A design‐variable‐based inelastic hysteretic model for beam–column connections

Yun, Gun Jin; Ghaboussi, Jamshid; Elnashai, Amr S.

doi:10.1002/eqe.771

Cited by 17 publications

(4 citation statements)

References 12 publications

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“…For minor-axis connections, Anderson et al [47] used ANN to predict the bilinear moment-rotation response. Using neural networks, Yaun et al [48] proposed an inelastic hysteretic beam-to-column model based on design variables. For extended endplate connections and top-and-seat angles with double-web angles, their results showed that the proposed module reproduced experimental data with reasonable accuracy.…”

Section: Surrogate Modelsmentioning

confidence: 99%

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Analysis, Seismic Performance-based Assessment, and Design of Steel Endplate Connections and Buildings Equipped With Shape Memory Alloy (SMA) Bolts

Mohammadi Nia

2024

Preprint

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<p>Smart materials can be used in structures to avoid damage due to earthquakes. Shape Memory Alloys (SMA) are the most popular smart materials used in structural systems. Unlike steel material, SMAs can dissipate induced cyclic energy without noticeable strength degradation and residual deformations. Proper implementation of SMA materials in steel structures, for example, in plastic hinge regions, can improve the seismic response of structures, especially reducing the residual deformations significantly. A structure capable of returning to its plumb position following a seismic excitation is called a self-centering structure. This dissertation investigates self-centering extended endplate connections equipped with SMA bolts. To this end, 3D finite element models are developed and extensively validated by comparing the finite element results with the experimental results. The developed finite element model is then used to perform sensitivity analyses using the Design of Experiments method. Next, a backbone curve is proposed for the SMA-based connections, and the influential factors obtained from the sensitivity analyses are used in another design of experiments to develop a new database and, consequently, predictive equations for the backbone curve parameters. The generated database based on the significant factors is used to train Artificial Neural Networks (ANN) to develop more accurate predictive models while including the experimental test data. The trained ANNs are then used to develop a Graphical User Interface (GUI) for predicting the backbone curve of the connections. Additionally, the trained ANNs are used to conduct an optimization study to identify the optimal regions for the design parameters. Using the predictive tool, a phenomenological model for the SMA-based beam-to-column connections is developed in the OpenSees. The study illustrates the use of the predictive tool for accurate and efficient modeling of SMA-based connections and self-centering moment-resisting frames. The computation time for a typical SMA connection is significantly reduced from seven hours in ANSYS to only three minutes in OpenSees while providing the same level of prediction accuracy. Lastly, a performance-based seismic design is proposed. Two frames of 3- and 6-story height are designed using the proposed design framework to check the proposed design method against prescribed performance objectives.</p>

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Section: Surrogate Modelsmentioning

confidence: 99%

“…Anderson et al [47] applied ANN to predict the bilinear moment-rotation response for minor-axis connections. Yun et al[48] proposed a design-variable-based inelastic hysteretic model for beam-to-column connections using neural networks. Their results demonstrated that the proposed module could reproduce the experimental…”

mentioning

confidence: 99%

Analysis, Seismic Performance-based Assessment, and Design of Steel Endplate Connections and Buildings Equipped With Shape Memory Alloy (SMA) Bolts

Mohammadi Nia

2024

Preprint

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“…To model such behavior, a neural networks-based material model using a novel algorithmic tangent stiffness formulation was proposed. The adopted autoprogressive procedure was applied to a structure of hysteretic beam-column connections [141] and it was shown that the trained neural network model has a superior learning capability compared to the previous direct neural networks-based material models. Moreover, the neural network was shown to be able to successfully extract the local cyclic behavior from the global responses measured in synthetic and real experiments and is capable to generalize to unseen cyclic motions.…”

Section: Indirect Learningmentioning

confidence: 99%

Neural Networks for Constitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics

Dornheim,

Morand,

Nallani

et al. 2023

Arch Computat Methods Eng

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Analyzing and modeling the constitutive behavior of materials is a core area in materials sciences and a prerequisite for conducting numerical simulations in which the material behavior plays a central role. Constitutive models have been developed since the beginning of the 19th century and are still under constant development. Besides physics-motivated and phenomenological models, during the last decades, the field of constitutive modeling was enriched by the development of machine learning-based constitutive models, especially by using neural networks. The latter is the focus of the present review paper, which aims to give an overview of neural networks-based constitutive models from a methodical perspective. The review summarizes and compares numerous conceptually different neural networks-based approaches for constitutive modeling including neural networks used as universal function approximators, advanced neural network models and neural network approaches with integrated physical knowledge. The upcoming of these methods is in-turn closely related to advances in the area of computer sciences, what further adds a chronological aspect to this review. We conclude the review paper with important challenges in the field of learning constitutive relations that need to be tackled in the near future.

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“…Anderson et al (1997) applied ANN for predicting the bilinear moment-rotation response for minor-axis connections. Yun et al (2008) proposed a design-variable-based inelastic hysteretic model for beam-to-column connections using neural networks. Their results demonstrated that the proposed module could reproduce the experimental data with reasonable accuracy for extended endplate connections and top-and-seat angles with doubleweb angle connections.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network–Based Predictive Tool for Modeling of Self-Centering Endplate Connections with SMA Bolts

Nia

Moradi

2022

J. Struct. Eng.

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Endplate moment connections with shape memory alloy (SMA) bolts provide self-centering for the seismic resilience of structures. Predicting the self-centering response of these new beam-column connections, which have not been codified yet, requires conducting experimental tests or detailed continuum finite-element simulations. Computationally efficient predictive tools are needed to facilitate the analysis, design, and assessment of self-centering connections and moment frames. In this paper, artificial neural networks (ANNs) are used to develop a MATLAB tool for predicting the moment-rotation backbone and self-centering response of extended endplate connections with SMA bolts. As the input for the neural networks, the predictive model development employs a design database of response parameters from 72 finite-element (FE) models and experimental tests of seven beam-column connection specimens. Neural networks are trained for seven response parameters, and the trained networks are used to develop a graphical user interface (GUI). The coefficient of determination for the trained ANNs is in the range of 0.92 to 0.99, indicating acceptable prediction accuracy. Furthermore, optimization studies using a multiobjective genetic algorithm are performed, seeking the minimization of material use (steel and SMA) and improved connection-response characteristics (i.e., stiffness, strength, and ductility). A phenomenological model of SMA connections is also developed in OpenSees. The use of the ANN-based predictive tool for accurate and efficient modeling of SMA-based connections and self-centering moment-resisting frames is illustrated. The computation time for predicting the moment-rotation response of a typical SMA connection is significantly reduced from seven hours in ANSYS to only three minutes in OpenSees while providing the same level of response prediction accuracy. Furthermore, the optimization results are confirmed by performing nonlinear pushover and response history analyses.

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A design‐variable‐based inelastic hysteretic model for beam–column connections

Cited by 17 publications

References 12 publications

Analysis, Seismic Performance-based Assessment, and Design of Steel Endplate Connections and Buildings Equipped With Shape Memory Alloy (SMA) Bolts

Analysis, Seismic Performance-based Assessment, and Design of Steel Endplate Connections and Buildings Equipped With Shape Memory Alloy (SMA) Bolts

Neural Networks for Constitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics

Artificial Neural Network–Based Predictive Tool for Modeling of Self-Centering Endplate Connections with SMA Bolts

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