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

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده عمران و محیط زیست، دانشگاه صنعتی امیرکبیر

2 دانشگاه صنعتی امیر کبیر

3 hafez

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

موضوعات


عنوان مقاله [English]

Asphalt Pavement Bleeding Evaluation using Deep Learning and Wavelet Transform

نویسندگان [English]

  • Sajad Ranjbar 1
  • Fereidoon Moghadas Nejad 2
  • HAMZEH ZAKERI 3
1 Department of Civil & Environmental Engineering, Amirkabir University of Technology
2 Dep. of Civil Engineering, Amirkabir University of Technology
3 RESEARCHER /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
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