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Structure damage diagnosis of bleacher based on DSKNet model

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Abstract

Bleacher usually carries a large number of people, and their safety and stability are critical. Structure damage diagnosis of bleacher can find problems and repair them in time to ensure the safety of personnel. Based on Densely Connected Convolutional Networks (DenseNet) and Selective Kernel Networks (SKNet) models with excellent performance in image recognition, this paper proposes a new DSKNet model for structure damage diagnosis of bleacher. Using the bleacher simulator of Qatar University as the experimental object, the proposed DSKNet model is used to study the damage location and type diagnosis. In addition, the diagnosis results are compared with DenseNet, SKNet, 1DCNN (One Dimensional Convolution Neural Networks), and SVM (support vector machine) models under the same experimental conditions. In order to verify the anti − noise ability of the proposed model in this article, experiments are carried out between the DSKNet model and the above four models under different signal-to-noise ratios. The experimental results show that the DSKNet model can accurately judge the location of the damage. In the damage type experiment, the accuracy of the testing dataset can reach 100% when the model training epoch reaches 30. Under a normal and strong noise environment, the diagnosis performance of the DSKNet model is superior to DenseNet, SKNet, 1DCNN and SVM, which can accurately diagnose the structure damage of bleacher.

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References

  1. Yue N, Khodaei ZS, Aliabadi MH (2021) Damage detection in large composite stiffened panels based on a novel SHM building block philosophy. Smart Mater Struct 30(4):045004

    Article  Google Scholar 

  2. Feng D, Feng MQ (2018) Computer vision for SHM of civil infrastructure: from dynamic response measurement to damage detection–a review. Eng Struct 156:105–117

    Article  Google Scholar 

  3. Song G, Wang C, Wang B (2017) Structural health monitoring (SHM) of civil structures. Appl Sci 7(8):789

    Article  Google Scholar 

  4. Graybeal BA, Phares BM, Rolander DD et al (2002) Visual inspection of highway bridges. J Nondestr Eval 21(3):67–83

    Article  Google Scholar 

  5. Estes AC, Dan MF, Foltz SD (2004) Updating reliability of steel miter gates on locks and dams using visual inspection results. Eng Struct 26(3):319–333

    Article  Google Scholar 

  6. Song YY, Ying LU (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatr 27(2):130

    Google Scholar 

  7. Priyam A, Abhijeeta GR, Rathee A et al (2013) Comparative analysis of decision tree classification algorithms. Int J Curr Eng Technol 3(2):334–337

    Google Scholar 

  8. Webb GI, Keogh E, Miikkulainen R (2010) Naïve Bayes. Encycl Mach Learn 15(1):713–714

    Google Scholar 

  9. Wickramasinghe I, Kalutarage H (2021) Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation. Soft Comput 25(3):2277–2293

    Article  Google Scholar 

  10. Blanquero R, Carrizosa E, Ramírez−Cobo P et al (2021) Variable selection for Naïve Bayes classification. Comput Oper Res 135:105456

    Article  Google Scholar 

  11. Tanveer M, Rajani T, Rastogi R et al (2022) Comprehensive review on twin support vector machines. Ann Op Res. https://doi.org/10.48550/arXiv.2105.00336

    Article  Google Scholar 

  12. Kurani A, Doshi P, Vakharia A et al (2023) A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Ann Data Sci 10(1):183–208

    Article  Google Scholar 

  13. Guo Y, Zhang Z, Tang F (2021) Feature selection with kernelized multi−class support vector machine. Pattern Recogn 117:107988

    Article  Google Scholar 

  14. Bansal M, Goyal A, Choudhary A (2022) A comparative analysis of K−nearest neighbor, genetic, support vector machine, decision tree, and long short−term memory algorithms in machine learning. Decis Anal J 3:100071

    Article  Google Scholar 

  15. Cunningham P, Delany SJ (2021) k−Nearest neighbour classifiers−a tutorial. ACM Comput Surv (CSUR) 54(6):1–25

    Article  Google Scholar 

  16. Dann E, Henderson NC, Teichmann SA et al (2022) Differential abundance testing on single−cell data using k−nearest neighbor graphs. Nat Biotechnol 40(2):245–253

    Article  Google Scholar 

  17. Merainani B, Rahmoune C, Benazzouz D et al (2018) A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform, singular value decomposition, and SOM neural network. J Vib Control 24(12):2512–2531

    Article  MathSciNet  Google Scholar 

  18. Xing Z, Qu J, Chai Y et al (2017) Gear fault diagnosis under variable conditions with intrinsic time−scale decomposition−singular value decomposition and support vector machine. J Mech Sci Technol 31:545–553

    Article  Google Scholar 

  19. Bakator M, Radosav D (2018) Deep learning and medical diagnosis: a review of literature. Multimodal Technol Interact 2(3):47

    Article  Google Scholar 

  20. Bhatt C, Kumar I, Vijayakumar V et al (2021) The state of the art of deep learning models in medical science and their challenges. Multimedia Syst 27(4):599–613

    Article  Google Scholar 

  21. Suzuki K (2017) Overview of deep learning in medical imaging. Radiol Phys Technol 10(3):257–273

    Article  Google Scholar 

  22. Chen L, Li Y, Huang C et al (2022) Milestones in autonomous driving and intelligent vehicles: survey of surveys. IEEE Trans Intell Veh 8(2):1046–1056

    Article  Google Scholar 

  23. Zablocki É, Ben−Younes H, Pérez P, et al (2022) Explainability of deep vision−based autonomous driving systems: review and challenges. Int J Comput Vision 130(10):2425–2452

    Article  Google Scholar 

  24. Khatab E, Onsy A, Varley M et al (2021) Vulnerable objects detection for autonomous driving: a review. Integration 78:36–48

    Article  Google Scholar 

  25. Li Y. Research and application of deep learning in image recognition. (2022) In: IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, 994−999

  26. Li C, Li X, Chen M, et al. Deep Learning and Image Recognition. (2023) In: IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2023: 557−562

  27. Xiong J, Yu D, Liu S et al (2021) A review of plant phenotypic image recognition technology based on deep learning. Electronics 10(1):81

    Article  Google Scholar 

  28. Zheng X, Zheng S, Kong Y et al (2021) Recent advances in surface defect inspection of industrial products using deep learning techniques. Int J Adv Manuf Technol 113:35–58

    Article  Google Scholar 

  29. Saberironaghi A, Ren J, El−Gindy M. (2023) Defect detection methods for industrial products using deep learning techniques: a review. Algorithms 16(2):95

    Article  Google Scholar 

  30. Otter DW, Medina JR, Kalita JK (2020) A survey of the usages of deep learning for natural language processing. IEEE Trans Neural Netw Learn Syst 32(2):604–624

    Article  MathSciNet  Google Scholar 

  31. Wu S, Roberts K, Datta S et al (2020) Deep learning in clinical natural language processing: a methodical review. J Am Med Inform Assoc 27(3):457–470

    Article  Google Scholar 

  32. Lauriola I, Lavelli A, Aiolli F (2022) An introduction to deep learning in natural language processing: models, techniques, and tools. Neurocomputing 470:443–456

    Article  Google Scholar 

  33. Liu X, Zhou Q, Zhao J et al (2019) Fault diagnosis of rotating machinery under noisy environment conditions based on a 1−D convolutional autoencoder and 1−D convolutional neural network. Sensors 19(4):972

    Article  Google Scholar 

  34. He J, Yang S, Gan C (2017) Unsupervised fault diagnosis of a gear transmission chain using a deep belief network. Sensors 17(7):1564

    Article  Google Scholar 

  35. Islam MMM, Kim JM (2019) Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network. Comput Ind 106:142–153

    Article  Google Scholar 

  36. Broer A, Galanopoulos G, Benedictus R et al (2022) Fusion−based damage diagnostics for stiffened composite panels. Struct Health Monit 21(2):613–639

    Article  Google Scholar 

  37. Lee H, Lim HJ, Skinner T et al (2022) Automated fatigue damage detection and classification technique for composite structures using Lamb waves and deep autoencoder. Mech Syst Signal Process 163:108148

    Article  Google Scholar 

  38. Yu Y, Li J, Li J et al (2023) Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi−sensor fusion. Develop Built Environ 14:100128

    Article  Google Scholar 

  39. Ai D, Cheng J (2023) A deep learning approach for electromechanical impedance based concrete structural damage quantification using two−dimensional convolutional neural network. Mech Syst Signal Process 183:109634

    Article  Google Scholar 

  40. Eltouny KA, Liang X (2023) Large-scale structural health monitoring using composite recurrent neural networks and grid environments. Comput-Aided Civil Infrastr Eng 38(3):271–287

    Article  Google Scholar 

  41. Huang G, Liu Z, VanDer, Maaten L. (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4700−4708

  42. Li X, Wang W, Hu X. (2019) Selective kernel networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 510−519

  43. He K, Zhang X, Ren S, Sun J. (2016) Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770−778

  44. Abdeljaber A, Younis O, Avci N, Kiranyaz S (2017) Real−time vibration−based structural damage detection using one−dimensional convolutional neural networks. J Sound Vib 388:154–170

    Article  Google Scholar 

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Funding

This work was supported by the Nature Science Foundation of Hebei Province grant no. E2020402060, and Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province (Hebei University of Engineering) under Grant 202204 and 202206.

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Authors and Affiliations

  1. School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan, 056038, China

    Chaozhi Cai, Xiaoyu Guo, Yingfang Xue & Jianhua Ren

Authors
  1. Chaozhi Cai
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  2. Xiaoyu Guo
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  3. Yingfang Xue
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  4. Jianhua Ren
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Contributions

CC provided Funding acquisition, Methodology, Project administration, Formal analysis, and Writing—review and editing. XG provided Formal analysis, Software, Validation and Writing—original draft. YX.provided Methodology, Formal analysis, Investigation, Supervision and Writing—review and editing. JR provided Formal analysis, Investigation and Writing—review and editing.

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Correspondence to Chaozhi Cai.

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Cai, C., Guo, X., Xue, Y. et al. Structure damage diagnosis of bleacher based on DSKNet model. J Supercomput 80, 10197–10222 (2024). https://doi.org/10.1007/s11227-023-05834-8

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