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Developing an rtificial neural network to predict the effect of sulfate attack on the concrete structures / Sohaib Mundher Hussein Alukashy ; Supervised Osama A. Hodhod , Mostafa A. Abdeen

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Sohaib Mundher Hussein Alukashy , 2013Description: 84 P. : charts , facsimiles ; 30cmOther title:
  • تطوير شبكة عصبية اصطناعية للتنبؤ بتأثير مهاجمة الكبريتات للمنشآت الخرسانية [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering Summary: In this study, an artificail neural network (ANN) is developed to be used in prediciting the reduction incompressive strength of concrete exposed to sulfate bearing environments. A total of 214 cases are gathered from pertinent literature. The data used in developing the artificial neural network model are arranged in a format of nine inputs which are: cement type, cement content,water to cement ratio, fine and coarse aggregate contents, mineral admixture type and content, type and concentration of sulfate salt, and exposure time
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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.2013.So.D (Browse shelf(Opens below)) Not for loan 01010110061756000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.05.M.Sc.2013.So.D (Browse shelf(Opens below)) 61756.CD Not for loan 01020110061756000

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

In this study, an artificail neural network (ANN) is developed to be used in prediciting the reduction incompressive strength of concrete exposed to sulfate bearing environments. A total of 214 cases are gathered from pertinent literature. The data used in developing the artificial neural network model are arranged in a format of nine inputs which are: cement type, cement content,water to cement ratio, fine and coarse aggregate contents, mineral admixture type and content, type and concentration of sulfate salt, and exposure time

Issued also as CD

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