Time series estimation based on deep Learning for structural dynamic nonlinear prediction
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Time series estimation based on deep Learning for structural dynamic nonlinear prediction
Authors
Keywords
Auto-regression model, Time series estimation model, Piecewise linear least squares (PLLS) method, Fully-connected neural network (FCNN) method, Long short term memory neural network (LSTMNN), Deep learning, Artificial intelligence
Journal
Structures
Volume 29, Issue -, Pages 1016-1031
Publisher
Elsevier BV
Online
2020-12-18
DOI
10.1016/j.istruc.2020.11.049
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure
- (2020) Tongwei Liu et al. SENSORS
- A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications
- (2020) Onur Avci et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- An accurate and efficient algorithm for the simulation of fatigue crack growth based on XFEM and combined approximations
- (2018) S.Z. Feng et al. APPLIED MATHEMATICAL MODELLING
- Deep Transfer Learning for Image-Based Structural Damage Recognition
- (2018) Yuqing Gao et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
- Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data
- (2018) Hua-Ping Wan et al. JOURNAL OF STRUCTURAL ENGINEERING
- 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data
- (2018) Osama Abdeljaber et al. NEUROCOMPUTING
- Convolutional neural networks for automated damage recognition and damage type identification
- (2018) Ceena Modarres et al. Structural Control & Health Monitoring
- Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data
- (2018) Hua-Ping Wan et al. JOURNAL OF STRUCTURAL ENGINEERING
- Deep Convolutional Neural Network for Structural Dynamic Response Estimation and System Identification
- (2018) Rih-Teng Wu et al. JOURNAL OF ENGINEERING MECHANICS
- Dynamic fracture analysis of the soil-structure interaction system using the scaled boundary finite element method
- (2017) Denghong Chen et al. ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- (2017) Shaoqing Ren et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Vision-based structural displacement measurement: System performance evaluation and influence factor analysis
- (2016) X.W. Ye et al. MEASUREMENT
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Seismic Analysis of Masonry Gravity Dams Using the Discrete Element Method: Implementation and Application
- (2015) Eduardo M. Bretas et al. JOURNAL OF EARTHQUAKE ENGINEERING
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Corrosion fatigue effects on life estimation of deteriorated bridges under vehicle impacts
- (2014) W. Zhang et al. ENGINEERING STRUCTURES
- Structural health monitoring of a cable-stayed bridge with Bayesian neural networks
- (2014) Stefania Arangio et al. Structure and Infrastructure Engineering
- Monitoring of typhoon effects on a super-tall building in Hong Kong
- (2013) Q. S. Li et al. Structural Control & Health Monitoring
- Neural network based prediction schemes of the non-linear seismic response of 3D buildings
- (2011) Nikos D. Lagaros et al. ADVANCES IN ENGINEERING SOFTWARE
- Bayesian neural networks for bridge integrity assessment
- (2010) S. Arangio et al. Structural Control & Health Monitoring
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started