Predicting Shear Capacity of FRP-Reinforced Concrete Beams without Stirrups by Artificial Neural Networks, Gene Expression Programming, and Regression Analysis
المؤلفون المشاركون
Jumaa, Ghazi Bahroz
Yousif, Ali Ramadhan
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-11-15
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
The shear strength prediction of fiber-reinforced polymer- (FRP-) reinforced concrete beams is one of the most complicated issues in structural engineering applications.
Developing accurate and reliable prediction models is necessary and cost saving.
This paper proposes three new prediction models, utilizing artificial neural networks (ANNs) and gene expression programming (GEP), as a recently developed artificial intelligent techniques, and nonlinear regression analysis (NLR) as a conventional technique.
For this purpose, a large database including 269 shear test results of FRP-reinforced concrete members was collected from the literature.
The performance of the proposed models is compared with a large number of available codes and previously proposed equations.
The comparative statistical analysis confirmed that the ANNs, GEP, and NLR models, in sequence, showed excellent performance, great efficiency, and high level of accuracy over all other existing models.
The ANNs model, and to a lower level the GEP model, showed the superiority in accuracy and efficiency, while the NLR model showed that it is simple, rational, and yet accurate.
Additionally, the parametric study indicated that the ANNs model defines accurately the interaction of all parameters on shear capacity prediction and have a great ability to predict the actual response of each parameter in spite of its complexity and fluctuation nature.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Jumaa, Ghazi Bahroz& Yousif, Ali Ramadhan. 2018. Predicting Shear Capacity of FRP-Reinforced Concrete Beams without Stirrups by Artificial Neural Networks, Gene Expression Programming, and Regression Analysis. Advances in Civil Engineering،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1116131
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Jumaa, Ghazi Bahroz& Yousif, Ali Ramadhan. Predicting Shear Capacity of FRP-Reinforced Concrete Beams without Stirrups by Artificial Neural Networks, Gene Expression Programming, and Regression Analysis. Advances in Civil Engineering No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1116131
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Jumaa, Ghazi Bahroz& Yousif, Ali Ramadhan. Predicting Shear Capacity of FRP-Reinforced Concrete Beams without Stirrups by Artificial Neural Networks, Gene Expression Programming, and Regression Analysis. Advances in Civil Engineering. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1116131
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1116131
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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