کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6713348 | 1428732 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Characterization and identification of asphalt mixtures based on Convolutional Neural Network methods using X-ray scanning images
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی عمران و سازه
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چکیده انگلیسی
Accurate identification of asphalt mixtures based on images is a difficult task due to the complex components of materials under random mixing processes. This study explores the application of a Convolutional Neural Network (CNN) in classifying and identifying asphalt mixtures using the sectional images obtained from the X-ray computed tomography (CT) method. Images of 11 asphalt mixtures with two mixing types (hot mix and cold recycled mix), three nominal maximum aggregate sizes (13, 20 and 25â¯mm), and two compaction methods (Marshall and Superpave Gyratory) were obtained and segmented into three parts, including air voids, mastics, and aggregates, based on digital image processing (DIP) methods. The CNN models were trained on different combinations of image sets, and data augmentation technique was applied to the train set. Test results showed that CNN models from binary images of mastics and aggregates could success classify different asphalt mixtures with higher reliability than CNN model from images of air voids. CNN model from mastic could better distinguished different compaction methods while CNN model from aggregates could recognize different gradation types better.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Construction and Building Materials - Volume 174, 20 June 2018, Pages 72-80
Journal: Construction and Building Materials - Volume 174, 20 June 2018, Pages 72-80
نویسندگان
Jiwang Jiang, Zhen Zhang, Qiao Dong, Fujian Ni,