percolation-based image processing,» Machine Vision and Applications, pp. 797-809, 2010.

[6] Z. Chen y T. C. Hutchinson, «Image-Based Framework for Concrete Surface Crack Monitoring and Quantification,» de Advances in Civil Engineering, Hindawi Publishing Corporation, 2010.

[7] J. Valenca, D. Dias-da-Costa, E. Julio, H. Araujo y H. Costa, «Automatic crack monitoring using photogrammetry and image processing,» de Measurement, 2013, pp. 433-441.

[8] W. Benning, J. Lange, R. Schwermann, C. Effkemann y S. Gortz, «MONITORING CRACK ORIGIN AND EVOLUTION AT CONCRETE ELEMENTS USING PHOTOGRAMMETRY,» de TS COMM V: Metrology and Industrial Applications, 2004.

[9] U. Hampel y H.-G. Maas, «Cascaded image analysis for dynamic crack detection in material testing,» ISPRS Journal of Photogrammetry and Remote Sensing, vol. 64, pp. 345- 350, 2009.

[10] S. Nishiyama, N. Minakata, T. Kikuchi y T. Yano, «Improved digital photogrammetry technique for crack monitoring,» de Advanced Engineering Informatics, 2015.

[11] G. Medioni, C.-K. Tang y M.-S. Lee, «Tensor Voting: Theory and Applications».

[12] The Mathworks, «MATLAB, Image Processing Toolbox and Vision Computer Toolbox,» Natick, 2019.

[13] A. International, Standard Test Method for Compressive Strength of Hydraulic Cement Mortars (Using 2-in. or

[14] S. Y. Alvarez Ordoñez, «COMPARACIÓN DE LAS PROPIEDADES MECÁNICAS DE UNIDADES Y PRISMAS DE BLOQUES DE TIERRA COMPRIMIDA ESTABILIZADA CON CEMENTO Y GEOPOLÍMERO DE PUZOLANA,» PUCP, Lima, 2018.

[15] L. E. Yamin Lacouture, C. Philips Bernal, J. C. Reyes Ortiz y D. Ruiz Valencia, «Estudios de vulnerabilidad sismica, rehabilitacion y refuerzo de casas en adobe y tapia pisada,» de APUNTES, 2007, pp. 286-303.

[16] G. K. Velarde Abugattas, «ANÁLISIS DE VULNERABILIDAD SÍSMICA DE VIVIENDAS DE DOS PISOS DE ADOBE EXISTENTE EN LIMA,» Lima, 2014.