Aims and Scope
Investigation of Steel Frames Equipped with Steel Eccentric Braces and Steel-Concrete Buckling-Restrained Braces Having Moment LinkAlireza Bahrami, Mahmood Heidari
Different bracing systems of steel Eccentric Braces (EBs) and steel-concrete Buckling-Restrained Braces (BRBs) can be used in steel frames in order to make the frames stronger in resisting lateral loads. These steel frames with EBs or BRBs are generally called Eccentrically Braced Frames (EBFs) or Buckling-Restrained Braced Frames (BRBFs), respectively.
This study aims to investigate steel frames with bracing systems of steel EBs and steel-concrete BRBs having moment link.
The EBFs and BRBFs are nonlinearly analysed employing the finite element software ABAQUS. Experimental tests of the EBF and BRB are utilised for the validation of their modelling. The modelling is validated by comparing the modelling results with experimental tests results. Then, an EBF and a BRBF are designed having moment link. The extreme earthquake records of Tabas, Chi-Chi, and Northridge are selected for the dynamic analyses of the EBF and BRBF. The validated modelling method is applied to analyse the designed EBF and BRBF under the selected earthquake records.
The achieved results from the analyses are lateral displacements, base shears, and energy dissipations of the EBF and BRBF and moment link rotations. These results are compared and discussed.
It is concluded that the hierarchy of the lateral displacements of the analysed EBF and BRBF, having moment link, is related to the Tabas, Chi-Chi, and Northridge records because the lateral displacements of the frames are directly proportional to the peak ground accelerations of the records, and there is the same hierarchy for the records in terms of their peak ground accelerations. Lower lateral displacements are witnessed for the BRBF than the EBF subjected to the Tabas and Chi-Chi records. However, larger lateral displacement is observed for the BRBF than the EBF under the Northridge record. The same procedure as the lateral displacements is also revealed for the effectiveness of the BRBF with regard to its link rotations compared with the EBF. Moreover, the BRBF improves the base shear capacities and energy dissipations of the frame compared with the EBF. Consequently, the BRBF is generally demonstrated to be superior to the EBF from the structural performance point of view. Thus, the BRBF can be used more efficiently in structures subjected to large lateral loads compared with the EBF.
May 06, 2021
In this study , a methodology based on non-destructive tests was used to characterize historical masonry and later to obtain information regarding the mechanical parameters of these elements. Due to the historical and cultural value that these buildings represent, the maintenance and rehabilitation work are important to maintain the appreciation of history. The preservation of buildings classified as historical-cultural heritage is of social interest, since they are important to the history of society. Considering the research object as a historical building, it is not recommended to use destructive investigative techniques.
This work contributes to the technical-scientific knowledge regarding the characterization of granite masonry based on geophysical, mechanical and neural networks techniques.
The database was built using the GPR (Ground Penetrating Radar) method, sonic and dynamic tests, for the characterization of eight stone masonry walls constructed in a controlled environment. The mechanical characterization was performed with conventional tests of resistance to uniaxial compression, and the elastic modulus was the parameter used as output data of ANNs.
For the construction and selection of network architecture, some possible combinations of input data were defined, with variations in the number of hidden layer neurons (5, 10, 15, 20, 25 and 30 nodes), with 122 trained networks.
A mechanical characterization tool was developed applying the Artificial Neural Networks (ANN), which may be used in historic granite walls. From all the trained ANNs, based on the errors attributed to the estimated elastic modulus, networks with acceptable errors were selected.
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