Surrogate modeling of structural seismic response using probabilistic learning on manifolds
K Zhong, JG Navarro, S Govindjee… - … & Structural Dynamics, 2023 - Wiley Online Library
Nonlinear response history analysis (NLRHA) is generally considered to be a reliable and
robust method to assess the seismic performance of buildings under strong ground motions …
robust method to assess the seismic performance of buildings under strong ground motions …
Hybrid surrogate model combining physics and data for seismic drift estimation of shear‐wall structures
To address the issue of costly computational expenditure related to high‐fidelity numerical
models, surrogate models have been widely used in various engineering tasks, including …
models, surrogate models have been widely used in various engineering tasks, including …
Estimation of first-passage probability under stochastic wind excitations by active-learning-based heteroscedastic Gaussian process
In the processes for designing and assessing structural systems, it is essential to evaluate
their reliability against stochastic loads caused by natural or human-made hazards, eg, wind …
their reliability against stochastic loads caused by natural or human-made hazards, eg, wind …
Earthquake data augmentation using wavelet transform for training deep learning based surrogate models of nonlinear structures
Abstract Machine learning methods have gained traction in the civil engineering community
for analysis of civil infrastructures. A major field of application is in development of surrogate …
for analysis of civil infrastructures. A major field of application is in development of surrogate …
Efficient seismic fragility analysis considering uncertainties in structural systems and ground motions
Fragility plays a pivotal role in performance‐based earthquake engineering, which
represents the seismic performance of structural systems. To comprehensively understand …
represents the seismic performance of structural systems. To comprehensively understand …
A multi-fidelity stochastic simulation scheme for estimation of small failure probabilities
Computing small failure probabilities is often of interest in the reliability analysis of
engineering systems. However, this task can be computationally demanding since many …
engineering systems. However, this task can be computationally demanding since many …
SVD enabled data augmentation for machine learning based surrogate modeling of non-linear structures
The computationally expensive estimation of engineering demand parameters (EDPs) via
finite element (FE) models, while considering earthquake and material parameter …
finite element (FE) models, while considering earthquake and material parameter …
Uncertainty quantification for seismic response using dimensionality reduction‐based stochastic simulator
This paper introduces a stochastic simulator for seismic uncertainty quantification, which is
crucial for performance‐based earthquake engineering. The proposed simulator extends the …
crucial for performance‐based earthquake engineering. The proposed simulator extends the …
[HTML][HTML] AI-based modeling and data-driven identification of moving load on continuous beams
H Zhang, Y Zhou - Fundamental Research, 2023 - Elsevier
Traffic load identification for bridges is of great significance for overloaded vehicle control as
well as the structural management and maintenance in bridge engineering. Unlike the …
well as the structural management and maintenance in bridge engineering. Unlike the …
The automated collapse data constructor technique and the data‐driven methodology for seismic collapse risk assessment
Majority of the past research on application of machine learning (ML) in earthquake
engineering focused on contrasting the predictive performance of different ML algorithms. In …
engineering focused on contrasting the predictive performance of different ML algorithms. In …