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Predicting the chloride ingress process inside blended concrete using artificial neural networks / (Record no. 48699)

MARC details
000 -LEADER
fixed length control field	01822cam a2200325 a 4500
003 - CONTROL NUMBER IDENTIFIER
control field	EG-GiCUC
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20250223031119.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	141213s2014 ua e f m 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency	EG-GiCUC
Language of cataloging	eng
Transcribing agency	EG-GiCUC
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title	eng
049 ## - LOCAL HOLDINGS (OCLC)
Holding library	Deposite
097 ## - Thesis Degree
Thesis Level	Ph.D
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number	Cai01.13.05.Ph.D.2014.Ha.P
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name	Hany Ibrahim Ahmed Awad
245 10 - TITLE STATEMENT
Title	Predicting the chloride ingress process inside blended concrete using artificial neural networks /
Statement of responsibility, etc.	Hany Ibrahim Ahmed Awad ; Supervised Osama A. Hodhod
246 15 - VARYING FORM OF TITLE
Title proper/short title	التنبؤ بعمل{u٠٦أأ}ة دخول الكلور{u٠٦أأ}دات داخل الخرسانة المخلوطة باستخدام الشبكات العصب{u٠٦أأ}ة الاصطناع{u٠٦أأ}ة
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc.	Cairo :
Name of publisher, distributor, etc.	Hany Ibrahim Ahmed Awad ,
Date of publication, distribution, etc.	2014
300 ## - PHYSICAL DESCRIPTION
Extent	126 P. :
Other physical details	plans ;
Dimensions	30cm
502 ## - DISSERTATION NOTE
Dissertation note	Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Civil Engineering
520 ## - SUMMARY, ETC.
Summary, etc.	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
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Additional physical form available note	Issued also as CD
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term	Chloride
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term	Diffusion
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term	Slag
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name	Osama Abdalghafour Hodhod ,
Relator term
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier	<a href="http://172.23.153.220/th.pdf">http://172.23.153.220/th.pdf</a>
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger	Nazla
Reviser	Revisor
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger	Samia
Reviser	Cataloger
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme	Dewey Decimal Classification
Koha item type	Thesis

Holdings
Source of classification or shelving scheme	Not for loan	Home library	Current library	Date acquired	Full call number	Barcode	Date last seen	Koha item type	Copy number
Dewey Decimal Classification		المكتبة المركزبة الجديدة - جامعة القاهرة	قاعة الرسائل الجامعية - الدور الاول	11.02.2024	Cai01.13.05.Ph.D.2014.Ha.P	01010110064148000	22.09.2023	Thesis
Dewey Decimal Classification		المكتبة المركزبة الجديدة - جامعة القاهرة	مخـــزن الرســائل الجـــامعية - البدروم	11.02.2024	Cai01.13.05.Ph.D.2014.Ha.P	01020110064148000	22.09.2023	CD - Rom	64148.CD

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Predicting the chloride ingress process inside blended concrete using artificial neural networks / (Record no. 48699)