یک روش هوشمند برای طبقه‏بندی ترک در سازه‏های بتنی بر اساس شبکه‏های عصبی عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی بزق-کنترل، دانشکده فنی و مهندسی، دانشگاه بین المللی امام خمینی (ره)

2 گروه مهندسی برق-کنترل، دانشکده فنی و مهندسی، دانشگاه بین‏‏المللی امام خمینی(ره)، قزوین، ایران،

3 گروه مهندسی برق-کنترل، دانشکده فنی و مهندسی، دانشگاه بین‏‏المللی امام خمینی(ره)، قزوین، ایران

چکیده

شناسایی و بررسی انواع ترک‏ها در سازه‏های بتنی یکی از موضوعات چالش‏برانگیز در حوزه مهندسی به شمار می‏رود. تشخیص چندشاخگی در ترک به دلیل اینکه موجب شناسایی سطوح شدت بالا در سازه‏های بتنی می‏شود، از اهمیت بسزایی برخوردار است. در این مقاله یک معماری جدید بر مبنای شبکه‏های عصبی کانولوشنی برای طبقه‏بندی ترک در سازه‏های بتنی ارائه گردید. معماری پیشنهادی در زمان کمتر و صحت بالاتر نسبت به سایر معماری‏های مرسوم و معتبر در یادگیری عمیق، چندشاخگی در ترک را شناسایی و طبقه‏بندی کرد. در این مقاله ترک‏های موجود در 12000 تصویر سازه‏های بتنی توسط الگوریتم پیشنهادی بررسی شدند که در نتیجه این تصاویر با صحت 99/3 درصد در دسته‏های تصاویر بدون ترک، تصاویر دارای ترک ساده و تصاویر دارای چندشاخگی در ترک طبقه‏بندی شدند. همچنین تحلیل ماتریس درهم‏ریختگی نشان از دقت 99/3 درصد و فراخوانی99/5 درصد داشت که تأییدی بر عملکرد مناسب الگوریتم پیشنهادی بود. آنالیز حساسیت الگوریتم ‏پیشنهادی نیز الزام وجود تناسب میان تعداد داده، تعداد نورون‏های لایه تماماً متصل، زمان اجرا و درصد صحت مورد انتظار با توجه به کابرد مسأله را نشان داد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

An Intelligent Method for Crack Classification in Concrete Structures Based on Deep Neural Networks

نویسندگان [English]

  • Nooshin Bigdeli 1
  • Hamed Jabbari 2
  • Mahdi Shojaei 3
1 Control Eng. Dept., Faculty of Technical and Engineering, Imam Khomeini International University
2 Control Engineering Department, Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran.
3 Control Engineering Department, Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran.
چکیده [English]

Identifying and examining the types of cracks in concrete structures is one of the challenging engineering issues. Detection of crack bifurcation is very important because it detects high-intensity surfaces in concrete structures. In this paper, a new architecture based on convolutional neural networks is presented for crack classification in concrete structures. The proposed architecture detected and classified crack bifurcation in less time and with higher accuracy than other conventional and authentic deep learning architectures. In this paper, the cracks in 12000 images of concrete structures were investigated by the proposed algorithm, which resulted in 99.3% accuracy in categorizing as non-cracked images, images with simple cracks, and bifurcated crack images. Moreover, the analysis of the confusion matrix showed an accuracy of 99.3% and a recall of 99.5%, which confirmed the proper performance of the proposed algorithm. The sensitivity analysis of the proposed algorithm also showed the need for proportionality between the number of data, the number of neurons in the fully connected layer, execution time, and the expected percentage of accuracy according to the application of the problem.

کلیدواژه‌ها [English]

  • Cracks
  • concrete structures
  • Machine learning
  • Deep learning
  • Convolutional Neural Networks
[1] L. Qiu, S. Yuan, C. Boller, An adaptive guided wave-Gaussian mixture model for damage monitoring under time-varying conditions: Validation in a full-scale aircraft fatigue test, Structural health monitoring, 16(5) (2017) 501-517.
[2] P. Liu, H.J. Lim, S. Yang, H. Sohn, C.H. Lee, Y. Yi, D. Kim, J. Jung, I.-h. Bae, Development of a “stick-and-detect” wireless sensor node for fatigue crack detection, Structural Health Monitoring, 16(2) (2017) 153-163.
[3] J. Xu, Z. Fu, Q. Han, G. Lacidogna, A. Carpinteri, Micro-cracking monitoring and fracture evaluation for crumb rubber concrete based on acoustic emission techniques, Structural Health Monitoring, 17(4) (2018) 946-958.
[4] D. Reagan, A. Sabato, C. Niezrecki, Feasibility of using digital image correlation for unmanned aerial vehicle structural health monitoring of bridges, Structural Health Monitoring, 17(5) (2018) 1056-1072.
[5] W.-H. Hu, S. Said, R.G. Rohrmann, Á. Cunha, J. Teng, Continuous dynamic monitoring of a prestressed concrete bridge based on strain, inclination and crack measurements over a 14-year span, Structural Health Monitoring, 17(5) (2018) 1073-1094.
[6] H. Kim, E. Ahn, M. Shin, S.-H. Sim, Crack and noncrack classification from concrete surface images using machine learning, Structural Health Monitoring, 18(3) (2019) 725-738.
[7] J. Valença, D. Dias-da-Costa, E. Júlio, H. Araújo, H. Costa, Automatic crack monitoring using photogrammetry and image processing, Measurement, 46(1) (2013) 433-441.
[8] D. Dias‐da‐Costa, J. Valença, E. Júlio, H. Araújo, Crack propagation monitoring using an image deformation approach, Structural Control and Health Monitoring, 24(10) (2017) e1973.
[9] T.-H. Yi, H.-N. Li, M. Gu, Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge, Measurement, 46(1) (2013) 420-432.
[10] A. Mohan, S. Poobal, Crack detection using image processing: A critical review and analysis, Alexandria Engineering Journal, 57(2) (2018) 787-798.
[11] J.-K. Oh, G. Jang, S. Oh, J.H. Lee, B.-J. Yi, Y.S. Moon, J.S. Lee, Y. Choi, Bridge inspection robot system with machine vision, Automation in Construction, 18(7) (2009) 929-941.
[12] N.-D. Hoang, Q.-L. Nguyen, A novel method for asphalt pavement crack classification based on image processing and machine learning, Engineering with Computers, 35(2) (2019) 487-498
[13] Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT press, 2016.
[14] N. Dwivedi, D.K. Singh, Review of Deep Learning Techniques for Gender Classification in Images, in:  Harmony Search and Nature Inspired Optimization Algorithms, Springer, 2019, pp. 1089-1099.
[15] M. Shariati, N.H. Ramli-Sulong, M.M.A. KH, P. Shafigh, H. Sinaei, Assessing the strength of reinforced concrete structures through Ultrasonic Pulse Velocity and Schmidt Rebound Hammer tests, Scientific Research and Essays, 6(1) (2011) 213-220.
[16] M. Hamidian, A. Shariati, M.A. Khanouki, H. Sinaei, A. Toghroli, K. Nouri, Application of Schmidt rebound hammer and ultrasonic pulse velocity techniques for structural health monitoring, Scientific Research and Essays, 7(21) (2012) 1997-2001.
[17] S. Alam, A. Loukili, F. Grondin, E. Rozière, Use of the digital image correlation and acoustic emission technique to study the effect of structural size on cracking of reinforced concrete, Engineering Fracture Mechanics, 143 (2015) 17-31.
[18] J. Valença, I. Puente, E. Júlio, H. González-Jorge, P. Arias-Sánchez, Assessment of cracks on concrete bridges using image processing supported by laser scanning survey, Construction and Building Materials, 146 (2017) 668-678.
[19] M.S. Kaseko, S.G. Ritchie, A neural network-based methodology for pavement crack detection and classification, Transportation Research Part C: Emerging Technologies, 1(4) (1993) 275-291.
[20] Z. Liu, S.A. Suandi, T. Ohashi, T. Ejima, Tunnel crack detection and classification system based on image processing, in:  Machine Vision Applications in Industrial Inspection X, International Society for Optics and Photonics, 2002, pp. 145-152.
[21] H. Moon, J. Kim, Intelligent crack detecting algorithm on the concrete crack image using neural network, Proceedings of the 28th ISARC,  (2011) 1461-1467.
[22] H. Nhat-Duc, Q.-L. Nguyen, V.-D. Tran, Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network, Automation in Construction, 94 (2018) 203-213.
[23] E.J. Willemse, D.D. Pollard, On the orientation and patterns of wing cracks and solution surfaces at the tips of a sliding flaw or fault, Journal of Geophysical Research: Solid Earth, 103(B2) (1998) 2427-2438.
[24] K. Ohno, M. Ohtsu, Crack classification in concrete based on acoustic emission, Construction and Building Materials, 24(12) (2010) 2339-2346.
[25] A. Cubero-Fernandez, F.J. Rodriguez-Lozano, R. Villatoro, J. Olivares, J.M. Palomares, Efficient pavement crack detection and classification, EURASIP Journal on Image and Video Processing, 2017(1) (2017) 39.
[26] K. Chen, A. Yadav, A. Khan, Y. Meng, K.J.M. Zhu, S.i. Engineering, Improved Crack Detection and Recognition Based on Convolutional Neural Network, 2019 (2019).
[27] M. Peppa, J. Hall, J. Goodyear, J. Mills, Photogrammetric assessment and comparison of DJI Phantom 4 pro and phantom 4 RTK small unmanned aircraft systems, ISPRS Geospatial Week 2019,  (2019).
[28] Y. Xie, L. Ning, M. Wang, C. Li, Image enhancement based on histogram equalization, in:  Journal of Physics: Conference Series, IOP Publishing, 2019, pp. 012161.
[29] M.W. Gardner, S. Dorling, Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences, Atmospheric environment, 32(14-15) (1998) 2627-2636.
[30] A.A.M. Al-Saffar, H. Tao, M.A. Talab, Review of deep convolution neural network in image classification, in:  2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), IEEE, 2017, pp. 26-31.
[31] T. Suzuki, H. Kudo, Image Correction in Emission Tomography Using Deep Convolution Neural Network, in:  ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2019, pp. 3667-3671.
[32] M.T. McCann, K.H. Jin, M. Unser, Convolutional neural networks for inverse problems in imaging: A review, IEEE Signal Processing Magazine, 34(6) (2017) 85-95.
[33] D. Scherer, A. Müller, S. Behnke, Evaluation of pooling operations in convolutional architectures for object recognition, in:  International conference on artificial neural networks, Springer, 2010, pp. 92-101.
[34] K. He, X. Zhang, S. Ren, J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE transactions on pattern analysis and machine intelligence, 37(9) (2015) 1904-1916.
[35] A. Santoro, D. Raposo, D.G. Barrett, M. Malinowski, R. Pascanu, P. Battaglia, T. Lillicrap, A simple neural network module for relational reasoning, in:  Advances in neural information processing systems, 2017, pp. 4967-4976.
[36] V. Romanuke, Appropriate number and allocation of ReLUs in convolutional neural networks, Naukovi Visti NTUU KPI, (1) (2017) 69-78.
[37] W. Liu, Y. Wen, Z. Yu, M. Yang, Large-margin softmax loss for convolutional neural networks, in:  ICML, 2016, pp. 7.
[38] S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv:1502.03167,  (2015).
[39] Y.D. Chun, N.C. Kim, I.H. Jang, Content-based image retrieval using multiresolution color and texture features, IEEE Transactions on Multimedia, 10(6) (2008) 1073-1084.
[40] W. Nawaz, S. Ahmed, A. Tahir, H.A. Khan, Classification of breast cancer histology images using alexnet, in:  International Conference Image Analysis and Recognition, Springer, 2018, pp. 869-876.
[41] M.-K. Kim, Contactless Palmprint Identification Using the Pretrained VGGNet Model, Journal of Korea Multimedia Society, 21(12) (2018) 1439-1447.
[42] M. Al-Qizwini, I. Barjasteh, H. Al-Qassab, H. Radha, Deep learning algorithm for autonomous driving using googlenet, in:  2017 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2017, pp. 89-96.