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Integrated approach for rebar classification of reinforced concrete elements using GPR and deep learning / (Record no. 174996)

MARC details
000 -LEADER
fixed length control field	04296namaa22004331i 4500
003 - CONTROL NUMBER IDENTIFIER
control field	OSt
005 - أخر تعامل مع التسجيلة
control field	20251102111633.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	251019s2024 ua a\|\|\|frm\|\|\| 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloguing agency	EG-GICUC
Language of cataloging	eng
Transcribing agency	EG-GICUC
Modifying agency	EG-GICUC
Description conventions	rda
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title	eng
Language code of summary or abstract	eng
--	ara
049 ## - Acquisition Source
Acquisition Source	Deposit
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	620.0042
092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC)
Classification number	620.0042
Edition number	21
097 ## - Degree
Degree	M.Sc
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Local Call Number	Cai01.13.05.M.Sc.2024.Mo.I
100 0# - MAIN ENTRY--PERSONAL NAME
Authority record control number or standard number	Mostafa Wageh Mostafa Mohamed Khedr,
Preparation	preparation.
245 10 - TITLE STATEMENT
Title	Integrated approach for rebar classification of reinforced concrete elements using GPR and deep learning /
Statement of responsibility, etc.	by Mostafa Wageh Mostafa Mohamed Khedr ; Supervision Prof. Mohamed Mahdy Marzouk, Dr. Mahmoud Samy Metawie.
246 15 - VARYING FORM OF TITLE
Title proper/short title	منھجیة لتصنيف حديد التسليح بالعناصر الخرسانية باستخدام المسح الرادارى والتعلم العميق
264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice	2024.
300 ## - PHYSICAL DESCRIPTION
Extent	115 pages :
Other physical details	illustrations ;
Dimensions	30 cm. +
Accompanying material	CD.
336 ## - CONTENT TYPE
Content type term	text
Source	rda content
337 ## - MEDIA TYPE
Media type term	Unmediated
Source	rdamedia
338 ## - CARRIER TYPE
Carrier type term	volume
Source	rdacarrier
502 ## - DISSERTATION NOTE
Dissertation note	Thesis (M.Sc)-Cairo University, 2024.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note	Bibliography: pages 105-115.
520 #3 - SUMMARY, ETC.
Summary, etc.	Reinforced rebar is a crucial aspect of the structural assessment of reinforced concrete buildings. Traditional methods often involve destructive methods that can compromise the integrity of the structure. Non-destructive testing methods, such as ground penetrating radar, have become increasingly popular for structural assessments in buildings. These methods provide valuable information about the condition of the rebar without damaging the structure. However, interpreting the data can be complex and requires specialized training. This research proposes a framework that integrates GPR with deep learning to automate rebar detection and analysis, overcoming the limitations of manual data interpretation. The framework involves three stages: creating the data set, data set preparation and processing, and training and evaluating the deep model. The integration of GPR with deep learning has the potential to improve the accuracy and efficiency of rebar detection in structural assessments. By automating the process, the proposed framework can help save time and resources while ensuring reliable results.
520 #3 - SUMMARY, ETC.
Summary, etc.	يعد حديد التسليح من العوامل الأساسية لتقييم حالة المباني، وغالبًا ما تعتمد الأساليب التقليدية على أساليب مدمرة من الممكن أن تهدد سلامة المبني بشكل عام. لقد أصبحت طرق الاختبار غير المتلفة من الطرق الشائعة، مثل الرادار مخترق الأراضي في تقييم المباني، لما توفره هذه الطرق من معلومات قيمة حول حديد التسليح دون الإضرار بهيكل المبني. ومع ذلك فإن استخدام هذه الطرق يحتوي على صعاب متمثلة في تفسير البيانات التي تحتاج إلى أصحاب المهارات العالية. ويقترح هذا البحث إطارًا لدمج التعليم الآلي لاكتشاف البيانات وتحليلها للتغلب على قيود التفسير غير الآلي الذي يـأخذ وقتًا طويلًا، وهذا الدمج يعمل علي تحسين الدقة وكفاءة الكشف عن حديد التسليح في المباني، ومن خلال هذه العملية يتم توفير الوقت والموارد مع ضمان تحقيق نتائج دقيقة
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Issues CD	Issues also as CD.
546 ## - LANGUAGE NOTE
Text Language	Text in English and abstract in Arabic & English.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	Engineering design
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	التصميم الهندسي
653 #1 - INDEX TERM--UNCONTROLLED
Uncontrolled term	Ground Penetrating Radar (GPR)
--	Reinforcement Concrete Elements
--	Rebar Detection
--	Deep Learning
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name	Mohamed Mahdy Marzouk
Relator term	thesis advisor.
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name	Mahmoud Samy Metawie
Relator term	thesis advisor.
900 ## - Thesis Information
Grant date	01-01-2025
Supervisory body	Mohamed Mahdy Marzouk
--	Mahmoud Samy Metawie
Universities	Cairo University
Faculties	Faculty of Engineering
Department	Department of Integrated Engineering Design in Construction Projects
905 ## - Cataloger and Reviser Names
Cataloger Name	Shimaa
Reviser Names	Eman Ghareb
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme	Dewey Decimal Classification
Koha item type	Thesis
Edition	21
Suppress in OPAC	No

Holdings
Source of classification or shelving scheme	Home library	Current library	Date acquired	Inventory number	Full call number	Barcode	Date last seen	Effective from	Koha item type
Dewey Decimal Classification	المكتبة المركزبة الجديدة - جامعة القاهرة	قاعة الرسائل الجامعية - الدور الاول	19.10.2025	92216	Cai01.13.05.M.Sc.2024.Mo.I	01010110092216000	19.10.2025	19.10.2025	Thesis

جامعة القاهرة

المكتبة المركزية الجديدة

مكتبة جامعة القاهرة الأهلية

Integrated approach for rebar classification of reinforced concrete elements using GPR and deep learning / (Record no. 174996)