آمار

تعداد نشریات7
تعداد شماره‌ها406
تعداد مقالات5,427
تعداد مشاهده مقاله5,579,835
تعداد دریافت فایل اصل مقاله5,037,107
رنجبر, سجاد, مقدس نژاد, فریدون, ذاکری, حمزه. (1400). ارزیابی خرابی قیرزدگی روسازی آسفالتی با استفاده از یادگیری عمیق و تبدیل موجک. نشریه مهندسی عمران امیرکبیر, 53(11), 4577-4598. doi: 10.22060/ceej.2020.18292.6820
سجاد رنجبر; فریدون مقدس نژاد; حمزه ذاکری. "ارزیابی خرابی قیرزدگی روسازی آسفالتی با استفاده از یادگیری عمیق و تبدیل موجک". نشریه مهندسی عمران امیرکبیر, 53, 11, 1400, 4577-4598. doi: 10.22060/ceej.2020.18292.6820
رنجبر, سجاد, مقدس نژاد, فریدون, ذاکری, حمزه. (1400). 'ارزیابی خرابی قیرزدگی روسازی آسفالتی با استفاده از یادگیری عمیق و تبدیل موجک', نشریه مهندسی عمران امیرکبیر, 53(11), pp. 4577-4598. doi: 10.22060/ceej.2020.18292.6820
رنجبر, سجاد, مقدس نژاد, فریدون, ذاکری, حمزه. ارزیابی خرابی قیرزدگی روسازی آسفالتی با استفاده از یادگیری عمیق و تبدیل موجک. نشریه مهندسی عمران امیرکبیر, 1400; 53(11): 4577-4598. doi: 10.22060/ceej.2020.18292.6820

ارزیابی خرابی قیرزدگی روسازی آسفالتی با استفاده از یادگیری عمیق و تبدیل موجک

نشریه مهندسی عمران امیرکبیر
مقاله 2، دوره 53، شماره 11، بهمن 1400، صفحه 4577-4598    اصل مقاله (3.59 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22060/ceej.2020.18292.6820
نویسندگان
سجاد رنجبر1؛ فریدون مقدس نژاد* 2؛ حمزه ذاکری3
1دانشکده عمران و محیط زیست، دانشگاه صنعتی امیرکبیر
2دانشگاه صنعتی امیر کبیر
3hafez
چکیده
اطلاعات مرتبط با وضعیت روسازی نظیر خرابی‌ها، ورودی و مواد اولیه سامانه مدیریت روسازی را تأمین می‌کند. در صورت عدم ارزیابی وضعیت روسازی و یا ارزیابی ناقص و نادرست وضعیت روسازی، امکان انجام عملیات تعمیر و نگهداری مناسب و به موقع وجود نخواهد داشت که این موضوع به افزایش هزینه‌های نگهداری و بهسازی و افزایش احتمال بروز تصادفات منجر خواهد شد. از این رو، تحقیقات گسترده‌ای با هدف بکارگیری فناوری‌های جدید در جهت ارزیابی دقیق و خودکار خرابی‌های روسازی انجام شده است. خرابی قیرزدگی یکی از خرابی‌های روسازی آسفالتی است که مستقیماً بر اصطکاک سطحی و مانورپذیری وسایل نقلیه تأثیر می‌گذارد. علی‌رغم اهمیت خرابی قیرزدگی، ارزیابی خودکار این خرابی نسبت به سایر خرابی‌ها نظیر ترک‌خوردگی، چاله، شیارافتادگی کمتر مورد توجه جامعه تحقیق بوده است. در این پژوهش، سعی شده است که با استفاده از روش‌های جدید نظیر یادگیری عمیق و ابزارهای مختلف پردازش تصویر، یک سامانه کارآمد مبتنی بر تصویر به منظور ارزیابی خودکار خرابی قیرزدگی ارائه شود. برای این منظور، از روش انتقال یادگیری برای ساخت مدل تشخیص خرابی و از یک فرآیند پردازش تصویر مبتنی بر تبدیل موجک برای تفکیک نواحی قیرزده استفاده شد. نتایج به دست آمده نشان می‌دهد که سامانه ارائه شده در این پژوهش، در تشخیص خرابی و تفکیک نواحی قیرزده به ترتیب با متوسط بالای 98 و 87 درصد، عملکرد خوبی در ارزیابی خرابی قیرزدگی دارد و می‌تواند به عنوان ابزاری کارآمد در ارزیابی قیرزدگی بکار گرفته شود.
کلیدواژه‌ها
سامانه مدیریت روسازی؛ ارزیابی خرابی‌؛ قیرزدگی؛ یادگیری عمیق؛ تبدیل موجک
موضوعات
بینائی کامپیوتری؛ پایش سلامت راه؛ پردازش تصویر؛ شناسائی اتوماتیک
عنوان مقاله [English]
Asphalt Pavement Bleeding Evaluation using Deep Learning and Wavelet Transform
نویسندگان [English]
Sajad Ranjbar1؛ Fereidoon Moghadas Nejad2؛ HAMZEH ZAKERI3
1Department of Civil & Environmental Engineering, Amirkabir University of Technology
2Dep. of Civil Engineering, Amirkabir University of Technology
3RESEARCHER /AUT
چکیده [English]
Pavement inspection is an important part of pavement management systems because this part provides input and raw material information to the system. If the pavement situation has not been assessed or incorrectly assessed, it will not be possible to carry out optimum maintenance and repair operations. It can also cause higher maintenance costs and the risk of accidents. Pavement distress information is crucial data that should be collected and evaluated in the pavement inspection process. Accordingly, wide research has been conducted to develop more efficient systems for the evaluation of pavement distresses using new technologies. Bleeding is one of the asphalt pavement distresses, which directly affects the skid resistance and vehicle maneuverability. Based on the literature, pavement bleeding received the attention from the research community less than other pavement distress such as cracks, rutting, raveling, and potholes. This research attempts to develop an efficient system for the automatic evaluation of asphalt pavement bleeding. For this aim, the transfer learning method was applied to train a pre-trained convolutional neural network for bleeding detection. Also, various image processing techniques (wavelet transform analysis as the main technique) were used to segment bleeding regions in pavement images. Results indicated that the proposed system has good performance in bleeding detection and segmentation with 98% and 87%, respectively. Accordingly, this system can be applied as an efficient system for pavement bleeding evaluation.
کلیدواژه‌ها [English]
Pavement management system, Distress evaluation, Bleeding, Deep learning, Wavelet transform
مراجع
  1. Y. Shahin, Pavement management for airports, roads, and parking lots, 1994.
  2. Zakeri, F.M. Nejad, A. Fahimifar, Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review, Archives of Computational Methods in Engineering, 24(4) (2017) 935-977.
  3. Zakeri, F.M. Nejad, A. Fahimifar, Rahbin: A quadcopter unmanned aerial vehicle based on a systematic image processing approach toward an automated asphalt pavement inspection, Automation in Construction, 72(Part 2) (2016) 211-235.
  4. Koch, K. Georgieva, V. Kasireddy, B. Akinci, P. Fieguth, A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure, Advanced Engineering Informatics, 292 (2015) 196-210.
  5. Mataei, H. Zakeri, M. Zahedi, F.M. Nejad, Pavement friction and skid resistance measurement methods: a literature review, Open J. Civ. Eng., 6(04) (2016) 537.
  6. S. Miller, W.Y. Bellinger, Distress identification manual for the long-term pavement performance program, United States. Federal Highway Administration. Office of Infrastructure Research and Development, 2014.
  7. Kodippily, T.F.P. Henning, J.M. Ingham, G. Holleran, Quantifying the effects of chip seal volumetrics on the occurrence of pavement flushing, Journal of Materials in Civil Engineering, 26(8) (2014).
  8. Bennert, Determining Binder Flushing Causes in New York State Final Report, (2014).
  9. Khosravi, S.M. Abtahi, B. Koosha, M. Manian, An analytical–empirical investigation of the bleeding mechanism of asphalt mixes, Construction and Building Materials, 45(Supplement C) (2013) 138-144.
  10. Lawson, S. Senadheera, Chip seal maintenance: solutions for bleeding and flushed pavement surfaces, Transportation Research Record: Journal of the Transportation Research Board, (2108) (2009) 61-68.
  11. Henderson, P. Cenek, N. Jamieson, D. Wilson, The influence of binder rise in reducing tyre–road friction June 2011, (2011).
  12. Arbabpour Bidgoli, A. Golroo, H. Sheikhzadeh Nadjar, A. Ghelmani Rashidabad, M.R. Ganji, Road roughness measurement using a cost-effective sensor-based monitoring system, Automation in Construction, 104 (2019) 140-152.
  13. Designation, D6433 Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys, (2007).
  14. B.J. Coenen, A. Golroo, A review on automated pavement distress detection methods, Cogent Engineering, 4(1) (2017).
  15. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature, 521 (2015) 436.
  16. Goodfellow, Y. Bengio, A. Courville, Y. Bengio, Deep learning, MIT press Cambridge, 2016.
  17. Li, K.C. Wang, A. Zhang, E. Yang, G. Wang, Automatic classification of pavement crack using deep convolutional neural network, International Journal of Pavement Engineering, (2018) 1-7.
  18. Tong, D. Yuan, J. Gao, Z. Wang, Pavement defect detection with fully convolutional network and an uncertainty framework, Computer‐Aided Civil and Infrastructure Engineering, (2020).
  19. Liu, J. Yao, X. Lu, R. Xie, L. Li, DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation, Neurocomputing, (2019).
  20. Ye, W. Jiang, Z. Tong, D. Yuan, J. Xiao, Convolutional neural network for pothole detection in asphalt pavement, Road Materials and Pavement Design, (2019) 1-17.
  21. Song, X. Wang, Faster region convolutional neural network for automated pavement distress detection, Road Materials and Pavement Design, (2019) 1-19.
  22. S. Addison, The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance, CRC press, 2017.
  23. Yang, A. Alhasan, Y. Zhang, H. Ceylan, S. Kim, Pavement curling and warping analysis using wavelet techniques, International Journal of Pavement Engineering, (2020) 1-16.
  24. Zhang, C. Sun, R. Bridgelall, M. Sun, Road profile reconstruction using connected vehicle responses and wavelet analysis, Journal of Terramechanics, 80 (2018) 21-30.
  25. Khan, F. Qiao, L. Yu, Wavelet analysis to characterize the dependency of vehicular emissions on road roughness, Transportation Research Record, 2641(1) (2017) 111-125
  26. Alhasan, D.J. White, K. De Brabanter, Wavelet filter design for pavement roughness analysis, Computer‐Aided Civil and Infrastructure Engineering, 31(12) (2016) 907-920.
  27. Yang, Q.J. Li, Y.J. Zhan, K.C. Wang, C. Wang, Wavelet based macrotexture analysis for pavement friction prediction, KSCE Journal of Civil Engineering, 22(1) (2018) 117-124.
  28. S. Rodrigues, M. Pasin, A. Kozakevicius, V. Monego, Pothole Detection in Asphalt: An Automated Approach to Threshold Computation Based on the Haar Wavelet Transform, in: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), IEEE, 2019, pp. 306-315.
  29. Luo, X. Qu, X. Qing, J. Gu, CT Image Denoising Using Double Density Dual Tree Complex Wavelet with Modified Thresholding, in: 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA), IEEE, 2018, pp. 287-290.
  30. S. Yaseen, O.N. Pavlova, A.N. Pavlov, A.E. Hramov, Image denoising with the dual-tree complex wavelet transform, in: Saratov Fall Meeting 2015: Third International Symposium on Optics and Biophotonics and Seventh Finnish-Russian Photonics and Laser Symposium (PALS), International Society for Optics and Photonics, 2016.
  31. Prasad, G. Umamadhuri, Biorthogonal Wavelet-based Image Compression, in: Artificial Intelligence and Evolutionary Computations in Engineering Systems, Springer, 2018, pp. 391-404.
  32. Karthikeyan, C. Palanisamy, An Efficient Image Compression Method by Using Optimized Discrete Wavelet Transform and Huffman Encoder, Journal of Computational Theoretical Nanoscience, 15(1) (2018) 289-298.
  33. Bakhshipour, A. Jafari, S.M. Nassiri, D. Zare, Weed segmentation using texture features extracted from wavelet sub-images, Biosystems Engineering, 157 (2017) 1-12.
  34. M. Atto, Y. Berthoumieu, P. Bolon, 2-d wavelet packet spectrum for texture analysis, IEEE Transactions on Image Processing, 22(6) (2013) 2495-2500.
  35. O. Ouma, M. Hahn, Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform, Advanced Engineering Informatics, 30(3) (2016) 481-499.
  36. Moghadas Nejad, H. Zakeri, An expert system based on wavelet transform and radon neural network for pavement distress classification, Expert Systems with Applications, 38(6) (2011) 7088-7101.
  37. Wang, Y. Hu, Y. Dai, M. Tian, Asphalt Pavement Pothole Detection and Segmentation Based on Wavelet Energy Field, Mathematical Problems in Engineering, 2017 (2017) 13.
  38. Mataei, F. Moghadas Nejad, M. Zahedi, H. Zakeri, Evaluation of pavement surface drainage using an automated image acquisition and processing system, Automation in Construction, 86 (2018) 240-255.
  39. M. Nejad, N. Karimi, H. Zakeri, Automatic image acquisition with knowledge-based approach for multi-directional determination of skid resistance of pavements, Automation in Construction, 71(Part 2) (2016) 414-429.
  40. Karaşahin, M. Saltan, S. Çetin, Determination of seal coat deterioration using image processing methods, Construction and Building Materials, 53(Supplement C) (2014) 273-283.
  41. M. Hadjidemetriou, S.E. Christodoulou, Vision-and Entropy-Based Detection of Distressed Areas for Integrated Pavement Condition Assessment, Journal of Computing in Civil Engineering, 33(3) (2019).
  42. Laurent, J. Hébert, M. Talbot, Using full lane 3D road texture data for the automated detection of sealed cracks, bleeding and ravelling, in: Proceedings of the World Conference on Pavement and Asset Management, Milan, Italy, 2017, pp. 12-16.
  43. Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT press, 2016.
  44. A. Nielsen, Neural networks and deep learning, Determination press USA, 2015.
  45. Gopalakrishnan, S.K. Khaitan, A. Choudhary, A. Agrawal, Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection, Construction and Building Materials, 157 (2017) 322-330.
  46. Zhang, H. Cheng, B. Zhang, Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning, Journal of Computing in Civil Engineering, 32(2) (2018) 04018001.
  47. J. Pan, Q. Yang, A survey on transfer learning, IEEE Transactions on knowledge data engineering, 22(10) (2010) 1345-1359.
  48. Gao, K.M. Mosalam, Deep Transfer Learning for Image-Based Structural Damage Recognition, Computer‐Aided Civil and Infrastructure Engineering, 33(9) (2018) 748-768.
  49. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, Imagenet large scale visual recognition challenge, International Journal of Computer Vision, 115(3) (2015) 211-252.
  50. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in neural information processing systems, 2012, pp. 1097-1105.
  51. C. Gonzalez, R.E. Woods, Digital image processing, (2007).
  52. M. Nejad, H. Zakeri, An optimum feature extraction method based on Wavelet–Radon Transform and Dynamic Neural Network for pavement distress classification, Expert Systems with Applications, 38(8) (2011) 9442-9460.
  53. M. Nejad, H. Zakeri, The Hybrid Method and its Application to Smart Pavement Management, in: X.-S. Yang, A.H. Gandomi, S. Talatahari, A.H. Alavi (Eds.) Metaheuristics in Water, Geotechnical and Transport Engineering, Elsevier, Oxford, 2013, pp. 439-484.
  54. Gao, K.M. Mosalam, Deep Transfer Learning for Image-Based Structural Damage Recognition, 33(9) (2018) 748-768.
  55. R. Young, D.C. Rose, T.P. Karnowski, S.-H. Lim, R.M. Patton, Optimizing deep learning hyper-parameters through an evolutionary algorithm, in: Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, ACM, 2015, pp. 4.
آمار
تعداد مشاهده مقاله: 858
تعداد دریافت فایل اصل مقاله: 1,419


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب