این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
International Journal of Coastal and Offshore Engineering، جلد ۳، شماره ۱، صفحات ۳۵-۴۳
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Prediction of Structural Response for HSSCC Deep Beams Implementing a Machine Learning Approach
چکیده انگلیسی مقاله High Strength Concrete (HSC) is a complex type of concrete, that meets the combination of performance and uniformity at the same time. This paper demonstrates the use of artificial neural networks (ANN) to predict the deflection of high strength reinforced concrete deep beams, which are one of the main elements in offshore structures. More than one thousand test data were collected from the experimental investigation of 6 deep beams for the case of study. The data was arranged in a format of 10 input parameters, 2 hidden layers, and 1 output as network architecture to cover the geometrical and material properties of the high strength self-compacting concrete (HSSCC) deep beam. The corresponding output value is the deflection prediction. It is found that the feed forward back-propagation neural network, 15 & 5 neurons in first and second, TRAINBR training function, could predict the load-deflection diagram with minimum error of less than 1% and maximum correlation coefficient close to 1.
کلیدواژه‌های انگلیسی مقاله Deep beam,Artificial intelligence,Deflection,HSSCC
نویسندگان مقاله Mohammad Mohammadhassani |
Academic Staff of Seismology Engineering & Risk Department, Road, Housing & Urban Development Research Center (BHRC)

Mahdi Zarrini |
Academic Staff, Islamic Azad University, Astanee-Ashrafiye Branch

Ehsan Noroozinejad Farsangi |
Academic Staff, Department of Earthquake Engineering, Graduate University of Advanced Technology, Kerman

Neda Khadem Gerayli |
Technology management, technology transfer, master of science, transportation research institute, road, housing and urban development research (BHRC)

نشانی اینترنتی https://www.ijcoe.org/article_149290_a6d098c791633bb392a7f51d0893337f.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات