Optimization of the mix design of styrofoam light weight concrete using orthogonal arrays and neural networks / Amr Hamdy Mohamed Shawat ; Supervised Osama Abdelghafor Hodhod , Hatem Hassan Ali

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Amr Hamdy Mohamed Shawat

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TextLanguage: English Publication details: Cairo : Amr Hamdy Mohamed Shawat , 2020Description: 179 P. : charts , facimiles ; 30cmOther title:

تحقيق الأمثلية فى تصميم خلطات الخرسانة خفيفة الوزن ذات ركام الستايروفوم باستخدام المصفوفات المتعامده و الشبكات العصبية الصناعية [Added title page title]

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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering Summary: This research aims at creating a mathematical model for optimizing the mix proportions of extruded polystyrene Styrofoam aggregate concrete. This was achieved by an experimental program based on the application of orthogonal arrays to create a signal-to-noise ratio analysis that investigates the effects of 10 mixing parameters on 6 fresh and mechanical properties.The data is used to train and verify an artificial neural network that predicts the resultant properties of any given concrete mix within its operational parameters

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Item type	Current library	Home library	Call number	Copy number	Status	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.05.M.Sc.2020.Am.O (Browse shelf(Opens below))		Not for loan	01010110081861000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.05.M.Sc.2020.Am.O (Browse shelf(Opens below))	81861.CD	Not for loan	01020110081861000

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

This research aims at creating a mathematical model for optimizing the mix proportions of extruded polystyrene Styrofoam aggregate concrete. This was achieved by an experimental program based on the application of orthogonal arrays to create a signal-to-noise ratio analysis that investigates the effects of 10 mixing parameters on 6 fresh and mechanical properties.The data is used to train and verify an artificial neural network that predicts the resultant properties of any given concrete mix within its operational parameters

Issued also as CD

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