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ANN Model for Predicting Ultimate Shear Strength of Reinforced Concrete Corbels
Author(s) -
Abdalkader A. Mohammed,
Salim T. Yousif
Publication year - 2011
Publication title -
maǧallaẗ al-handasaẗ al-rāfidayn
Language(s) - English
Resource type - Journals
eISSN - 2220-1270
pISSN - 1813-0526
DOI - 10.33899/rengj.2011.26615
Subject(s) - artificial neural network , reinforcement , parametric statistics , reinforced concrete , structural engineering , shear (geology) , sigmoid function , shear strength (soil) , computer science , materials science , mathematics , engineering , geology , artificial intelligence , composite material , statistics , soil science , soil water
The artificial neural network (ANN) model was developed using previous experimental data on Reinforced concrete (RC) corbels to simulate the behavior of RC corbels. The neural network model has six input parameters representing the concrete compressive strength ( ), shear span (a), effective depth (d), corbel width (b), area of main reinforcement (As), area of secondary reinforcement (Ah), one output parameter representing the ultimate shear load (Vu). A back propagation neural network (BPNN) with the log-sigmoid activation function is adopted due to its accuracy of prediction. The ANN model is constructed using the experimental data from the literature. The ANN predicted ultimate shear load which compared with those calculated by ACI31808 code Formula and Russo model. The neural network model is to predict the shear load of RC corbel more accurate than the ACI318-08 code Formula, and Russo model. Through the parametric studies using the ANN model, the effects of various parameters such as ( , a, d, b, As, and Ah) on the behavior of RC corbel were shown. The results reveal that the proposed network model captures the RC corbel underlying shear behavior very well. KEY WORD: Artificial neural network, Corbel, Reinforced concrete, Shear Strength. ةحلسملا ةیناسرخلا فئاتكلل ىوصقلا صقلا ةمواقمب ؤبنتلل ةیبصعلا تاكبشلا جذومن قلا دبع فسوی بیط ملاس .د دمحم يلع ردا ةصلاخلا روط اكاحمل ةحلسملا ةیناسرخلا فئاتكلا ىلع ةقباس ةیلمع جئاتن مادختساب ةیبصعلا ةكبشلا جذومن ة اھكولس . نإ ةیبصعلا ةكبشلا جذومن ةساردلا هذھ يف مدختسملا ةناسرخلا طاغضنا ةمواقم لثمت ةلخدم تلاماعم ةتس نم نوكتی ضرعو رثؤملا قمعلاو صقلا ءاضفو لثمی دحاو جرخم و يوناثلا حیلستلا ةحاسمو يسیئرلا حیلستلا ةحاسمو فتكلا صقلا لمح ىصقلأا . دامتعا مت ةیبصعلا ةكبشلا راشتنلاا تاذ ةلاد عم يفلخلا لیعفتلا عونلا نم (log-sigmoid) كلذو .ؤبنتلا يف اھتقدل أشنا تاساردلا نم ةیلمعلا جئاتنلا مادختساب ةیبصعلا تاكبشلا جذومن .ةقباسلا نإ صقلا لمح جتانلا عم نروق ةیعانطصلاا ةیبصعلا ةكبشلا نم ذ كل ةیكیرملاا ةناسرخلا ةنودم ةلداعم نم بوسحملا ACI318-08 ةلداعمو ثحابلا Russo ةیبصعلا تاكبشلا جذومنل ؤبنتلا . ل نم ةقد رثكا ةیناسرخلا فئاتكلل صقلا لمحل ةناسرخلا ةنودم ةلداعم ةیكیرملاا ACI318-08 ثحابلا ةلداعمو Russo ةساردلا يف ةیبصعلا تاكبشلا جذومن مادختسا للاخ نم . ةنراقملا ، لثم ةفلتخملا تلاماعملا ریثأت ) , a, d, b As, Ah ( .تحضو دق ةحلسملا ةیناسرخلا فئاتكلا كولس ىلع جئاتنلا تنیب لا ةردق لا ةدیج ل ةیبصعلا تاكبشلا جذومن ب ؤبنتلل فئاتكلل صقلا كولس .ةحلسملا ةیناسرخلا صقلا ةمواقم ,ةحلسملا ةناسرخلا ,فئاتكلا ,ةیعانطصلاا ةیبصعلا تاكبشلا :ةلادلا تاملكلا Received: 17 – 3 2010 Accepted: 16 – 1 – 2011 ¢

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