A novel study for the estimation of crack propagation in concrete using machine learning algorithms

Abstract In this study, the crack pattern and propagation in a random concrete surface has been examined using machine learning algorithm called voronoi diagrams. A random photo of a concrete crack located on the surface is taken from a common source and the crack dimensions and directions have been measured. After then, the crack has been divided into 12 parts to evaluate the machine learning algorithm’s capability for estimating the crack pattern including its direction. Consequently, it has been shown that this novel technique is precise, quick, cheap and useful for monitoring and estimating crack propagation on concrete surfaces. Besides, it has great potential for not only cement and concrete industries and also for many different industries in the means of automation, sustainability, safety, cost and time savings for observing and estimating crack propagations or other properties of materials.

[1]  Anders Heyden,et al.  Image analysis for monitoring of crack growth in hydropower concrete structures , 2009 .

[2]  Eduardo Júlio,et al.  Characterisation of concrete cracking during laboratorial tests using image processing , 2012 .

[3]  I. Topcu,et al.  Effect of Non-Ground-Granulated Blast-Furnace Slag as Fine Aggregate on Shrinkage Cracking of Mortars , 2010 .

[4]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

[5]  Chung-Ming Yang,et al.  Thin crack observation in a reinforced concrete bridge pier test using image processing and analysis , 2015, Adv. Eng. Softw..

[6]  Turhan Bilir,et al.  Prediction of restrained shrinkage crack widths of slag mortar composites by Takagi and Sugeno ANFIS models , 2016, Neural Computing and Applications.

[7]  Guomin Zhang,et al.  A method of detecting the cracks of concrete undergo high-temperature , 2018 .

[8]  Savaş Erdem,et al.  Self-sensing damage assessment and image-based surface crack quantification of carbon nanofibre reinforced concrete , 2017 .

[9]  Kefei Li,et al.  Geometry of crack network and its impact on transport properties of concrete , 2012 .

[10]  Na Wang,et al.  An experimental study on crack propagation at rock-concrete interface using digital image correlation technique , 2017 .

[11]  Emad L. Labib,et al.  Use of digital image correlation technique in full-scale testing of prestressed concrete structures , 2014 .

[12]  Satoshi Nishiyama,et al.  Improved digital photogrammetry technique for crack monitoring , 2015, Adv. Eng. Informatics.

[13]  Gabriel Sas,et al.  Using digital image correlation to evaluate fatigue behavior of strengthened reinforced concrete beams , 2015 .

[14]  I. Topcu,et al.  Experimental investigation of drying shrinkage cracking of composite mortars incorporating crushed tile fine aggregate , 2010 .

[15]  Alida Mazzoli,et al.  Evaluation of the early-age-shrinkage of Fiber Reinforced Concrete (FRC) using image analysis methods , 2015 .

[16]  I. Löfgren,et al.  Characterisation of bending cracks in R/FRC using image analysis , 2016 .

[17]  François Hild,et al.  Comparison between experimental and numerical results of mixed-mode crack propagation in concrete: Influence of boundary conditions choice , 2017 .

[18]  I. Topcu,et al.  Properties of mortars with fly ash as fine aggregate , 2015 .

[19]  Jean Marc Kanema,et al.  Shrinkage of earth concrete amended with recycled aggregates and superplasticizer: Impact on mechanical properties and cracks , 2016 .

[20]  Fan Xi,et al.  Detection crack in image using Otsu method and multiple filtering in image processing techniques , 2016 .

[21]  Sung-Han Sim,et al.  Comparative analysis of image binarization methods for crack identification in concrete structures , 2017 .

[22]  Abdelkarim Aït-Mokhtar,et al.  Study of cracking due to drying in coating mortars by digital image correlation , 2012 .

[23]  Xiaobin Hu,et al.  A combined-alpha-shape-implicit-surface approach to generate 3D random concrete mesostructures via digital image processing, spectral representation, and point cloud , 2017 .

[24]  Sumathi Poobal,et al.  Crack detection using image processing: A critical review and analysis , 2017, Alexandria Engineering Journal.

[25]  Baoju Liu,et al.  Image analysis for detection of bugholes on concrete surface , 2017 .

[26]  R.N.F. Carmo,et al.  Assessing steel strains on reinforced concrete members from surface cracking patterns , 2015 .

[27]  B. Goszczyńska.  Analysis of the process of crack initiation and evolution in concrete with acoustic emission testing , 2014 .

[28]  N. De Belie,et al.  Monitoring crack movement in polymer-based self-healing concrete through digital image correlation, acoustic emission analysis and SEM in-situ loading , 2017 .

[29]  I. Topcu,et al.  Effect of Bottom Ash as Fine Aggregate on Shrinkage Cracking of Mortars , 2010 .

[30]  Pedro Arias-Sánchez,et al.  Assessment of cracks on concrete bridges using image processing supported by laser scanning survey , 2017 .

[31]  Weiliang Jin,et al.  Crack shape and rust distribution in corrosion-induced cracking concrete , 2012 .

[32]  Sofiane Amziane,et al.  Flexural cracking behavior of normal strength, high strength and high strength fiber concrete beams, using Digital Image Correlation technique , 2016 .

[33]  Luigi Barazzetti,et al.  Crack measurement: Development, testing and applications of an automatic image-based algorithm , 2009 .

[34]  L. Sneed,et al.  A Study of the Compressive Behavior of Concrete Columns Confined with SRP Jackets using Digital Image Analysis , 2017 .

[35]  S. Pyo,et al.  Crack propagation speed in ultra high performance concrete (UHPC) , 2016 .

[36]  M. Hattab,et al.  Drying, cracks and shrinkage evolution of a natural silt intended for a new earth building material. Impact of reinforcement , 2015 .

[37]  J. Lees,et al.  Experimental investigation of crack propagation and crack branching in lightly reinforced concrete beams using digital image correlation , 2017 .