![](/images/graphics-bg.png)
Artificial neural network model for forecasting concrete compressive strength and slump in Egypt
المؤلفون المشاركون
Henigal, Ashraf
al-Baltgui, Imad
al-Dwuni, Mustafa
Sari, Muhammad
المصدر
Journal of al Azhar University : Engineering Sector
العدد
المجلد 11، العدد 39 (30 إبريل/نيسان 2016)، ص ص. 435-446، 12ص.
الناشر
تاريخ النشر
2016-04-30
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Slump and compressive strength of concrete are commonly used criteria in evaluating fresh and hardened concrete.
Accordingly, prediction of such criteria is important for the quality assurance of the produced concrete.
In this paper, a Neural Network (NN) model is developed to predict concrete compressive strength and slump in Egypt.
The Artificial Neural Network (ANN) model is developed, trained and tested using 1000 different concrete mixes gathered from different batch plants distributed all over the Arab Republic of Egypt.
Important parameters that have noticeable effect on the compressive strength and slump are identified and used as the inputs for the ANNs model.
The developed model can be used either to predict the compressive strength and slump for a given mix or to estimate the different ingredients to achieve a targeted compressive strength after seven and twenty eight days.
To verify the results of the ANNs model, seventeen concrete samples are prepared and tested at laboratory and the same ingredients of the mixes are used to predict the strength and slump using the developed ANN model.
The Root Mean Square Error (RMSE) of the results for the slump and the compressive strength after 7 and 28 days equal 3.74, 1.79 and 3.05, respectively.
These results showed the ability of the developed ANNs as an effective tool to predict and estimate the compressive strength and slump of concrete in Egypt.
The analysis of the test results leads to the conclusion that this idea can be used for the development of valid systems for specifications and standards
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Henigal, Ashraf& al-Baltgui, Imad& al-Dwuni, Mustafa& Sari, Muhammad. 2016. Artificial neural network model for forecasting concrete compressive strength and slump in Egypt. Journal of al Azhar University : Engineering Sector،Vol. 11, no. 39, pp.435-446.
https://search.emarefa.net/detail/BIM-855744
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Henigal, Ashraf…[et al.]. Artificial neural network model for forecasting concrete compressive strength and slump in Egypt. Journal of al Azhar University : Engineering Sector Vol. 11, no. 39 (Apr. 2016), pp.435-446.
https://search.emarefa.net/detail/BIM-855744
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Henigal, Ashraf& al-Baltgui, Imad& al-Dwuni, Mustafa& Sari, Muhammad. Artificial neural network model for forecasting concrete compressive strength and slump in Egypt. Journal of al Azhar University : Engineering Sector. 2016. Vol. 11, no. 39, pp.435-446.
https://search.emarefa.net/detail/BIM-855744
نوع البيانات
مقالات
لغة النص
الإنجليزية
رقم السجل
BIM-855744
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)