header
Image from OpenLibrary

Prediction of creep in concrete using multi-gene genetic programming hybridized with artificial neural network / Abdulaziz Mamdouh Ataya ; Supervised Osama A. A. Hodhod , Tamer E. A. Said

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Abdulaziz Mamdouh Ataya , 2015Description: 124 P. : plans ; 30cmOther title:
  • التنبؤ بالزحف فى الخرسانة باستخدام البرمجة الوراثية متعددة الجينات المهجنة بواسطة الشبكة العصبية الاصطناعية [Added title page title]
Subject(s): Available additional physical forms:
  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering Summary: In this study a multi - gene genetic programming (MGGP) and artificial neural network (ANN) are used to develop two models for prediction of compliance in concrete. The first model was developed by MGGP technique and the second model by hybridized MGGP - ANN. A total of 187 experimental data sets are filtered from the NU - ITI database to develop this models. The two models contain six input variables which are: average compressive strength, relative humidity, volume to surface ratio, cement type, age at start of loading and age at the creep measurement. The output is compliance in concrete. The models: ACI209, CEB, B3 and GL2000 are used to confirm the accuracy of MGGP and MGGP - ANN models by comparing the results of six models against NU-ITI database. The accuracy of the models can be arranged as follows: MGGP - ANN model, GL2000 model, MGGP model, B3 model, ACI209 model and CEB model which has the least accuracy
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.05.M.Sc.2015.Ab.P (Browse shelf(Opens below)) Not for loan 01010110067339000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.05.M.Sc.2015.Ab.P (Browse shelf(Opens below)) 67339.CD Not for loan 01020110067339000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering

In this study a multi - gene genetic programming (MGGP) and artificial neural network (ANN) are used to develop two models for prediction of compliance in concrete. The first model was developed by MGGP technique and the second model by hybridized MGGP - ANN. A total of 187 experimental data sets are filtered from the NU - ITI database to develop this models. The two models contain six input variables which are: average compressive strength, relative humidity, volume to surface ratio, cement type, age at start of loading and age at the creep measurement. The output is compliance in concrete. The models: ACI209, CEB, B3 and GL2000 are used to confirm the accuracy of MGGP and MGGP - ANN models by comparing the results of six models against NU-ITI database. The accuracy of the models can be arranged as follows: MGGP - ANN model, GL2000 model, MGGP model, B3 model, ACI209 model and CEB model which has the least accuracy

Issued also as CD

There are no comments on this title.

to post a comment.