Predicting the chloride ingress process inside blended concrete using artificial neural networks / Hany Ibrahim Ahmed Awad ; Supervised Osama A. Hodhod
Material type:
TextLanguage: English Publication details: Cairo : Hany Ibrahim Ahmed Awad , 2014Description: 126 P. : plans ; 30cmOther title: - التنبؤ بعمل{u٠٦أأ}ة دخول الكلور{u٠٦أأ}دات داخل الخرسانة المخلوطة باستخدام الشبكات العصب{u٠٦أأ}ة الاصطناع{u٠٦أأ}ة [Added title page title]
- Issued also as CD
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Thesis
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قاعة الرسائل الجامعية - الدور الاول | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.13.05.Ph.D.2014.Ha.P (Browse shelf(Opens below)) | Not for loan | 01010110064148000 | ||
CD - Rom
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مخـــزن الرســائل الجـــامعية - البدروم | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.13.05.Ph.D.2014.Ha.P (Browse shelf(Opens below)) | 64148.CD | Not for loan | 01020110064148000 |
Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Civil Engineering
Three back-propagation neural networks (BPNN) were developed. One of them was developed to predict corrosion initiation time by simulating the error function solution to Fick`s second law of diffusion. The other two BPMMs were created to predict the chloride diffusivity in both FA and GGBFS concrete. Comparision between experimental data and ANN model predictions has proven that the developed ANN models have efficiently characterized both the chloride diffusivity of high performance concrete and the error function solution to Fick`s second law of diffustion
Issued also as CD
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