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ارائه شاخصی جدید جهت ارزیابی خودکار توزیع یکنواخت اندود سطحی و نفوذی روسازی راهها | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 3، دوره 53، شماره 3، خرداد 1400، صفحه 823-846 اصل مقاله (2.72 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2020.16463.6250 | ||
| نویسندگان | ||
| مژگان حاجی علی1؛ فریدون مقدس نژاد* 2؛ حمزه ذاکری3 | ||
| 1مهندسی عمران(راه و ترابری)، دانشکده مهندسی عمران و محیط زیست، دانشگاه امیرکبیر، تهران، ایران | ||
| 2دانشگاه صنعتی امیر کبیر | ||
| 3hafez | ||
| چکیده | ||
| اندودها، یکی از اجزای تأثیرگذار در کارایی و عمر رویه راهها است. پارامترهایی در اجرای صحیح اندودها از جمله نوع اندود، زمان عملآوری، نرخ اجرا، دما، یکنواختی اجرا و غیره تأثیرگذار است. یکنواختی اجرا نیاز به کنترل میدانی دارد و در حال حاضر جهت کنترل وزنی اندود پخش شده از آزمایش سینی استفاده میشود، این آزمایش بدلیل پیوسته نبودن برداشت و نحوه همپوشانی، خطاهای زیادی دارد. یکی از موضوعاتی که کمتر به آن توجه شده است، بررسی یکنواختی اجرای اندود میباشد. در این پژوهش سامانه خودکاری با استفاده از دوربین، موقعیتیاب، برد میکروکنترلر و ... بر مبنای پردازش تصویر ارائه شده است که قادر به تحلیل توزیع یکنواخت اندود و ارائه دستهبندی خوب، متوسط و ضعیف برای ارزیابی خودکار توزیع یکنواخت اندود است. با استفاده از پردازش تصویر، کیفیت تصاویر ارتقاء داده شده و تصاویر فشرده و کاهش نویز گردید. برای جداسازی اندود اجرا شده از پیشزمینه تصویر از آستانهگذاری استفاده گردید. پس از آستانهگذاری، ویژگیهای مختلفی مانند مساحت اندود، ضریب تغییرات، بیشینه و کمینههای نسبی و غیره از تصاویر بدست آمده، و برای ارزیابی وضعیت توزیع اندود مورد استفاده قرار میگیرد. جهت انتخاب ویژگیهای مؤثر در دستهبندی تصاویر از الگوریتمهای طبقهبندی استفاده شد. مقایسه نتایج بدست آمده از دستهبندی تصاویر بوسیله ماتریس درهمریختگی صورت گرفت، که در نهایت نتایج نشان داد که سیستم ارائه شده دقتی برابر 86% دارد. همچنین با استفاده از پارامترهای مؤثر در مدل، شاخص توزیع یکنواخت اندود ارائه گردید. این شاخص مقداری بین 0 تا 100 دارد که نشاندهنده بهترین و بدترین حالت توزیع اندود میباشد. | ||
| کلیدواژهها | ||
| سامانه خودکار؛ اندود سطحی؛ اندود نفوذی؛ توزیع یکنواخت؛ پردازش تصویر | ||
| موضوعات | ||
| پایش سلامت راه؛ پردازش تصویر؛ شناسائی اتوماتیک؛ کنترل خودکار؛ مدیریت هوشمند | ||
| عنوان مقاله [English] | ||
| Providing Criterion to Automatic Evaluation of the Accuracy of Distribution of Tack Coat and Prime Coat Pavement Roads | ||
| نویسندگان [English] | ||
| mozhgan hajiali1؛ Fereydoon Moghaddasnezhad2؛ HAMZEH ZAKERI3 | ||
| 1Department of Civil Engineering and Environmental, Amirkabir University of Technology, Tehran, Iran | ||
| 2Department of Civil and Environmental Engineering, Amirkabir University of Technology(AUT), Thehran. Iran | ||
| 3RESEARCHER /AUT | ||
| چکیده [English] | ||
| The coating is one of the most important components that affect the efficiency of the pavements. Parameters are effective in the proper implementation of the coating such as the type of coating, the application time, the rate of application, temperature, uniformity of application, etc. The uniformity of application requires field control in the project implementation, is currently used to control the spreading weight of the tray. This test has many errors due to the lack of continuity. The issue of uniform distribution of coating has become less attention. In this study, the automatic system is presented based on image processing using a camera, GPS, microcontroller board, and ..., which can analyze the uniform distribution and provide a good, moderate and poor classification for coating distribution evaluation. Image quality has improved with image processing and compression and noise reduction have been done. The thresholding was used to separate the coating from the background. After the thresholding, various properties such as the area of the coating, coefficient of variation, local maximum, and minimum, etc. are obtained from the images and used to evaluate the coating distribution. Used categorization algorithms to select effective features in categorizing images. A comparison of the results of the classification of images by a confusion matrix. Finally, the results showed that the presented system has a precision of 86%. Also, using the effective parameters in the model, the uniform distribution index was presented. This index has a value between0 and 100, which indicates the best and worst distributions. | ||
| کلیدواژهها [English] | ||
| Automatic system, Tack coat, Prime coat, Uniform distribution, Image processing | ||
| مراجع | ||
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[1] Karshenas, Tack Coat Bond Strength Evaluation Methods and Mechanistic Design of the Interface for Multilayer Asphalt Pavement, University of North Carolina, 2015. [2] Asphalt-Institute, HMA Construction of Hot Mix Asphalt Pavements; Manual Series No. 22 (HMA Construction), (2003). [3] L.N. Mohammad, M. Raqib, B. Huang, Influence of asphalt tack coat materials on interface shear strength, Transportation Research Record, 1789(1) (2002) 56-65. [4] L.N. Mohammad, M. Raqib, B. Huang, Influence of asphalt tack coat materials on interface shear strength, Transportation Research Record, 1789(1) (2002) 56-65. [5] Asphalt-Institute, a basic asphalt emulsion manual. Manual series no. 19. Third edition, (1997) Chapter three. [6] U.S.D.o. Transportation, Hot-Mix Asphalt Paving handbook, 2 ed., 2000. [7] K. Hasiba, Development of a testing approach for tack coat application rate at pavement layer interfaces, University of Illinois at Urbana-Champaign, 2012. [8] J.-S. Chen, C.-C. Huang, Effect of surface characteristics on bonding properties of bituminous tack coat, Transportation Research Record, 2180(1) (2010) 142-149. [9] A. Raposeiras, D. Castro-Fresno, A. Vega-Zamanillo, J. Rodriguez-Hernandez, Test methods and influential factors for analysis of bonding between bituminous pavement layers, Construction and Building Materials, 43 (2013) 372-381. [10] Bae, L.N. Mohammad, M.A. Elseifi, J. Button, N. Patel, Effects of temperature on interface shear strength of emulsified tack coats and its relationship to rheological properties, Transportation Research Record, 2180(1) (2010) 102-109. [11] J.C. Du, Evaluation of shear strength on pavement layers by use tack materials, in: Advanced Materials Research, Trans Tech Publ, 2011, pp. 3176-3179. [12] Deysarkar, Test set-up to determine quality of tack coat, The University of Texas at El Paso, 2004. [13] B.B. Sutradhar, Evaluation of bond between bituminous pavements layers, National Institute of Thechnology, 2012. [14] L.N. Mohammad, A. Bae, M.A. Elseifi, J. Button, J.A. Scherocman, Evaluation of bond strength of tack coat materials in field: Development of pull-off test device and methodology, Transportation Research Record, 2126(1) (2009) 1-11. [15] L. Tashman, K. Nam, T. Papagiannakis, K. Willoughby, L. Pierce, T. Baker, Evaluation of construction practices that influence the bond strength at the interface between pavement layers, Journal of Performance of Constructed Facilities, 22(3) (2008) 154-161. [16] 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. [17] N.-D. Hoang, Q.-L. Nguyen, D. TienBui, Image processing–based classification of asphalt pavement cracks using support vector machine optimized by artificial bee colony, Journal of Computing in Civil Engineering 32.5, (2018). [18] N.-D. Hoang, Detection of surface crack in building structures using image processing technique with an improved Otsu method for image thresholding, Advances in Civil Engineering, (2018). [19] Y.S. Kumbargeri, I. Boz, M.E. Kutay, Investigating the effect of binder and aggregate application rates on performance of chip seals via digital image processing and sweep tests, Construction and Building Materials 222, (2019) 213-221. [20] Xing, H. Xu, Y. Tan, X. Liu, Q. Ye, Mesostructured property of aggregate disruption in asphalt mixture based on digital image processing method, Construction and Building Materials 200, (2019) 781-789. [21] M. Staniek, Detection of cracks in asphalt pavement during road inspection processes, Zeszyty Naukowe. Transport/Politechnika Śląska, (2017). [22] M. Baqersad, A. Hamedi, M. Mohammadafzali, H. Ali, Asphalt mixture segregation detection: digital image processing approach, Advances in Materials Science and Engineering, (2017). [23] Y. Noh, D. Koo, Y.-M. Kang, D. Park, D. Lee, Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering, International Conference on Applied System Innovation (ICASI), (2017). [24] X. Xie, G. Lu, P. Liu, D. Wang, Q. Fan, M. Oeser, Evaluation of morphological characteristics of fine aggregate in asphalt pavement, Construction and Building Materials, (2017) 1-8. [25] H. zakeri, An expert system for pavement distress classification, Amirkabir University of Technology, 2008. [26] N. Ismail, A. Ismail, R. Atiq, An overview of expert systems in pavement management, European Journal of Scientific Research, 30(1) (2009) 99-111.v [27] Image processing toolbox user’s guide, the mathworks Inc, (2011). [28] J.A. Stark, Adaptive image contrast enhancement using generalizations of histogram equalization, IEEE Transactions on image processing, 9(5) (2000) 889-896. [29] O.S. Temiatse, S. Misra, C. Dhawale, R. Ahuja, V. Matthews, Image Enhancement of Lemon Grasses Using Image Processing Techniques (Histogram Equalization), in: International Conference on Recent Developments in Science, Engineering and Technology, Springer, 2017, pp. 298-308. [30] R.C. Gonzalez, R.E. Woods, S.L. Eddins, Digital image processing using MATLAB. 2004, Publisher T. Robbins, Printed in USA, 11 (2009) 531-534. [31] Y. Chang, C. Jung, P. Ke, H. Song, J. Hwang, Automatic contrast-limited adaptive histogram equalization with dual gamma correction, IEEE Access, 6 (2018) 11782-11792. [32] R.C. Gonzalez, R.E. Woods, Digital image processing [M], Publishing house of electronics industry, 141(7) (2002). [33] A.W. Busch, Wavelet transform for texture analysis with application to document analysis, Queensland University of Technology, 2004. [34] E. Avci, An expert system based on Wavelet Neural Network-Adaptive Norm Entropy for scale invariant texture classification, Expert Systems with Applications, 32(3) (2007) 919-926. [35] J.S. Walker, A primer on wavelets and their scientific applications, CRC press, 2002. [36] X. Zheng, H. Ye, Y. Tang, Image bi-level thresholding based on gray level-local variance histogram, Entropy, 19(5) (2017) 191. [37] M. Sezgin, B. Sankur, Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic imaging, 13(1) (2004) 146-166. [38] M. Hossin, M. Sulaiman, A review on evaluation metrics for data classification evaluations, International Journal of Data Mining & Knowledge Management Process, 5(2) (2015) 1.
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