Access the full text.
Sign up today, get DeepDyve free for 14 days.
L. Sörnmo, P. Laguna (2005)
Bioelectrical Signal Processing in Cardiac and Neurological Applications
Guoqing Gui, Hong Pan, Zhibin Lin, Yonghua Li, Zhijun Yuan (2017)
Data-driven support vector machine with optimization techniques for structural health monitoring and damage detectionKSCE Journal of Civil Engineering, 21
J. Semmlow (2004)
Biosignal and Medical Image Processing
C. Therrien (1992)
Discrete Random Signals and Statistical Signal Processing
Alberto Díez, N. Khoa, Mehrisadat Alamdari, Yang Wang, Fang Chen, Peter Runcie (2016)
A clustering approach for structural health monitoring on bridgesJournal of Civil Structural Health Monitoring, 6
S. Pirmoradi, M. Teshnehlab, N. Zarghami, A. Sharifi (2020)
A Self-organizing Deep Auto-Encoder approach for Classification of Complex Diseases using SNP Genomics DataAppl. Soft Comput., 97
D. An, Nam-Ho Kim, Jooho Choi (2015)
Practical options for selecting data-driven or physics-based prognostics algorithms with reviewsReliab. Eng. Syst. Saf., 133
Gyungmin Toh, Junhong Park (2020)
Review of Vibration-Based Structural Health Monitoring Using Deep LearningApplied Sciences, 10
S. Alessio (2016)
Digital Signal Processing and Spectral Analysis for Scientists
M. Daneshvar, A. Gharighoran, S. Zareei, A. Karamodin (2020)
Early damage detection under massive data via innovative hybrid methods: application to a large-scale cable-stayed bridgeStructure and Infrastructure Engineering, 17
M. Fallahian, F. Khoshnoudian, V. Meruane (2018)
Ensemble classification method for structural damage assessment under varying temperatureStructural Health Monitoring, 17
Yusheng Chen, Meng Lin, R. Yu, Tianshu Wang (2021)
Research on Simulation and State Prediction of Nuclear Power System Based on LSTM Neural NetworkScience and Technology of Nuclear Installations
Mohsen Azimi, A. Eslamlou, G. Pekcan (2020)
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art ReviewSensors (Basel, Switzerland), 20
J. Semmlow (2004)
Biosignal and biomedical image processing : MATLAB-based applications
Min Chung, Seungjun Kim, Kanghyeok Lee, Do Shin (2020)
Detection of damaged mooring line based on deep neural networksOcean Engineering, 209
H. Sarmadi, A. Karamodin (2020)
A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effectsMechanical Systems and Signal Processing, 140
Onur Avcı, Osama Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj, D. Inman (2020)
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning ApplicationsArXiv, abs/2004.04373
S. Sajedi, Xiao Liang (2019)
Vibration‐based semantic damage segmentation for large‐scale structural health monitoringComputer‐Aided Civil and Infrastructure Engineering, 35
P. Baraldi, F. Cadini, F. Mangili, E. Zio (2013)
Prognostics under Different Available InformationChemical engineering transactions, 33
A. Subasi (2007)
Selection of optimal AR spectral estimation method for EEG signals using Cramer-Rao boundComputers in biology and medicine, 37 2
F. Catbas, O. Celik, Onur Avcı, Osama Abdeljaber, M. Gul, N. Do (2017)
Sensing and Monitoring for Stadium Structures: A Review of Recent Advances and a Forward LookFrontiers in Built Environment, 3
(2009)
This PDF file includes: Materials and Methods
M. Wahab, G. Roeck (1999)
Damage detection in bridges using modal curvatures: application to a real damage scenarioJournal of Sound and Vibration, 226
A. Deraemaeker, E. Reynders, G. Roeck, J. Kullaa (2008)
Vibration based Structural Health Monitoring using output-only measurements under changing environmentMechanical Systems and Signal Processing, 22
Rui Zhao, Ruqiang Yan, Zhenghua Chen, K. Mao, Peng Wang, R. Gao (2019)
Deep learning and its applications to machine health monitoringMechanical Systems and Signal Processing
Marwa Chaabane, A. Hamida, M. Mansouri, H. Nounou, Onur Avcı (2016)
Damage detection using enhanced multivariate statistical process control technique2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)
AJ Holden (2006)
10.1126/science.1127647Science, 313
S. Park, C. Yun, Y. Roh, Jong-Jae Lee (2006)
PZT-based active damage detection techniques for steel bridge componentsSmart Materials and Structures, 15
(2019)
Deep learning book
A. Dineva, Bence Csomós, Szabolcs Sz., I. Vajda (2021)
Investigation of the performance of direct forecasting strategy using machine learning in State-of-Charge prediction of Li-ion batteries exposed to dynamic loadsJournal of energy storage, 36
M. Fallahian, F. Khoshnoudian, Saeid Talaei, V. Meruane, F. Shadan (2018)
Experimental validation of a deep neural network—Sparse representation classification ensemble methodThe Structural Design of Tall and Special Buildings, 27
S. Dyke, D. Bernal, J. Beck, C. Ventura (2003)
Experimental Phase II of the Structural Health Monitoring Benchmark Problem
M. Lallam, A. Mammeri, A. Djebli (2021)
Fuzzy Analytical Hierarchy Processes for Damage State Assessment of Arch Masonry BridgeCivil Engineering Journal
Rih-Teng Wu, M. Jahanshahi (2018)
Data fusion approaches for structural health monitoring and system identification: Past, present, and futureStructural Health Monitoring, 19
J. Amezquita-Sanchez, H. Adeli (2014)
Signal Processing Techniques for Vibration-Based Health Monitoring of Smart StructuresArchives of Computational Methods in Engineering, 23
A. Nath, S. Karthikeyan (2018)
Enhanced prediction of recombination hotspots using input features extracted by class specific autoencoders.Journal of theoretical biology, 444
Adam Santos, E. Figueiredo, M. Silva, C. Sales, João Costa (2016)
Machine learning algorithms for damage detection: Kernel-based approachesJournal of Sound and Vibration, 363
Feng Jia, Y. Lei, Liang Guo, Jing Lin, Saibo Xing (2018)
A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machinesNeurocomputing, 272
A. Entezami, H. Sarmadi, S. Mariani (2020)
An Unsupervised Learning Approach for Early Damage Detection by Time Series Analysis and Deep Neural Network to Deal with Output-Only (Big) DataProceedings of 7th International Electronic Conference on Sensors and Applications
(2009)
An introduction to feature geometry. Gest, Seg Prosody 25:149–165
Hongmei Liu, Lianfeng Li, Jian Ma (2016)
Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound SignalsShock and Vibration, 2016
Kanghyeok Lee, Min Chung, Seungjun Kim, Do Shin (2021)
Damage detection of catenary mooring line based on recurrent neural networksOcean Engineering, 227
Soong Heng, S. Zhang, A. Tan, J. Mathew (2009)
Rotating machinery prognostics. State of the art, challenges and opportunities
Osama Abdeljaber, Onur Avcı, S. Kiranyaz, M. Gabbouj, D. Inman (2017)
Real-time vibration-based structural damage detection using one-dimensional convolutional neural networksJournal of Sound and Vibration, 388
Ahmad Karim, M. Güzel, M. Tolun, H. Kaya, F. Çelebi (2018)
A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and ProcessingMathematical Problems in Engineering
Feng Jia, Y. Lei, Jing Lin, Xin Zhou, N. Lu (2016)
Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive dataMechanical Systems and Signal Processing, 72
SM Kay (1981)
10.1109/PROC.1981.12184Proc IEEE, 69
H. Sarmadi, A. Entezami (2020)
Application of supervised learning to validation of damage detectionArchive of Applied Mechanics, 91
Pascal Vincent, H. Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol (2010)
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising CriterionJ. Mach. Learn. Res., 11
C. Farrar, K. Worden (2012)
Structural Health Monitoring: A Machine Learning Perspective
S. Kay (1988)
Modern Spectral Estimation: Theory and Application
(1981)
Spectrum analysis-a modern perspective
(2020)
A SURVEY ON DEEP LEARNING TECHNIQUESStrad Research
Joshua Patterson, Adam Gibson (2017)
Deep Learning: A Practitioner's Approach
Liang Guo, Hongli Gao, HaiFeng Huang, He Xiang, Shichao Li (2016)
Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition MonitoringShock and Vibration, 2016
Y. Li (2010)
Hypersensitivity of strain-based indicators for structural damage identification: A reviewMechanical Systems and Signal Processing, 24
S. Doebling, C. Farrar, M. Prime (1998)
A summary review of vibration-based damage identification methodsThe Shock and Vibration Digest, 30
Wei Li, Y. Huang (2020)
A method for damage detection of a jacket platform under random wave excitations using cross correlation analysis and PCA-based methodOcean Engineering, 214
O. Lautour, P. Omenzetter (2010)
Damage classification and estimation in experimental structures using time series analysis and pattern recognitionMechanical Systems and Signal Processing, 24
Ming Wang, J. Lynch, H. Sohn (2014)
Sensor Technologies for Civil Infrastructures
E. Figueiredo, G. Park, C. Farrar, K. Worden, J. Figueiras (2011)
Machine learning algorithms for damage detection under operational and environmental variabilityStructural Health Monitoring, 10
M. Flah, Itzel Nunez, Wassim Chaabene, M. Nehdi (2020)
Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic ReviewArchives of Computational Methods in Engineering, 28
M Broe (2009)
10.1017/cbo9780511519918.007Gest, Seg Prosody, 25
Onur Avcı, Osama Abdeljaber, S. Kiranyaz, M. Hussein, D. Inman (2018)
Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networksJournal of Sound and Vibration
In civil engineering, monitoring the structural damage becomes critically important to ensure safety and avoid sudden failures of structures. Therefore, improving the accuracy of methods for Structural Health Monitoring problems remains a priority. This paper proposes a new framework that combines the Burg Autoregressive (BAR) and Stacked Autoencoder-based Deep Neural Network (SAE-DNN) for the damage detection of steel frames using time-series data. Firstly, features of the time-series data are extracted using the BAR method. Then, the Autoencoder (AE) network is employed to reduce the dimension and learn sensitive features. Finally, the AE and Softmax layers are stacked and trained in a supervised manner of DNN for structural damage detection. The experimental data of two steel frame benchmarks are adopted to verify the performance of the proposed framework. The results show that the proposed framework could achieve high accuracy (97.8 and 99%) in the damage identification of steel frames.
Innovative Infrastructure Solutions – Springer Journals
Published: Oct 1, 2022
Keywords: Autoencoder; Burg autoregressive; Damage detection; Deep neural network; Steel frame
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.