Experimental and numerical analysis of beam to column joints in steel structures

Abdollahzadeh, Gholamreza; Shabanian, Seyed Mostafa

doi:10.1007/s11709-017-0457-z

Cited by 12 publications

(6 citation statements)

References 35 publications

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“…As observed in Figure , in regressive mode, predicted output by learned neural network models is used for calculating of input values in the next stage. Therefore, existing inputs including hysteresis parameters and previous sections are estimated based on force displacement with neural network output in the previous step …”

Section: Numerical Analysismentioning

confidence: 99%

“…Therefore, existing inputs including hysteresis parameters and previous sections are estimated based on force displacement with neural network output in the previous step. [36][37][38][39] 3 | EXPERIMENTAL PHASE Various methods and practical experiments have been conducted by many researchers in different fields to study baseplate behaviors and different components and effective factors about this connection in static loading state. Hence, baseplates are mediated between structure and foundation and transmit dynamic impact of earthquake to the structure.…”

Section: The Neural Network Modelmentioning

confidence: 99%

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Experimental and numerical evaluation of rigid column to baseplate connection under cyclic loading

Abdolahzadeh

Shabanian

Tavakol

2019

Structural Design Tall Build

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Summary One of the most important connections in steel structures is column to the baseplate connection. This kind of connection has a complex nature, due to different behaviors of the constituents, including baseplate, grout, foundation, and anchor bolts. Studying the impact of this connection in general behavior of structure seems to be an essential issue, especially during earthquake, which is more likely to be associated with plastic hinge forming. The main goal of this work is to provide a hybrid modeling for describing hysteresis behavior of column to the baseplate connection. Within hybrid modeling, a mechanical model is accomplished by using neural network model components. Our findings reveal that hybrid models are able to show pinched hysteresis complex behavior of baseplate connections. Also, the proposed hybrid developed model is robust and reliable approaches for predicting behaviors of a recent designed connection.

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Section: Numerical Analysismentioning

confidence: 99%

Section: The Neural Network Modelmentioning

confidence: 99%

Experimental and numerical evaluation of rigid column to baseplate connection under cyclic loading

Abdolahzadeh

Shabanian

Tavakol

2019

Structural Design Tall Build

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“…Many studies have been conducted to assess the seismic performance of steel SMFs [7][8][9][10][11][12][13][14][15][16][17][18]. To obtain the reliable results of seismic performance assessment for steel SMFs, the cyclic behavior of SMF components has been investigated through experimental and numerical studies [19,20]. However, only a limited number of studies have been conducted for steel IMFs, OMFs, and existing moment frames [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Effect of Wind Loads on Collapse Performance and Seismic Loss for Steel Ordinary Moment Frames

2022

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The aim of this study is to investigate the effect of wind loads on the seismic collapse performance and seismic loss for steel ordinary moment frames (OMFs). For this purpose, 9-, 12-, 15-, and 18-story steel OMFs are repeatedly designed for (1) gravity load + seismic load, (2) gravity load + seismic load + wind load (wind speed = 44 m/s), and (3) gravity load + seismic load + wind load (wind speed = 55 m/s). The seismic collapse performance and seismic loss of OMFs are evaluated using the procedures in FEMA P695 (FEMA, 2009) and FEMA P58 (FEMA, 2018), respectively. Steel OMFs designed with consideration of wind loads have larger member sections than corresponding steel OMFs designed without consideration of wind loads as expected. Although member sections are increased when wind loads are considered, the growth in the maximum base shear force and lateral stiffness of OMFs are insignificant. Unlike our expectation, OMFs designed with consideration of wind loads have higher expected annual loss (EAL) than corresponding OMFs designed without consideration of wind loads.

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“…However, the inability of traditional statistical methods to handle missing or noisy data, as well as to manage nonlinearities and to identify certain behavior patterns, opens up space to the use of innovative computer-based solutions. In the last couple of years, the use of Artificial Neural Networks (ANNs) is becoming increasingly popular in many civil engineering applications [4][5][6], namely in the field of earthquake engineering [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Traditionally, ANNs are used as "black boxes" to obtain a problem solution without a clear understanding about the mathematical relations between the inputs and the outputs, which are often considered as being a handicap for engineering purposes.…”

Section: Introductionmentioning

confidence: 99%

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry

Ferreira

Estêvão

Maio

et al. 2020

Front. Struct. Civ. Eng.

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This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage, not with the goal of replacing existing approaches, but as a mean to improve the precision of empirical methods. For such, damage data collected in the aftermath of the 1998 Azores earthquake (Portugal) is used to develop a comparative analysis between damage grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial Neural Networks (ANNs). The analysis is carried out on the basis of a vulnerability index computed with a hybrid seismic vulnerability assessment methodology, which is subsequently used as input to both approaches. The results obtained are then compared with real postearthquake damage observation and critically discussed taking into account the level of adjustment achieved by each approach. Finally, a computer routine that uses the ANN as an approximation function is develop and applied to derive a new vulnerability curve expression. In general terms, the ANN developed in this study allowed to obtain much better approximations than those achieved with the original vulnerability approach, which has revealed to be quite non-conservative. Similarly, the proposed vulnerability curve expression was found to provide a more accurate damage prediction than the traditional analytical expressions.

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Experimental and numerical analysis of beam to column joints in steel structures

Cited by 12 publications

References 35 publications

Experimental and numerical evaluation of rigid column to baseplate connection under cyclic loading

Experimental and numerical evaluation of rigid column to baseplate connection under cyclic loading

Effect of Wind Loads on Collapse Performance and Seismic Loss for Steel Ordinary Moment Frames

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry

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