☆ 4.7 Article

Attention-based LSTM (AttLSTM) neural network for Seismic Response Modeling of Bridges

COMPUTERS & STRUCTURES (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Engineering, Marine

Joint damage detection of structures with noisy data by an effective deep learning framework using autoencoder-convolutional gated recurrent unit

Tam T. Truong et al.

Summary: This paper introduces a new deep learning framework for structural joint damage detection using noisy data. By removing noise and automatically extracting important features, this method can predict the location and severity of damaged joints in structures. Through validation and comparison experiments, the accuracy and reliability of the method are demonstrated, and the influence of different parameters is investigated.

OCEAN ENGINEERING (2022)

添加到收藏夹

Article Engineering, Mechanical

A long short-term memory based deep learning algorithm for seismic response uncertainty quantification

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)

添加到收藏夹

Article Engineering, Marine

An effective framework for real-time structural damage detection using one-dimensional convolutional gated recurrent unit neural network and high performance computing

Tam T. Truong et al.

Summary: This paper introduces an efficient one-dimensional convolutional gated recurrent unit neural network (1D-CGRU) for real-time structural damage detection by combining a one-dimensional convolutional neural network (1D-CNN) and a gated recurrent unit neural network (GRU). By leveraging feature extraction in spatial and temporal relations, the proposed framework shows promising performance and efficiency for low-cost structural damage detection.

OCEAN ENGINEERING (2022)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Real-time regional seismic damage assessment framework based on long short-term memory neural network

Yongjia Xu et al.

Summary: This study proposes a framework for real-time regional seismic damage assessment based on the LSTM neural network architecture, which can rapidly and accurately assess earthquake-induced damage. By establishing a workflow and optimizing model utilization, damage prediction can be performed at a regional scale.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2021)

添加到收藏夹

Article Engineering, Civil

Experimental and numerical investigations on seismic performance of RC bridge piers considering buckling and low-cycle fatigue of high-strength steel bars

Yang Ding et al.

Summary: This study investigated the mechanical properties of high-strength steel bars (HSSB) and the seismic performance of high-strength RC piers. It was found that HTRB600 steel has lower ductility and inferior low-cycle fatigue performance compared to HRB400E steel. Increasing the slenderness ratio weakens the low-cycle fatigue life of steel bars. Additionally, the ultimate displacement of RC piers with C80 concrete is significantly lower than that with C40 and C60 concrete. Increasing the axial load ratio reduces the ultimate displacement of RC piers.

ENGINEERING STRUCTURES (2021)

添加到收藏夹

Article Engineering, Civil

Deep learning for nonlinear seismic responses prediction of subway station

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)

添加到收藏夹

Article Construction & Building Technology

Geometric Attention Regularization Enhancing Convolutional Neural Networks for Bridge Rubber Bearing Damage Assessment

Mida Cui et al.

Summary: This research proposes a geometric attention regularization (GAR) method to enhance the performance of CNN for the condition evaluation of rubber bearings. By utilizing the location information of the bearings to enhance the loss function of CNN, the method aims to improve performance and mitigate overfitting.

JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES (2021)

添加到收藏夹

Article Engineering, Civil

DynNet: Physics-based neural architecture design for nonlinear structural response modeling and prediction

Soheil Sadeghi Eshkevari et al.

Summary: A physics-based recurrent neural network model is proposed in this study to accurately estimate dynamic responses of linear and nonlinear multi-degree-of-freedom systems. Compared with other models, this model has higher accuracy and requires fewer trainable variables. Numerical case studies demonstrate the network's ability to learn different nonlinear behaviors of dynamic systems with high accuracy.

ENGINEERING STRUCTURES (2021)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

Han Gao et al.

Summary: The paper introduces a novel physics-constrained CNN learning architecture to learn solutions of parametric PDEs on irregular domains without labeled data. Elliptic coordinate mapping is used to enable coordinate transforms between irregular physical domain and regular reference domain. The proposed method has been assessed by solving a number of steady-state PDEs on irregular domains and has shown notable superiority over the FC-NN based PINN in terms of efficiency and accuracy.

JOURNAL OF COMPUTATIONAL PHYSICS (2021)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Multi-objective optimization of multi-directional functionally graded beams using an effective deep feedforward neural network-SMPSO algorithm

Tam T. Truong et al.

Summary: This paper introduces an intelligent multi-objective optimization approach using DNN and SMPSO, which replaces numerical models with a high-accuracy DNN and intelligent sampling technique, to accurately search for Pareto optimal solutions. By considering the ceramic volume fraction values at control points as design variables and input parameters of the DNN model, the method demonstrates accuracy, efficiency, and applicability in solving multi-objective optimization problems of 2D-FG beams.

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION (2021)

添加到收藏夹

Article Engineering, Civil

Time series estimation based on deep Learning for structural dynamic nonlinear prediction

Hong Peng et al.

Summary: This paper explores the use of deep learning techniques to analyze and predict structural dynamic nonlinear behaviors in civil engineering applications. Different methods are compared, with the LSTMNN method based on time series estimation model showing superior performance and robust anti-noise capabilities. The study demonstrates the feasibility of applying deep learning methods in analyzing structural behaviors in civil engineering.

STRUCTURES (2021)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

A multi-scale attention neural network for sensor location selection and nonlinear structural seismic response prediction

Teng Li et al.

Summary: This paper proposes a four-stage practical solution for predicting seismic displacement responses of civil structures using acceleration measurements at optimized sensor locations. A novel multi-scale attention-based recurrent neural network is trained to focus on relevant input data and a floor displacement loss function is designed to measure the prediction accuracy. Performance evaluation on numerical and real-world data shows that the proposed methodology outperforms state-of-the-art methods in prediction accuracy and reliability.

COMPUTERS & STRUCTURES (2021)

添加到收藏夹

Article Engineering, Civil

A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced composite plates using modal kinetic energy

Huy Q. Le et al.

Summary: This study proposes a new Deep Feed-forward Neural Network (DFNN) approach for damage detection in functionally graded carbon nanotube-reinforced composite plates. The DFNN model, developed based on a dataset of 20,000 samples obtained via finite element simulation, utilizes elemental modal kinetic energy values for input to detect damages accurately. The model demonstrates high performance in detecting damages under different configurations of CNT fibers.

FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING (2021)

添加到收藏夹

Article Acoustics

Neural network-based seismic response prediction model for building structures using artificial earthquakes

Byung Kwan Oh et al.

JOURNAL OF SOUND AND VIBRATION (2020)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Prediction of aeroelastic response of bridge decks using artificial neural networks

Tajammal Abbas et al.

COMPUTERS & STRUCTURES (2020)

添加到收藏夹

Article Engineering, Civil

Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling

Ruiyang Zhang et al.

ENGINEERING STRUCTURES (2020)

添加到收藏夹

Article Engineering, Multidisciplinary

Physics-informed multi-LSTM networks for metamodeling of nonlinear structures

Ruiyang Zhang et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)

添加到收藏夹

Article Construction & Building Technology

Bayesian dynamic forecasting of structural strain response using structural health monitoring data

Y. W. Wang et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2020)

添加到收藏夹

Article Engineering, Civil

Seismic Vulnerability Analysis of RC Bridges Based on Kriging Model

Yuye Zhang et al.

JOURNAL OF EARTHQUAKE ENGINEERING (2019)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

M. Raissi et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism

Yong Li et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

EA-LSTM: Evolutionary attention-based LSTM for time series prediction

Youru Li et al.

KNOWLEDGE-BASED SYSTEMS (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Bidirectional LSTM with attention mechanism and convolutional layer for text classification

Gang Liu et al.

NEUROCOMPUTING (2019)

添加到收藏夹

Article Engineering, Multidisciplinary

Prediction of blast induced ground vibration (BIGV) of quarry mining using hybrid genetic algorithm optimized artificial neural network

Yousef Azimi et al.

MEASUREMENT (2019)

添加到收藏夹

Article Construction & Building Technology

Dynamic wind response of tall buildings using artificial neural network

T. J. Nikose et al.

STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS (2019)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Deep long short-term memory networks for nonlinear structural seismic response prediction

Ruiyang Zhang et al.

COMPUTERS & STRUCTURES (2019)

添加到收藏夹

Article Engineering, Civil

Modeling and forecasting of temperature-induced strain of a long-span bridge using an improved Bayesian dynamic linear model

Hao Wang et al.

ENGINEERING STRUCTURES (2019)

添加到收藏夹

Article Engineering, Mechanical

Deep Convolutional Neural Network for Structural Dynamic Response Estimation and System Identification

Rih-Teng Wu et al.

JOURNAL OF ENGINEERING MECHANICS (2019)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Least-Square-Support-Vector-Machine-based approach to obtain displacement from measured acceleration

Jale Tezcan et al.

ADVANCES IN ENGINEERING SOFTWARE (2018)

添加到收藏夹

Article Construction & Building Technology

Maintenance Cost Optimization for Bridge Structures Using System Reliability Analysis and Genetic Algorithms

Farzad Ghodoosi et al.

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT (2018)

添加到收藏夹

Article Engineering, Civil

Data-driven modeling of vortex-induced vibration of a long-span suspension bridge using decision tree learning and support vector regression

Shanwu Li et al.

JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS (2018)

添加到收藏夹

Article Engineering, Geological

A new baseline correction method for near-fault strong-motion records based on the target final displacement

Yuanzheng Lin et al.

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING (2018)

添加到收藏夹

Article Construction & Building Technology

Maintenance Cost Optimization for Bridge Structures Using System Reliability Analysis and Genetic Algorithms

Farzad Ghodoosi et al.

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT (2018)

添加到收藏夹

Article Construction & Building Technology

An improved substructural damage detection approach of shear structure based on ARMAX model residual

Liu Mei et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2016)

添加到收藏夹

Article Engineering, Geological

Nonlinear dynamic analysis of Melo land Road Overpass using three-dimensional continuum modeling approach

Amin Rahmani et al.

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING (2014)

添加到收藏夹

Article Engineering, Civil

Prediction of seismic-induced structural damage using artificial neural networks

Oliver Richard de Lautour et al.

ENGINEERING STRUCTURES (2009)

添加到收藏夹

Article Construction & Building Technology

Benchmark structural control problem for a seismically excited highway bridge-Part I: Phase I Problem definition

Anil Agrawal et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2009)

添加到收藏夹

Article Engineering, Civil

NGA project strong-motion database

Brian Chiou et al.

EARTHQUAKE SPECTRA (2008)

添加到收藏夹

Article Construction & Building Technology

Seismic analysis of meloland road overcrossing using multiplatform simulation software including SSI

Oh-Sung Kwon et al.

JOURNAL OF STRUCTURAL ENGINEERING-ASCE (2008)

添加到收藏夹

Article Construction & Building Technology

Seismic performance of segmental precast unbonded posttensioned concrete bridge columns

Yu-Chen Ou et al.

JOURNAL OF STRUCTURAL ENGINEERING (2007)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Underground blast induced ground shock and its modelling using artificial neural network

Y Lu

COMPUTERS AND GEOTECHNICS (2005)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Quick seismic response estimation of prestressed concrete bridges using artificial neural networks

CH Jeng et al.

JOURNAL OF COMPUTING IN CIVIL ENGINEERING (2004)

添加到收藏夹

Article Construction & Building Technology

Analysis of recent bridge failures in the United States

K Wardhana et al.

JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES (2003)

添加到收藏夹

Article Construction & Building Technology

Seismic performance of rectangular hollow bridge columns

YK Yeh et al.

JOURNAL OF STRUCTURAL ENGINEERING-ASCE (2002)

添加到收藏夹

Article Instruments & Instrumentation

Damage diagnosis using time series analysis of vibration signals

H Sohn et al.

SMART MATERIALS & STRUCTURES (2001)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.