Predicting the compressive strength of ultra-high-performance concrete using machine learning and deep learning techniques / (Record no. 175471)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03545namaa22004211i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | EG-GICUC |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20251103145629.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251103s2025 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 | 624.1834 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 624.1834 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.13.05.M.Sc.2025.Ah.P |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Ahmed Tamer Abd El-Rahman El-Nasser, |
| Preparation | preparation. |
| 245 10 - TITLE STATEMENT | |
| Title | Predicting the compressive strength of ultra-high-performance concrete using machine learning and deep learning techniques / |
| Statement of responsibility, etc. | by Ahmed Tamer Abd El-Rahman El-Nasser ; Supervisors Prof. Dr. Mohamed I. Serag. |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | التنبؤ بمقاومة الضغط للخرسانة فائقة الاجهاد باستخدام تقنيات التعلم الآلي والتعلم العميق |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 162 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, 2025. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 157-162. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | This study highlights Ultra-High-Performance Concrete (UHPC) as a distinguished engineering innovation, leveraging machine learning (ML) and deep learning (DL) techniques to accurately predict compressive strength. Using a filtered dataset of 810 samples, the CatBoost algorithm demonstrated superior performance with an R² of 96.57%, while the Artificial Neural Network (ANN) model achieved an R² of 92.87%. The study revealed that ML models outperformed in efficiency and error metrics, whereas ANN showed sensitivity to minor changes. The findings included sensitivity analysis to identify influential factors and optimized mix designs using predictive models, offering an innovative and sustainable approach to enhancing UHPC. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | تسلط هذه الدراسة الضوء على الخرسانة فائقة الأداء (UHPC) كابتكار هندسي متميز، مستفيدة من تقنيات التعلم الآلي (ML) والتعلم العميق (DL) للتنبؤ بمقاومة الضغط بدقة. باستخدام بيانات مفلترة لـ 810 عينات، أظهرت خوارزمية CatBoost أداءً متفوقًا بنسبة R² بلغت96.57 %، بينما حقق نموذج الشبكة العصبية الاصطناعية (ANN) R² بنسبة92.87 %. كشفت الدراسة أن نماذج ML تفوقت في الكفاءة ومقاييس الخطأ، بينما أظهر ANN حساسية للتغيرات الطفيفة. تضمنت النتائج تحليل الحساسية لتحديد العوامل المؤثرة وتصميم خلطات محسنة باستخدام النماذج التنبؤية، مما يوفر نهجًا مبتكرًا ومستدامًا لتحسين UHPC.وتحسينًا. |
| 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 | High strength concrete |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | الخرسانة عالية القوة |
| 653 #1 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Ultra-High-Performance Concrete |
| -- | Machine Learning |
| -- | Deep Learning |
| -- | Compressive Strength |
| -- | Prediction |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mohamed I. Serag |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2025 |
| Supervisory body | Mohamed I. Serag |
| Discussion body | Osama A. Hodhod |
| -- | Sayed M. Ahmed |
| Universities | Cairo University |
| Faculties | Faculty of Engineering |
| Department | Department of Structural Engineering |
| 905 ## - Cataloger and Reviser Names | |
| Cataloger Name | Shimaa |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| Edition | 21 |
| Suppress in OPAC | No |
| 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 | 03.11.2025 | 92349 | Cai01.13.05.M.Sc.2025.Ah.P | 01010110092349000 | 03.11.2025 | 03.11.2025 | Thesis |