Machine learning and classical statistical techniques in predicting concrete compressive strength / (Record no. 82795)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03564cam a2200337 a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | EG-GiCUC |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250223032835.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 211024s2021 ua dh 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 | M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Classification number | Cai01.18.01.M.Sc.2021.Pe.M |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Peter Sobhy Hanna Shaker |
| 245 10 - TITLE STATEMENT | |
| Title | Machine learning and classical statistical techniques in predicting concrete compressive strength / |
| Statement of responsibility, etc. | Peter Sobhy Hanna Shaker ; Supervised Farouk Shoaib Tamam , Noura Anwar Abdelfattah |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | أساليب التعلم الالى والأحصاء الكلاسيكية فى التنبؤ باجهادات الخرسانة |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Cairo : |
| Name of publisher, distributor, etc. | Peter Sobhy Hanna Shaker , |
| Date of publication, distribution, etc. | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 78 Leaves : |
| Other physical details | charts , facsimiles ; |
| Dimensions | 30cm |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Researches - Department of Bio-Statistics and Demography |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The use of machine learning techniques is becoming more popular than classical statistical techniques due to its remarkable reduction of hypothesis. This advantage makes the machine learning techniques are increasingly used to simulate the behavior of concrete properties Which is an important research area.The compressive strength of the concrete is a major civil engineering issue.This thesis provides a comprehensive study using both classical statistical and machine learning techniques to predict the concrete compressive strength at different ages. Only multiple regression analysis is applied as a classical statistical technique while, different algorithms (linear regression, M5P tree and multilayer perceptron) are applied as machine learning technique.The variables used in the prediction models were from the knowledge of the mix itself. The independent variables are cement, fine aggregate,coarse aggregate, fly ash, BFS, free water and age while the dependent variable is the concrete compressive strength.To achieve this objective, an experimental program was divided into two phases: In Phase I, exploring process was planned to determine the most effective independent variables to the compressive strength in case of classical statistical model followed by obtaining the final model of the classic statistical technique. In Phase II, exploring, proposing models and obtaining the final model in case of applying machine learning algorithms followed by a comparison between all models of the two techniques.The results show that the Model developed using M5P tree Algorithm through machine learning technique is found to be the best model between the machine learning algorithms with an adjusted R Square value of 0.66 while, the model developed using Multiple regression analysis in case of applying classical statistical technique has an adjusted R Square value of 0.67 but it is used only to predict concrete without BFS and Fly ash.The M5P tree algorithms model verify that the machine learning algorithms model enjoys more flexibility,capability and accuracy in predicting the compressive strength of concrete |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Additional physical form available note | Issued also as CD |
| 653 #4 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Classical statistical techniques |
| 653 #4 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Machine learning techniques |
| 653 #4 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Predicting concrete compressive strength |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Farouk Shoaib Tamam , |
| Relator term | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Noura Anwar Abdelfattah , |
| 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 | Shimaa |
| Reviser | Cataloger |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| 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 | Cai01.18.01.M.Sc.2021.Pe.M | 01010110084537000 | 22.09.2023 | Thesis | ||
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | مخـــزن الرســائل الجـــامعية - البدروم | 11.02.2024 | Cai01.18.01.M.Sc.2021.Pe.M | 01020110084537000 | 22.09.2023 | CD - Rom | 84537.CD |