Journals / Mathematical and Computational Applications / 2011 / Cilt: 16 - Sayı: 3

Determination of reducing coefficient values of semi-rigid frames using artificial neural network

Pages
649–658
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

A strong relationship was established between calculated and predicted values.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Lateral rigidity is a great issue in the view of structural analysis in civil engineering applications such as production of buildings, bridges, dams and roads.
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Key Findings & Data:In fact, there aren’t any structures with perfectly rigid connections between beams and columns.
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Conclusion & Impact:A strong relationship was established between calculated and predicted values.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Lateral rigidity is a great issue in the view of structural analysis in civil engineering applications such as production of buildings, bridges, dams and roads. Furthermore, the connection properties have a remarkable role on the lateral rigidity and the tendency of structures under service loads. In fact, there aren’t any structures with perfectly rigid connections between beams and columns. Therefore, all connections performed by construction process can be defined as semi-rigid as, especially connections in steel structures. The effect of connection type on the lateral rigidity values of structures can be determined by reducing coefficients decreasing lateral rigidity with a certain extent. In the scope of this study, connection flexibility is modeled by linear elastic rotational springs for semi-rigid frames. The reducing coefficients are determined by using a computer program. Results are compared to the values predicted by artificial neural network (ANN) analyses. A strong relationship was established between calculated and predicted values.

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Cite This Article

ŞEKER, S., ÖZTÜRK, A. U., KOZANOĞLU, C. (2011). Determination of reducing coefficient values of semi-rigid frames using artificial neural network. Mathematical and Computational Applications, Cilt: 16 - Sayı: 3, 649–658. https://ajindex.com/articles/280325

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