Abstract
Inspection and maintenance of civil infrastructures require structural assessment, usually performed based on the monitoring of critical sections. For concrete structures, the identification and characterization of the crack patterns is an important task to a rigorous evaluation of the structural performance. In the case of concrete dams, the timely detection and correction of structural problems can avoid major accidents. Despite the significance of crack monitoring and the recent innovations using image processing, the inspection of dams is usually simply based on visual inspections. This results in sketching crack patterns and also includes hand-held measurements, using crack width rulers and measuring tape. Thus, the development of automatic methods based on image processing to assess cracks in concrete dams has significant advantages. In this scope, most of the methods were applied in the laboratorial environment, and a gap to scale-up them to onsite assessment is clearly identified. In this paper, a method named MCrack-Dam, resulting from the scale-up of the method MCrack, previously developed and validated in controlled laboratorial conditions, is presented. The method is based on image processing and designed to automatically monitoring cracks in concrete dams. The MCrack-Dam relies on a predefined systematic acquisition of images: pre-processing those images for ortho-rectification; processing of the latter to identify and model cracks; and post-processing procedure to characterize the crack key parameters. The method was applied to a predefined region of the Itaipu Dam, at Brazil–Paraguay border. The results validate the ability of MCrack-Dam for performing a detailed characterization of cracks in concrete dams, not comparable to the traditional methods currently used. In addition, MCrack-Dam successfully works on surfaces with distinct features (‘noise’ for crack detection) such as drippings, sketched drawings, and smooth and rough textures, unlike other image processing methods when applied on ‘noise’ surfaces. Finally, the most relevant conclusions, and guidelines for the optimization of the exhaustive survey of the entire surface of the dam, are presented.
References
Qu Z, Guo Y, Ju FR, Liu L, Lin LD (2016) The algorithm of accelerated cracks detection and extracting skeleton by direction chain code in concrete surface image. Imaging Sci J 64(3):119–130
Bukenya P, Moyo P, Beushausen H, Oosthuizen C (2014) Health monitoring of concrete dams: a literature review. J Civil Struct Health Monit 4(4):235–244
González-Aguilera D, Gómez-Lahoz J, Sánchez J (2008) A new approach for structural monitoring of large dams with a three-dimensional laser scanner. Sensors 8(9):5866–5883
Mathur RK, Sehra RS, Gupta SL (2017) Instrumentation of concrete dams. Int J Eng Appl Sci 4(3):50–53
Valença J, Dias-da-Costa D, Júlio E (2012) Characterisation of concrete cracking during laboratorial tests using image processing. Constr Build Mater 28(1):607–615
Caetano de Souza AC (2008) Assessment and statistics of Brazilian hydroelectric power plants: dam areas versus installed and firm power. Renew Sustain Energy Rev 12(7):1843–1863
Rivarolo M, Bogarin J, Magistri L, Massardo AF (2012) Time-dependent optimization of a large size hydrogen generation plant using “spilled” water at Itaipu 14 GW hydraulic plant. Int J Hydrogen Energy 37(6):5434–5443
Itaipu-Binacional (2015) Site of Itaipu Binacional, http://www.itaipu.gov.br
PTI-Ceasb (2015) Site of Parque Tecnológico de Itaipu (PTI), http://www.pti.org.br/
Valença J, Dias-da-Costa D, Júlio E, Araújo H, Costa H (2013) Automatic crack monitoring using photogrammetry and image processing. Measurement 46(1):433–441
Valença J, Gonçalves L, Júlio E (2013) Damage assessment on concrete surfaces using multi-spectral image analysis. Constr Build Mater 40:971–981
Valença J, Dias-da-Costa D, Gonçalves L, Júlio E, Araújo H (2014) Automatic concrete health monitoring: assessment and monitoring of concrete surfaces. Struct Infrastructure Eng 10(12):1547–1554
Dias-da-Costa D, Valença J, Júlio E, Araújo H (2017) Crack propagation monitoring using an image deformation approach. Struct Control Health Monit 24(10):1–14
Valença J, Carmo RNF (2017) Method for assessing beam column joints in RC structures using photogrammetric computer vision. Struct Control Health Monit 24(11):1–18
Valença J, Puente I, Júlio E, González-Jorge H, Arias-Sánchez P (2017) Assessment of cracks on concrete bridges using image processing supported by laser scanning survey. Constr Build Mater 146:668–678
Valença J, Júlio E, Araújo H (2012) Application of photogrammetry to structural assessment. Exp Tech 36(5):71–81
Dias-da-Costa D, Valença J, Júlio E (2011) Laboratorial test monitoring applying photogrammetric post-processing procedures to surface displacements. Measurement 44(3):527–538
Carmo RNF, Valença J, Silva D, Dias-da-Costa D (2015) Assessing steel strains on reinforced concrete members from surface cracking patterns. Constr Build Mater 98:265–275
Carmo RNF, Costa H, Gomes G, Valença J (2017) Experimental evaluation of lightweight aggregate concrete beam–column joints with different strengths and reinforcement ratios. Struct Concrete 18(6):950–961
Dias-da-Costa D, Carmo RNF, Graça-e-Costa R, Valença J, Alfaiate J (2014) Longitudinal reinforcement ratio in lightweight aggregate concrete beams. Eng Struct 81:219–229
Dias-da-Costa D, Valença J, do Carmo RNF (2014) Curvature assessment of reinforced concrete beams using photogrammetric techniques. Mater Struct 47(10):1745–1760
Simões T, Octávio C, Valença J, Costa H, Dias-da-Costa D, Júlio E (2017) Influence of concrete strength and steel fibre geometry on the fibre/matrix interface. Compos Part B Eng 122:156–164
Winder S, Hua G, Brown M (2009) Picking the best DAISY. In: 2009 IEEE conference on computer vision and pattern recognition, pp 178–185
Valença J (2014) Systems based on photogrammetry to evaluation of built heritage: tentative guidelines and control parameters. ISPRS technical commission V symposium 2014. Int Arch Photogramm. Remote Sens Spatial Inf Sci - ISPRS Arch 40(5):607–613
Acknowledgements
The authors would like to acknowledge the Portuguese Foundation for Science and Technology (FCT) by funding the project PTDC/ECM-EST/6830/2014, entitled ‘Crack Monitoring in concrete bridges through multi-spectral image processing acquired by unmanned aerial vehicles’. Jónatas Valença also acknowledges the financial support of FCT through the post-doctoral grant SFRH/BPD/102790/2014.
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Valença, J., Júlio, E. MCrack-Dam: the scale-up of a method to assess cracks on concrete dams by image processing. The case study of Itaipu Dam, at the Brazil–Paraguay border. J Civil Struct Health Monit 8, 857–866 (2018). https://doi.org/10.1007/s13349-018-0309-0
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DOI: https://doi.org/10.1007/s13349-018-0309-0