Related references
Note: Only part of the references are listed.
Article
Computer Science, Software Engineering
Danilo S. Kusanovic et al.
Summary: We introduce Seismo-VLAB (SVL), a new open-source, object-oriented finite element software designed to optimize meso-scale simulations in structural and geotechnical engineering. SVL includes state-of-the-art tools and parallel computing capabilities for efficient modeling of soil-structure interaction and wave propagation in heterogeneous half-spaces, such as perfectly matched layer, domain reduction method, dynamic nonlinear solvers, cutting edge parallel linear system solvers, domain decomposition method, and various plasticity models. This work presents the numerical implementation and software structure, allowing enthusiastic developers to contribute to this open-source project and showcases the software capabilities using an illustrative example.
Article
Automation & Control Systems
Lin Li et al.
Summary: This study proposes deep learning models to predict soil seismic response based on recorded ground motions. These models achieve better accuracy and higher efficiency compared to conventional physics-based models like finite element method (FEM).
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Geological
Yongxin Wu et al.
Summary: A deep learning approach based on the gated recurrent neural network (GRU) is proposed to predict the seismic responses of underground structures in single-layered soil and multi-layered soil. The GRU network achieved satisfactory prediction performances on the acceleration responses of underground structures in various soil conditions. It showed better prediction performance in a tunnel located in a homogeneous sand layer than in a clay layer.
SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Yuchen Liao et al.
Summary: This paper proposes an innovative attention-based recurrent neural network model for predicting bridge responses under dynamic loads such as earthquakes. The model utilizes the recent advances in deep learning and demonstrates improved accuracy and reliability compared to traditional models through validation with numerical and real-world bridge data.
COMPUTERS & STRUCTURES
(2023)
Article
Engineering, Civil
Zekun Xu et al.
Summary: This paper proposed a recursive LSTM network for predicting nonlinear structural seismic responses with lower computational cost compared to traditional methods, demonstrating good accuracy and generalization capability.
ENGINEERING STRUCTURES
(2022)
Article
Engineering, Mechanical
Anirban Kundu et al.
Summary: This study proposes a LSTM-based deep learning algorithm for quantifying seismic response uncertainty, addressing both the stochastic nature of dynamic load and structural system parameter uncertainty. The algorithm demonstrates enhanced prediction capability in terms of accuracy matrices, regression analysis, seismic response statistics, and reliability results.
PROBABILISTIC ENGINEERING MECHANICS
(2022)
Article
Engineering, Civil
Kien T. Nguyen et al.
Summary: This paper proposes a methodology for analyzing three-dimensional nonlinear soil-structure interaction problems with Rayleigh wave incidence in horizontally layered soils. The methodology involves calculating the free-field displacement histories and effective input forces using thin-layer method and domain reduction method respectively, and then simulating the structure and its vicinity responses using finite element analysis. The results show good agreement with published research on SSI problems with boundary elements and on layered media problems using propagator matrix technique and thin-layer method. The capability of the methodology is demonstrated through a nonlinear analysis of a 12-story building on a layered soil overlaying a homogeneous halfspace.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
(2022)
Article
Engineering, Civil
Pengfei Huang et al.
Summary: A novel method for predicting the nonlinear seismic response of subway stations using deep learning approaches has been developed, with 1D-CNN and LSTM networks showing better prediction performance compared to baseline MLP model. The study demonstrates the potential of applying deep learning methods for reducing computational cost in seismic responses analysis.
ENGINEERING STRUCTURES
(2021)
Article
Engineering, Geological
Qiangqiang Sun et al.
Summary: This study investigates the feasibility of using surrogate models for global sensitivity analysis in seismic soil-tunnel systems, validating the accuracy and efficiency of the method. Parametric sensitivity analysis reveals that soil shear wave velocity and modulus reduction factor are key variables influencing seismic deformations in tunnels.
SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Ruiyang Zhang et al.
COMPUTERS & STRUCTURES
(2019)
Article
Computer Science, Interdisciplinary Applications
W. Zhang et al.
COMPUTERS AND GEOTECHNICS
(2019)
Article
Geochemistry & Geophysics
Jian Shi et al.
SEISMOLOGICAL RESEARCH LETTERS
(2018)
Article
Engineering, Geological
Tae-Hyung Lee et al.
BULLETIN OF EARTHQUAKE ENGINEERING
(2016)
Article
Engineering, Multidisciplinary
S. Kucukcoban et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2011)
Review
Engineering, Industrial
Bruno Sudret
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2008)
Article
Geochemistry & Geophysics
J Bielak et al.
BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA
(2003)
Review
Construction & Building Technology
YMA Hashash et al.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2001)