Automatic detection and assessment of crack development in ultra-high performance concrete in the spatial and Fourier domains

Jixing CAO , Yao ZHANG , Haijie HE , Weibing PENG , Weigang ZHAO , Zhiguo YAN , Hehua ZHU

Front. Struct. Civ. Eng. ›› 2024, Vol. 18 ›› Issue (3) : 350 -364.

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Front. Struct. Civ. Eng. ›› 2024, Vol. 18 ›› Issue (3) : 350 -364. DOI: 10.1007/s11709-024-1042-x
RESEARCH ARTICLE

Automatic detection and assessment of crack development in ultra-high performance concrete in the spatial and Fourier domains

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Abstract

Automatic detection and assessment of surface cracks are beneficial for understanding the mechanical performance of ultra-high performance concrete (UHPC). This study detects crack evolution using a novel dynamic mode decomposition (DMD) method. In this method, the sparse matrix ‘determined’ from images is used to reconstruct the foreground that contains cracks, and the global threshold method is adopted to extract the crack patterns. The application of the DMD method to the three-point bending test demonstrates the efficiency in inspecting cracks with high accuracy. Accordingly, the geometric features, including the area and its projection in two major directions, are evaluated over time. The relationship between the geometric properties of cracks and load-displacement curves of UHPC is discussed. Due to the irregular shape of cracks in the spatial domain, the cracks are then transformed into the Fourier domain to assess their development. Results indicate that crack patterns in the Fourier domain exhibit a distinct concentration around a central position. Moreover, the power spectral density of cracks exhibits an increasing trend over time. The investigation into crack evolution in both the spatial and Fourier domains contributes significantly to elucidating the mechanical behavior of UHPC.

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Keywords

dynamic mode decomposition / ultra-high performance concrete / crack detection / geometric features / Fourier domain

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Jixing CAO, Yao ZHANG, Haijie HE, Weibing PENG, Weigang ZHAO, Zhiguo YAN, Hehua ZHU. Automatic detection and assessment of crack development in ultra-high performance concrete in the spatial and Fourier domains. Front. Struct. Civ. Eng., 2024, 18(3): 350-364 DOI:10.1007/s11709-024-1042-x

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References

[1]

Chen Q, Zhu Z, Ma R, Jiang Z, Zhang Y, Zhu H. Insight into the mechanical performance of the UHPC repaired cementitious composite system after exposure to high temperatures. Materials, 2021, 14(15): 4095

[2]

Wang X, Yu R, Song Q, Shui Z, Liu Z, Wu S, Hou D. Optimized design of ultra-high performance concrete (UHPC) with a high wet packing density. Cement and Concrete Research, 2019, 126: 105921

[3]

Yan Z, Zhang Y, Woody Ju J, Chen Q, Zhu H. An equivalent elastoplastic damage model based on micromechanics for hybrid fiber-reinforced composites under uniaxial tension. International Journal of Damage Mechanics, 2019, 28(1): 79–117

[4]

Zhang Y, Yan Z, Ju J W, Zhu H, Chen Q. A multi-level micromechanical model for elastic properties of hybrid fiber reinforced concrete. Construction & Building Materials, 2017, 152: 804–817

[5]

Liu T, Wei H, Zhou A, Zou D, Jian H. Multiscale investigation on tensile properties of ultra-high performance concrete with silane coupling agent modified steel fibers. Cement and Concrete Composites, 2020, 111: 103638

[6]

Wu L S, Yu Z H, Zhang C, Bangi T. Design approach, mechanical properties and cost-performance evaluation of ultra-high performance engineered cementitious composite (UHP-ECC): A review. Construction & Building Materials, 2022, 340: 127734

[7]

Moosavi R, Grunwald M, Redmer B. Crack detection in reinforced concrete. Nondestructive Testing and Evaluation International, 2020, 109: 102190

[8]

Chen J, Wu Y, Yang C. Damage assessment of concrete using a non-contact nonlinear wave modulation technique. NDT & E International, 2019, 106: 1–9

[9]

He F, Biolzi L, Carvelli V, Monteiro P J M. Digital imaging monitoring of fracture processes in hybrid steel fiber reinforced concrete. Composite Structures, 2022, 298: 116005

[10]

Liu Y, Yeoh J K W. Automated crack pattern recognition from images for condition assessment of concrete structures. Automation in Construction, 2021, 128: 103765

[11]

Kheradmandi N, Mehranfar V. A critical review and comparative study on image segmentation-based techniques for pavement crack detection. Construction & Building Materials, 2022, 321: 126162

[12]

Smyl D, Pour-Ghaz M, Seppänen A. Detection and reconstruction of complex structural cracking patterns with electrical imaging. NDT & E International, 2018, 99: 123–133

[13]

LiPWangCLiSFengB. Research on crack detection method of airport runway based on twice-threshold segmentation. In: Proceedings of the 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC). Hangzhou: IEEE, 2015: 1716–1720

[14]

Li H, Song D, Liu Y, Li B. Automatic pavement crack detection by multi-scale image fusion. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(6): 2025–2036

[15]

TangJGuY. Automatic crack detection and segmentation using a hybrid algorithm for road distress analysis. In: Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics. Shenzhen: IEEE, 2013, 3026–3030

[16]

QiangSGuoyingLJingqiMHomgmeiZ. An edge-detection method based on adaptive canny algorithm and iterative segmentation threshold. In: Proceedings of the 2nd International Conference on Control Science and Systems Engineering (ICCSSE). Singapore: IEEE, 2016: 64–67

[17]

DixitAWagatsumaH. Investigating the effectiveness of the sobel operator in the MCA-based automatic crack detection. In: Proceedings of the 4th International Conference on Optimization and Applications (ICOA). Mohammedia, Morocco: IEEE, 2018: 1–6

[18]

Kim H, Ahn E, Shin M, Sim S H. Crack and noncrack classification from concrete surface images using machine learning. Structural Health Monitoring, 2019, 18(3): 725–738

[19]

Loverdos D, Sarhosis V. Automatic image-based brick segmentation and crack detection of masonry walls using machine learning. Automation in Construction, 2022, 140: 104389

[20]

Ahmadi A, Khalesi S, Golroo A. An integrated machine learning model for automatic road crack detection and classification in urban areas. International Journal of Pavement Engineering, 2022, 23(10): 3536–3552

[21]

Ali R, Chuah J H, Talip M S A, Mokhtar N, Shoaib M A. Structural crack detection using deep convolutional neural networks. Automation in Construction, 2022, 133: 103989

[22]

Dung C V, Anh L D. Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction, 2019, 99: 52–58

[23]

Bang S, Park S, Kim H, Kim H. Encoder–decoder network for pixel-level road crack detection in black-box images. Computer-Aided Civil and Infrastructure Engineering, 2019, 34(8): 713–727

[24]

Ren Y, Huang J, Hong Z, Lu W, Yin J, Zou L, Shen X. Image-based concrete crack detection in tunnels using deep fully convolutional networks. Construction & Building Materials, 2020, 234: 117367

[25]

Ahn E, Shin M, Popovics J S, Weaver R L. Effectiveness of diffuse ultrasound for evaluation of micro-cracking damage in concrete. Cement and Concrete Research, 2019, 124: 105862

[26]

Cao J, Jiang Z, Gao L, Liu Y, Bao C. Continuous crack detection using the combination of dynamic mode decomposition and connected component-based filtering method. Structures, 2023, 49: 640–654

[27]

Bhowmick S, Nagarajaiah S, Veeraraghavan A. Vision and deep learning-based algorithms to detect and quantify cracks on concrete surfaces from UAV videos. Sensors, 2020, 20(21): 6299

[28]

Barkavi T, Chidambarathanu N. Processing digital image for measurement of crack dimensions in concrete. Civil Engineering Infrastructures Journal, 2019, 52(1): 11–22

[29]

TuJ H. Dynamic mode decomposition: Theory and applications. Dissertation for the Doctoral Degree. Princeton: Princeton University, 2013

[30]

Erichson N B, Donovan C. Randomized low-rank dynamic mode decomposition for motion detection. Computer Vision and Image Understanding, 2016, 146: 40–50

[31]

DongSZhangWWangWKunZ. Action recognition based on dynamic mode decomposition. Journal of Ambient Intelligence and Humanized Computing, 2021, 1–14

[32]

Ngo T T, Nguyen V D, Pham X Q, Hossain M A, Huh E N. Motion saliency detection for surveillance systems using streaming dynamic mode decomposition. Symmetry, 2020, 12(9): 1397

[33]

JainA K. Fundamentals of Digital Image Processing. Englewood: Prentice-Hall, 1989

[34]

KutzJ NBruntonS LBruntonB WProctorJ L. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems. Philadelphia: Society for Industrial and Applied Mathematics, 2016

[35]

Ioannou D, Huda W, Laine A F. Circle recognition through a 2D Hough transform and radius histogramming. Image and Vision Computing, 1999, 17(1): 15–26

[36]

SundararajanD. The Discrete Fourier Transform: Theory, Algorithms and Applications. Singapore: World Scientific, 2001

[37]

CaoJHeHZhangYZhaoWYanZZhuH. Crack detection in ultrahigh-performance concrete using robust principal component analysis and characteristic evaluation in the frequency domain. Structural Health Monitoring, 2023, 23(2): 14759217231178457

[38]

Zhang Y, Zhang S, Jiang X, Zhao W, Wang Y, Zhu P, Yan Z, Zhu H. Uniaxial tensile properties of multi-scale fiber reinforced rubberized concrete after exposure to elevated temperatures. Journal of Cleaner Production, 2023, 389: 136068

[39]

Zhang Y, Zhang S, Zhao W, Jiang X, Chen Y, Hou J, Wang Y, Yan Z, Zhu H. Influence of multi-scale fiber on residual compressive properties of a novel rubberized concrete subjected to elevated temperatures. Journal of Building Engineering, 2023, 65: 105750

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