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An optimized artificial neural network to estimate the shear strength of fibrous and RC concrete / Eman Elnoss Abdelazeem ; Supervised Osama A. Hodhod

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Eman Elnoss Abdelazeem , 2016Description: 101 P. ; 30cmOther title:
  • شبكة عصبية صناعية مثلى لتقدير مقاومة القص للخرسانة الليفية و الخرسانة المسلحة [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering Summary: The basic idea of this thesis is to use an optimized artificial neural network, ANN, to predict the shear strength of reinforcement concrete, RC, and steel fiber reinforcement concrete, SFRC, beams. All experimental data, collected from three literatures, were used in the training set of these networks. To improve the performance of training, three ANN models (one for RC beams and two for SFRC beams) were used to predict the shear strength based on the experimental data of three literatures separately. In addition, the beam sample was used more times to train the network. After that, parametric study, to estimate the effect of some input parameters on the shear strength, was presented depending on ANN models and calculation techniques. It was found that ANN models have the ability to show the effect of a certain input parameter based on more accurate training behavior
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.05.M.Sc.2016.Em.O (Browse shelf(Opens below)) Not for loan 01010110070762000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.05.M.Sc.2016.Em.O (Browse shelf(Opens below)) 70762.CD Not for loan 01020110070762000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering

The basic idea of this thesis is to use an optimized artificial neural network, ANN, to predict the shear strength of reinforcement concrete, RC, and steel fiber reinforcement concrete, SFRC, beams. All experimental data, collected from three literatures, were used in the training set of these networks. To improve the performance of training, three ANN models (one for RC beams and two for SFRC beams) were used to predict the shear strength based on the experimental data of three literatures separately. In addition, the beam sample was used more times to train the network. After that, parametric study, to estimate the effect of some input parameters on the shear strength, was presented depending on ANN models and calculation techniques. It was found that ANN models have the ability to show the effect of a certain input parameter based on more accurate training behavior

Issued also as CD

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