Crack identification in concrete, using digital image correlation and neural network

Abstract

In engineering applications, concrete crack monitoring is very important. Traditional methods are of low efficiency, low accuracy, have poor timeliness, and are applicable in only a limited number of scenarios. Therefore, more comprehensive detection of concrete damage under different scenarios is of high value for practical engineering applications. Digital image correlation (DIC) technology can provide a large amount of experimental data, and neural network (NN) can process very rich data. Therefore, NN, including convolutional neural networks (CNN) and back propagation neural networks (BP), can be combined with DIC technology to analyze experimental data of three-point bending of plain concrete and four-point bending of reinforced concrete. In addition, strain parameters can be used for training, and displacement parameters can be added for comprehensive consideration. The data obtained by DIC technology are grouped for training, and the recognition results of NN show that the combination of strain and displacement parameters, i.e., the response of specimen surface and whole body, can make results more objective and comprehensive. The identification results obtained by CNN and BP show that these technologies can accurately identify cracks. The identification results for reinforced concrete specimens are less affected by noise than those of plain concrete specimens. CNN is more convenient because it can identify some features directly from images, recognizing the cracks formed by macro development. BP can issue early warning of the microscopic cracks, but it requires a large amount of data and computation. It can be seen that CNN is more intuitive and efficient in image processing, and is suitable when low accuracy is adequate, while BP is suitable for occasions with greater accuracy requirements. The two tools have advantages in different situations, and together they can play an important role in engineering monitoring.

Graphical abstract

Keywords

digital image correlation / convolutional neural network / back propagation neural neural network / damage detection / concrete

References

[1] ZhangJSongZBingHXieHLiP. Research progress on fatigue of highway concrete bridges under vehicle loading. China Civil Engineering Journal, 2023, 55(12): 65−79 (in Chinese)

[2] Liu X, Sun Q H, Song W, Bao Y H. Structural behavior of reinforced concrete tunnel linings with synthetic fibers addition. Tunnelling and Underground Space Technology, 2023, 131: 104771

[3] Mishra M, Lourenco P B, Ramana G V. Structural health monitoring of civil engineering structures by using the internet of things: A review. Journal of Building Engineering, 2022, 48: 103954

[4] Koike K, Yamaji T, Nishida T, Yonamine K, Adachi A, Nakagawa K. Influence of transportation and pumping on the properties of concrete with large amount of copper slag fine aggregate in actual construction of port and harbor structures. Journal of Material Cycles and Waste Management, 2022, 24(4): 1368–1377

[5] An Q, Chen X J, Wang H J, Yang H M, Yang Y J, Huang W, Wang L. Segmentation of concrete cracks by using fractal dimension and UHK-Net. Fractal and Fractional, 2022, 6(2): 95

[6] Chauhan A, Sharma U K. Identifying factors influencing corrosion rate in reinforced concrete under simulated natural climate. Current Science, 2022, 123(11): 1327–1333

[7] Zhao B N, Dong L, Fu J J, Yang L Q, Xu W X. Experimental study on micro-damage identification in reinforced concrete beam with wavelet packet and DIC method. Construction & Building Materials, 2019, 210: 338–346

[8] HuangY YZhengHZhouY H. Study of concrete block’s thermal stress considering tensile and pressure different creep. Journal of Wuhan University of Technology, 2011, 33(3): 87−92 (in Chinese)

[9] Liang S Y. Review on concrete cracking and maintenance. Sichuan Cement, 2016, 38(7): 262

[10] TongZ NGaoY. Cause and influence of mass concrete crack. International Conference on Chemical, Material and Food Engineering. 2015, 22:497–499

[11] Zhou S L, Zhao Q, Guo J Q. Analysis and control technology of hydraulic concrete crack. Water Resources Planning and Design, 2021, (4): 110–114

[12] Luo G H, Pan J W, Wang J T. Study on the influence factors of Rayleigh wave method for detecting the crack depth of concrete Surface. Water Resources and Hydropower Technology, 2021, 52(9): 165–171

[13] LiuX Z. Laser ultrasonic detection method for crack depth of concrete. Journal of Central South University (Science and Technology), 2021, 52(3): 839−847 (in Chinese)

[14] WeiS HLiuY FLiuJ H. Research on surface crack detection of concrete structure based on UAV and digital image method. Special Structures, 2020, 37(5):107–111

[15] Li Y W, Xiang H, Peng W. Research and application of high dam concrete crack detection technology based on UAV and underwater robot. Building Technology Development, 2021, 48(3): 52–54

[16] Cheng L, Tian G Y. Surface crack detection for carbon fiber reinforced plastic (CFRP) materials using pulsed eddy the current thermography. IEEE Sensors Journal, 2011, 11(12): 3261–3268

[17] Zhang L X, Shen J K, Zhu B J. A review of the research and application of deep learning-based computer vision in structural damage detection. Earthquake Engineering and Engineering Vibration, 2022, 21(1): 1–21

[18] Huang H W, Li Q T, Zhang D M. Deep learning based image recognition for crack and leakage defects of metro shield tunnel. Tunnelling and Underground Space Technology, 2018, 77: 166–176

[19] Ma X L, Lu J. Research on Road Surface Crack Image Classification Algorithm Based on Grayscale Analysis. Journal of Wuhan University of Technology (Traffic Science and Engineering), 2018, 42(5): 748–752, 756

[20] ChaiX SZhuX YLiJ CXueFXinX S. Tunnel lining crack recognition algorithm based on deep convolutional neural network. Railway Construction, 2018, 58(6): 60–65

[21] Zhang Z D, Jung C. GBDT-MO: Gradient-boosted decision trees for multiple outputs. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(7): 3156–3167

[22] Vieira F, Taveira-Pinto F, Rosa-Santos P. Novel time-efficient approach to calibrate VARANS-VOF models for simulation of wave interaction with porous structures using artificial neural networks. Ocean Engineering, 2021, 235: 103975

[23] Alazzam M B, Hajjej F, AlGhamdi A S, Ayouni S, Rahman M A. Mechanics of materials natural fibers technology on thermal properties of polymer. Advances in Materials Science and Engineering, 2022, 2022: 1–5

[24] Ghasemi A, Amirabadi R, Kamalian U R, Mazyak A R. Numerical modeling investigation of perforated geometry of caisson breakwater under irregular waves by considering porous media. Ocean Engineering, 2023, 269: 113558

[25] Li M Z, Yan R J, Xu L, Soares C G. A general framework of higher-order shear deformation theories with a novel unified plate model for composite laminated and FGM plates. Composite Structures, 2021, 261: 113560

[26] Le-Duc T, Nguyen Q H, Nguyen-Xuan H. Balancing composite motion optimization. Information Sciences, 2020, 520: 250–270

[27] Farzam A, Hassani B. Isogeometric analysis of in-plane functionally graded porous microplates using modified couple stress theory. Aerospace Science and Technology, 2019, 91: 508–524

[28] Dang B L, Nguyen-Xuan H, Wahab M A. An effective approach for VARANS-VOF modelling interactions of wave and perforated breakwater using gradient boosting decision tree algorithm. Ocean Engineering, 2023, 268: 113398

[29] GirshickR. Fast R-CNN. In: Proceedings of the International Conference on Computer Vision. Xi’an: IEEE, 2015, 1440–1448

[30] Tran V T, Nguyen T K, Nguyen-Xuan H, Wahab M A. Vibration and buckling optimization of functionally graded porous microplates using BCMO-ANN algorithm. Thin-walled Structures, 2023, 182: 110267

[31] Kilic H, Yuzgec U, Karakuzu C. Improved antlion optimizer algorithm and its performance on neuro fuzzy inference system. Neural Network World, 2019, 29(4): 235–254

[32] Yuan S C. Review of root-mean-square error calculation methods for large deployable mesh reflectors. International Journal of Aerospace Engineering, 2022, 2022: 5352146

[33] Ho L V, Trinh T T, de Roeck G, Bui-Tien T, Nguyen-Ngoc L, Abdel Wahab M. An efficient stochastic-based coupled model for damage identification in plate structures. Engineering Failure Analysis, 2022, 131: 105866

[34] Guo H W, Zhuang X Y, Chen P W, Alajlan N, Rabczuk T. Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media. Engineering with Computers, 2022, 38(6): 5173–5198

[35] Guo H W, Zhuang X Y, Chen P W, Alajlan N, Rabczuk T. Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis. Engineering with Computers, 2022, 38(6): 5423–5444

[36] HuHPYangY. A combined GLQP and DBN-DRF for face recognition in unconstrained environments. In: Proceedings of the 2nd International Conference on Control, Automation and Artificial Intelligence. Paris: Atlantis Press, 2017, 553–557

[37] Nayoung K, Min K Y, Ko S. Performance improvement method of convolutional neural network using agile activation function. KIPS Transactions on Software and Data Engineering, 2021, 9(7): 213–220

[38] Svozil D, Kvasnicka V, Pospichal J. Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems, 1997, 39(1): 43–62

[39] Guo M G, Gong H. Research on AlexNet improvement and optimization method. Computer Engineering and Application., 2021, 56(20): 124–131

[40] ChanK HImS KKeW. VGGreNet: A Light-Weight VGGNet with Reused Convolutional Set. In: Proceedings of the 13th International Conference on Utility and Cloud Computing (UCC). IEEE, 2020: 434–439

[41] Peng D L, Wang T X. Pruning algorithm based on GoogLeNet model. Control and Decision, 2019, 34(6): 1259–1264

[42] Su Y, Song J X. A detection method based on Bayesian hierarchical network for abnormal interaction. In: Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016). Lima: Atlantis Press, 2016, 60: 333–340

[43] TanMQuocL. Efficientnet: Rethinking model scaling for convolutional neural networks. In: Proceedings of the International Conference on Machine Learning. Long Beach, CA: PMLR, 2019, 6105–6114

[44] Xu J C, Zhang J K, Shen Z Z. Recognition method of internal concrete structure defects based on 1D-CNN. Journal of Intelligent & Fuzzy Systems, 2022, 42(6): 5215–5226

[45] Wang B X, Zhao W G, Gao P, Zhang Y F, Wang Z. Crack damage detection method via multiple visual features and efficient multi-task learning model. Sensors, 2018, 18(6): 1796

[46] Zhang X Q, Akber M Z, Zheng W. Predicting the slump of industrially produced concrete using machine learning: A multiclass classification approach. Journal of Building Engineering, 2022, 58: 104997

RIGHTS & PERMISSIONS

Higher Education Press