2011•Advanced materials researchOpen access

Predicted Behaviour of Partially Restrained Connection with Cold Formed High Strength Steel by 3D Finite Element Modelling

S. Taufik, Shahrizan Baharom, Robert Y. Xiao

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Abstract

Thispaper investigates the behavior prediction of partially restrained (PR) connection with high strength steel bythree-dimensional nonlinear finite-element (FE) analyses. The connectionmodel is such that angle cleats are represented by radiuses corner section shell elements. The full interaction between angle and beam and/or column is simulated by contact element. The analysis results of the moment- rotation relationship and behaviour characteristic of the connection with high strength steel are compared and discussed. It is found that contact element and strength enhancement of the corner regions employed to the model are very important parameters for accurate prediction of PR connection behaviour with cold-formed high strength steel. The moment capacity prediction of top and seat angle connections based on EC3 has been shown to be reasonable compared with FE modeling. Theproposed connection FE model is capable of predicting the ultimate load capacity and the plastic strain pattern with good accuracy. The model presented gives excellent results for increasing the connection capacity significantly due to employed higher strength steel section.

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What this paper is about

Thispaper investigates the behavior prediction of partially restrained (PR) connection with high strength steel bythree-dimensional nonlinear finite-element (FE) analyses. The connectionmodel is such that angle cleats are represented by radiuses corner section shell elements. The full interaction between angle and beam and/or column is simulated by contact element. The analysis results of the moment- rotation relationship and behaviour characteristic of the connection with high strength steel are compared and discussed. It is found that contact element and strength enhancement of the corner regions employed to the model are very important parameters for accurate prediction of PR connection behaviour with cold-formed high strength steel. The moment capacity prediction of top and seat angle connections based on EC3 has been shown to be reasonable compared with FE modeling. Theproposed connection FE model is capable of predicting the ultimate load capacity and the plastic strain pattern with good accuracy. The model presented gives excellent results for increasing the connection capacity significantly due to employed higher strength steel section.

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Available abstract

Thispaper investigates the behavior prediction of partially restrained (PR) connection with high strength steel bythree-dimensional nonlinear finite-element (FE) analyses. The connectionmodel is such that angle cleats are represented by radiuses corner section shell elements. The full interaction between angle and beam and/or column is simulated by contact element. The analysis results of the moment- rotation relationship and behaviour characteristic of the connection with high strength steel are compared and discussed. It is found that contact element and strength enhancement of the corner regions employed to the model are very important parameters for accurate prediction of PR connection behaviour with cold-formed high strength steel. The moment capacity prediction of top and seat angle connections based on EC3 has been shown to be reasonable compared with FE modeling. Theproposed connection FE model is capable of predicting the ultimate load capacity and the plastic strain pattern with good accuracy. The model presented gives excellent results for increasing the connection capacity significantly due to employed higher strength steel section.

Key concepts: Connection (principal bundle), Structural engineering, Finite element method, Moment (physics), Nonlinear system, Materials science, Cold-formed steel, Shell (structure)

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Predicted Behaviour of Partially Restrained Connection with Cold Formed High Strength Steel by 3D Finite Element Modelling — Research Paper | ScholarLens