کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6713609 | 1428731 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Compressive strength prediction of recycled concrete based on deep learning
ترجمه فارسی عنوان
پیش بینی قدرت فشاری بتن بازیافتی براساس یادگیری عمیق
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کلمات کلیدی
بتن بازیافت شده، استحکام فشاری، مدل پیش بینی، یادگیری عمیق، شبکه عصبی محکم،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی عمران و سازه
چکیده انگلیسی
Considering on the current difficulties of predicting the compressive strength of recycled aggregate concrete, this paper proposes a prediction model based on deep learning theory. First, the deep features of water-cement ratio, recycled coarse aggregate replacement ratio, recycled fine aggregate replacement ratio, fly ash replacement ratio as well as their combinations are learned through a convolutional neural networks. Then, the prediction model is developed using the softmax regression. 74 sets of concrete block masonry with different mix ratios are used in the experiments and the results show that the prediction model based on deep learning exhibits the advantages including higher precision, higher efficiency and higher generalization ability compared with the traditional neural network model, and could be considered as a new method for calculating the strength of recycled concrete.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Construction and Building Materials - Volume 175, 30 June 2018, Pages 562-569
Journal: Construction and Building Materials - Volume 175, 30 June 2018, Pages 562-569
نویسندگان
Fangming Deng, Yigang He, Shuangxi Zhou, Yun Yu, Haigen Cheng, Xiang Wu,