Multi-storey shear type buildings under earthquake loading: Adversarial learning-based prediction of the transient dynamics and damage classification

Gatti, Filippo; Rosafalco, Luca; Colombera, Giorgia; Mariani, Stefano; Corigliano, Alberto

doi:10.1016/j.soildyn.2023.108141

Cited by 8 publications

(2 citation statements)

References 66 publications

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“…The network input is defined as the predictor data. Each fully connected layer handles the input data by means of a weight matrix and a bias vector; an activation function (e.g., the rectified linear unit, hyperbolic tangent, sigmoid function, and linear function) can provide nonlinear transformations of the information/data, see [24][25][26]. A backpropagation algorithm is adopted to tune the weights of the RANN, managing a loss function (as a prediction error between the input and the output) to be minimized with the stochastic gradient descent algorithm.…”

Section: Network Configurationmentioning

confidence: 99%

A Parsimonious Yet Robust Regression Model for Predicting Limited Structural Responses of Remote Sensing

Entezami,

Behkamal,

De Michele

et al. 2023

Ecsa 2023

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Section: Network Configurationmentioning

confidence: 99%

A Parsimonious Yet Robust Regression Model for Predicting Limited Structural Responses of Remote Sensing

Entezami,

Behkamal,

De Michele

et al. 2023

Ecsa 2023

Self Cite

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show abstract

“…Modal analysis is a vital tool used to identify the dynamic behavior of structures in terms of natural frequencies, mode shapes, and damping ratios [1][2][3]. By understanding how the structures respond to the external forces, it can be ensured that they withstand such loads as well as the relevant environmental conditions [4]. Modal analysis serves as a crucial tool used in model order reduction techniques [5,6] and structural health monitoring (SHM) [7].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Structural Identification Using an Enhanced Phase-Based Method

Azizi,

Karami,

Mariani

2023

Ecsa 2023

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Time history seismic response prediction of multiple homogeneous building structures using only one deep learning‐based Structure Temporal Fusion Network

Li,

Yang,

Deng

et al. 2024

Earthq Engng Struct Dyn

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Structural response prediction under earthquakes is crucial for evaluating the structural performance and subsequent functional restoration. Deep learning provides the potential to rapidly obtain the responses by skipping the time‐consuming nonlinear finite element analysis. However, a single deep learning network may only predict the time history responses of one specific structure, resulting in redundancy and resource waste when building multiple networks for modeling different structures. Thus, this study proposes a Structure Temporal Fusion Network (STFN) that can predict responses of various homogeneous structures using a single network. The key concept is that the seismic waves and the structural characteristics, such as story numbers, are fused together to predict diverse time history responses. Two numeric experiments are conducted, including predicting responses of ideal single‐degree‐of‐freedom (SDOF) structures and regular multistory reinforced concrete frames. Furthermore, a series of ablation analyses are carried out to validate the network architecture. The results indicate that STFN can predict nonlinear time history responses of different structures with mean square errors in the magnitude of and for two experiments, respectively. The solutions also highlight the importance of fusing static characteristics for the modeling of various structures with only one network. The STFN presents a promising solution for time history response prediction across multiple structures in regions.

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Multi-storey shear type buildings under earthquake loading: Adversarial learning-based prediction of the transient dynamics and damage classification

Cited by 8 publications

References 66 publications

A Parsimonious Yet Robust Regression Model for Predicting Limited Structural Responses of Remote Sensing

A Parsimonious Yet Robust Regression Model for Predicting Limited Structural Responses of Remote Sensing

Vision-Based Structural Identification Using an Enhanced Phase-Based Method

Time history seismic response prediction of multiple homogeneous building structures using only one deep learning‐based Structure Temporal Fusion Network

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