پیوندهای مفید
آمار
| تعداد نشریات | 9 |
| تعداد شمارهها | 446 |
| تعداد مقالات | 5,719 |
| تعداد مشاهده مقاله | 8,018,944 |
| تعداد دریافت فایل اصل مقاله | 6,527,453 |
ارائه یک معیار جدید ارزیابی مقاومت لغزندگی روسازی آسفالتی | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 17، دوره 49، شماره 2، مرداد 1396، صفحه 389-402 اصل مقاله (1.33 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2016.591 | ||
| نویسندگان | ||
| فریدون مقدس نژاد* ؛ نسرین کریمی؛ حمزه ذاکری | ||
| دانشکده مهندسی عمران و محیط زیست، دانشگاه صنعتی امیرکبیر، تهران، ایران | ||
| چکیده | ||
| اصطکاک سطح یک روسازی نشان دهنده ایمنی روسازی است. ویژگیهای اصطکاکی یک روسازی به مشخصات بافت ریز و بافت درشت سطح آن روسازی بستگی دارد. امروزه محققان به دنبال ارائه روشهایی هستند که با دقت و سرعت بالایی مقاومت لغزشی روسازی را اندازهگیری کنند. راهکارهای ارائه شده در این زمینه بیشتر در محدوده روشهای غیر تماسی ، استفاده از لیزر و تصاویر دیجیتال است. نتایج به دست آمده نشان میدهد که پردازش تصویر به عنوان یک روش غیر تماسی و با دقت قابل قبول و سرعت بالا، میتواند زمینه خوبی برای تحقیقات بعدی درباره تعیین مقاومت لغزشی روسازی باشد. در این مطالعه سیستم هوشمندی بر مبنای پردازش تصویر معرفی میشود که قادر به آنالیز بافت روسازی و ارائه یک شاخص جدید برای مقاومت لغزشی روسازی با در نظر گرفتن تاثیر اجزای افقی، قائم و قطری بافت روسازی در آن است. با مقایسه نتایج نهایی سیستم پیشنهادی و نتیجه آزمایش پاندول بریتانیایی (BPN) متناظر با آنها میتوان گفت که، سیستم پیشنهادی با بهرهگیری از تکنولوژی پردازش تصویر و بررسی بافت روسازی قادر است بافت روسازی را درک کرده و نتایج تکرارپذیری در ارتباط با قابلیت لغزشی روسازی ارائه کند. | ||
| کلیدواژهها | ||
| مقاومت لغزشی؛ ایمنی؛ بافت روسازی؛ پردازش تصویر؛ سیستم خبره | ||
| موضوعات | ||
| راه و ترابری و حمل و نقل - روسازی انعطاف پذیر؛ مدیریت روسازی | ||
| عنوان مقاله [English] | ||
| Providing a new criterion to evaluate the skid resistance of asphalt pavement | ||
| نویسندگان [English] | ||
| F. Nejad؛ N. Karimi؛ H. Zakeri | ||
| Department of Civil & Environmental Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| چکیده [English] | ||
| The friction of a pavement surface indicates safety of a pavement. Pavement friction properties depend on pavement surface’s microtexture and macrotexture characteristics. Recently, considerable attention has been paid by researchers to find new methods and procedures for more accurate and quick measurement of the pavement skid resistance. Most of the proposed approaches in this regard have been in the range of non-contact methods, the use of laser and digital images. It has been found that image processing as a non-contact method with adequate precision and high speed can prove to be a promising and effective approach for further research on determining the pavement skid resistance. In this paper an intelligent system based on image processing is introduced which analyzes the texture of the pavement and presents a new index for pavement skid resistance by taking the effects of horizontal, vertical and diagonal components of its texture into consideration. By comparing the results of the proposed system and the corresponding British pendulum test results (BPN) it can be said that, the proposed system through using the image processing technology and more accurate assessment of pavement textures is capable of better recognizing the pavement textures and can produce repeatable results associated with the pavement skid resistance. | ||
| کلیدواژهها [English] | ||
| Skid Resistance, Safety, Pavement Texture, Image processing, Expert Systems | ||
|
سایر فایل های مرتبط با مقاله
|
||
| مراجع | ||
|
[1] E. Masad, A. Rezaei, A. Chowdhury, P. Harris, Predicting asphalt mixture skid resistance based on aggregate characteristics, Texas Department of Transportation and the Federal Highway Administration, (2009). [2] J.N. Meegoda, S. Gao, S. Liu, N.C. Gephart, Pavement texture from high-speed laser for pavement management system, International Journal of Pavement Engineering,14(8) (2013) 697-705. [3] S.M. Oh, S.M. Madanat, D.R. Ragland, C.-Y. Chan,Evaluation of traffic and environment effects on skid resistance in California, Safe Transportation Research & Education Center, (2010). [4] J. Hall, K. Smith, L. Titus-Glover, J. Wambold, T. Yager,Z. Rado, Guide for pavement friction, 1st edn, National Cooperative Highway Research Program, Transportation Research Board of the National Academies, USA,(2009). [5] P. Buddhavarapu, A. Banerjee, J.A. Prozzi, Influence of pavement condition on horizontal curve safety, Accident Analysis & Prevention, 52 (2013) 9-18. [6] I.M. Asi, Evaluating skid resistance of different asphalt concrete mixes, Building and Environment, 42(1) (2007)325-329. [7] G. Flintsch, E. de León, K. McGhee, I. AI-Qadi,Pavement surface macrotexture measurement and applications, Transportation Research Record: Journal of the Transportation Research Board, (1860) (2003) 168-177. [8] M. Khasawneh, R.Y. Liang, Temperature Effect on Frictional Properties of HMA at Different Polishing Stages, Jordan Journal of Civil Engineering, 6(1) (2012) 39-53. [9] E. Masad, A. Rezaei, A. Chowdhury, T.J. Freeman, Field evaluation of asphalt mixture skid resistance and its relationship to aggregate characteristics, Texas Transportation Institute, 2010. [10] E. Masad, A. Luce, E. Mahmoud, A. Chowdhury,Relationship of Aggregate Texture to Asphalt Pavement Skid Resistance Using Image Analysis of Aggregate Shape, Final Report for Highway IDEA Project, 114(2007). [11] A. Dunford, A. Ruff, R. Whiteoak, Measuring skid resistance without contact: 2007-2008 progress report,TRL Published Project Report, (2008). [12] H. Zelelew, A. Papagiannakis, E. de León Izeppi, Pavement macro-texture analysis using wavelets,International Journal of Pavement Engineering, 14(8)(2013) 725-735. [13] C. Huang, Texture characteristics of unpolished and polished aggregate surfaces, Tribology International,43(1) (2010) 188-196. [14] W. Wang, X. Yan, H. Huang, X. Chu, M. Abdel-Aty,Design and verification of a laser based device for pavement macrotexture measurement, Transportation Research Part C: Emerging Technologies, 19(4) (2011)682-694. [15] S.P. Hadiwardoyo, E.S. Sinaga, H. Fikri, The influence penetration index and temperature, Construction and Building Materials, 42 (2013) 5-10. [16] B. Pidwerbesky, J. Waters, D. Gransberg, R. Stemprok,Road surface texture measurement using digital image processing and information theory, Land Transport New Zealand Research Report, 290 (2006) 65-79. [17] A. Cigada, F. Mancosu, S. Manzoni, E. Zappa, Lasertriangulation device for in-line measurement of road texture at medium and high speed, Mechanical Systems and Signal Processing, 24(7) (2010) 2225-2234. [18] E.D. de Leon Izeppi, G.W. Flintsch, M. Saleh, K.K. McGhee, Area-Based Macrotexture Measurements:Stereo Vision Approach, in: Transportation Research Board 88th Annual Meeting, 2009. [19] S.N. Goodman, Y. Hassan, O. Abd El Halim, Digital Sand Patch Test: Use of Digital Image Analysis for Measurement of Pavement Macrotexture, in:Transportation Research Board 89th Annual Meeting,2010. [20] R. Elunai, V. Chandran, E. Gallagher, Asphalt concrete surfaces macrotexture determination from still images, IEEE Transactions on Intelligent Transportation Systems,12(3) (2011) 857-869. [21] T. Al-Rousan, E. Masad, E. Tutumluer, T. Pan, Evaluation of image analysis techniques for quantifying aggregate shape characteristics, Construction and Building Materials, 21(5) (2007) 978-990. [22] R. Elunai, V. Chandran, P. Mabukwa, Digital image processing techniques for pavement macro-texture analysis, in: Proceedings of the 24th ARRB Conference:Building on 50 years of road transport research, ARRB Group Ltd., 2010, pp. 1-5. [23] H. Zakeri, An expert system for pavement distress classification, MSC, amirkabir university of technology,2008. (in Persian) [24] C.T. Hendrickson, B. Janson, Expert systems and pavement management, in: Proceedings, 2nd NorthAmerican Conference on Managing Pavements, Toronto,1987, pp. 2.255-252.266. [25] S.-H. Liao, Expert system methodologies and applications—a decade review from 1995 to 2004,Expert systems with applications, 28(1) (2005) 93-103. [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. [27] S.M. Kiya, digital image processing in MATLAB, khadamat nashr kiyan rayaneye sabz, 2009. (in Persian) [28] F.M. 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. [29] N. Karimi, automatic skid resistance measurement system based on image processing, MSC, amirkabir university of technology, 2011. (in Persian) [30] F.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. [31] F.M. Nejad, H. Zakeri, A comparison of multi-resolution methods for detection and isolation of pavement distress,Expert Systems with Applications, 38(3) (2011) 2857-2872. [32] A.M. Atto, Y. Berthoumieu, P. Bolon, 2-D wavelet packet spectrum for texture analysis, IEEE Transactions on Image Processing, 22(6) (2013) 2495-2500. [33] F.M. Nejad, H. Zakeri, The hybrid method and its application to smart pavement management,Metaheuristics in Water, Geotechnical and Transport Engineering, Elsevier, Waltham, (2012) 439-484. [34] A.W. Busch, Wavelet transform for texture analysis with application to document analysis, Queensland University of Technology, 2004. [35] 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. [36] M. Virvou, K. Kabassi, Evaluating an intelligent graphical user interface by comparison with human experts, Knowledge-Based Systems, 17(1) (2004) 31-37. [37] F. Mustakim, Treating Hazardous Locations at Federal Route 50, Universiti Teknologi Malaysia, 2005. | ||
|
آمار تعداد مشاهده مقاله: 2,465 تعداد دریافت فایل اصل مقاله: 1,648 |
||
