PREDICTION OF THE BEHAVIOUR OF EXTERIOR REINFORCED CONCRETE JOINTS USING ARTIFICIAL NEURAL NETWORKS

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MOSTOUFINEZHAD D.; SOBHANI J.

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Journal: AMIRKABIR Year:2004 | Volume:15 | Issue:58-D Page(s): 535-546

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Information Journal Paper

Title

PREDICTION OF THE BEHAVIOUR OF EXTERIOR REINFORCED CONCRETE JOINTS USING ARTIFICIAL NEURAL NETWORKS

Author(s)

MOSTOUFINEZHAD D. | SOBHANI J. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKSQ2

EXTERIOR REINFORCED CONCRETE JOINTQ4

MOMENT-ROTATION CURVEQ4

Abstract

This paper describes how to predict moment-rotation behaviour, ultimate resistance moment, ultimate rotation and performance of joints using ARTIFICIAL NEURAL NETWORKS with back-propagation algorithm. To produce the input data for neural networks, 432 exterior reinforced concrete beam- column joints were non-linearly analyzed. The variables of design in elude the dimensions of beams and columns, longitudinal reinforcement of beams and columns, different arrangement for transverse reinforcement, and different amounts for transverse reinforcement in beams and columns. Two neural network systems have been trained one for predicting the rotation of joints, and the other for ultimate rotation, ultimate resistant moment, and performance of joints. At the end, a method proposed for prediction of MOMENT-ROTATION CURVEs of reinforced concrete connections.

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APA: Copy

MOSTOUFINEZHAD, D., & SOBHANI, J.. (2004). PREDICTION OF THE BEHAVIOUR OF EXTERIOR REINFORCED CONCRETE JOINTS USING ARTIFICIAL NEURAL NETWORKS. AMIRKABIR, 15(58-D), 535-546. SID. https://sid.ir/paper/849/en

Vancouver: Copy

MOSTOUFINEZHAD D., SOBHANI J.. PREDICTION OF THE BEHAVIOUR OF EXTERIOR REINFORCED CONCRETE JOINTS USING ARTIFICIAL NEURAL NETWORKS. AMIRKABIR[Internet]. 2004;15(58-D):535-546. Available from: https://sid.ir/paper/849/en

IEEE: Copy

D. MOSTOUFINEZHAD, and J. SOBHANI, “PREDICTION OF THE BEHAVIOUR OF EXTERIOR REINFORCED CONCRETE JOINTS USING ARTIFICIAL NEURAL NETWORKS,” AMIRKABIR, vol. 15, no. 58-D, pp. 535–546, 2004, [Online]. Available: https://sid.ir/paper/849/en

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