| 000 | 01822cam a2200325 a 4500 | ||
|---|---|---|---|
| 003 | EG-GiCUC | ||
| 005 | 20250223031119.0 | ||
| 008 | 141213s2014 ua e f m 000 0 eng d | ||
| 040 |
_aEG-GiCUC _beng _cEG-GiCUC |
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| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aPh.D | ||
| 099 | _aCai01.13.05.Ph.D.2014.Ha.P | ||
| 100 | 0 | _aHany Ibrahim Ahmed Awad | |
| 245 | 1 | 0 |
_aPredicting the chloride ingress process inside blended concrete using artificial neural networks / _cHany Ibrahim Ahmed Awad ; Supervised Osama A. Hodhod |
| 246 | 1 | 5 | _aالتنبؤ بعمل{u٠٦أأ}ة دخول الكلور{u٠٦أأ}دات داخل الخرسانة المخلوطة باستخدام الشبكات العصب{u٠٦أأ}ة الاصطناع{u٠٦أأ}ة |
| 260 |
_aCairo : _bHany Ibrahim Ahmed Awad , _c2014 |
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| 300 |
_a126 P. : _bplans ; _c30cm |
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| 502 | _aThesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Civil Engineering | ||
| 520 | _aThree 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 | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aChloride | |
| 653 | 4 | _aDiffusion | |
| 653 | 4 | _aSlag | |
| 700 | 0 |
_aOsama Abdalghafour Hodhod , _eSupervisor |
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| 856 | _uhttp://172.23.153.220/th.pdf | ||
| 905 |
_aNazla _eRevisor |
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| 905 |
_aSamia _eCataloger |
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| 942 |
_2ddc _cTH |
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| 999 |
_c48699 _d48699 |
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