Machine learning paradigm for structural health monitoring
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Yuequan Bao | Hui Li | Y. Bao | Hui Li
[1] Jie Chen,et al. Decision-Making Algorithm for Multisensor Fusion Based on Grey Relation and DS Evidence Theory , 2016, J. Sensors.
[2] Hui Li,et al. Computer vision and deep learning–based data anomaly detection method for structural health monitoring , 2019 .
[3] F. Viégas,et al. Deep learning of aftershock patterns following large earthquakes , 2018, Nature.
[4] Yan Yu,et al. Investigation of vortex-induced vibration of a suspension bridge with two separated steel box girders based on field measurements , 2011 .
[5] Chih-Chen Chang,et al. IDENTIFICATION OF TIME-VARYING HYSTERETIC STRUCTURES USING WAVELET MULTIRESOLUTION ANALYSIS , 2010 .
[6] Ahsan Kareem,et al. SmartSync: An Integrated Real-Time Structural Health Monitoring and Structural Identification System for Tall Buildings , 2013 .
[7] Hui Li,et al. Automatic seismic damage identification of reinforced concrete columns from images by a region‐based deep convolutional neural network , 2019, Structural Control and Health Monitoring.
[8] Hui Li,et al. Optimal policy for structure maintenance: A deep reinforcement learning framework , 2020 .
[9] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.
[10] Danhui Dan,et al. Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision , 2019, Measurement.
[11] Charles R. Farrar,et al. Efficient Full-Field Vibration Measurements and Operational Modal Analysis Using Neuromorphic Event-Based Imaging , 2018, Journal of Engineering Mechanics.
[12] VARUN CHANDOLA,et al. Anomaly detection: A survey , 2009, CSUR.
[13] Yuequan Bao,et al. Video‐based multiscale identification approach for tower vibration of a cable‐stayed bridge model under earthquake ground motions , 2019, Structural Control and Health Monitoring.
[14] Hui Li,et al. 3-D modelling and statistical properties of surface pits of corroded wire based on image processing technique , 2016 .
[15] James M. W. Brownjohn,et al. ARMA modelled time-series classification for structural health monitoring of civil infrastructure , 2008 .
[16] H. Burton,et al. A machine learning framework for assessing post-earthquake structural safety , 2018 .
[17] Maria Q. Feng,et al. Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review , 2018 .
[18] Charles R. Farrar,et al. Blind identification of full-field vibration modes of output-only structures from uniformly-sampled, possibly temporally-aliased (sub-Nyquist), video measurements , 2017 .
[19] Jer-Nan Juang,et al. An eigensystem realization algorithm for modal parameter identification and model reduction. [control systems design for large space structures] , 1985 .
[20] Yi-Qing Ni,et al. In-service condition assessment of bridge deck using long-term monitoring data of strain response , 2012 .
[21] K.W.Y. Chan,et al. Field measurement results of Tsing Ma suspension bridge during typhoon Victor , 2000 .
[22] You-Lin Xu,et al. Damage Detection in Long Suspension Bridges Using Stress Influence Lines , 2015 .
[23] Ikhlas Abdel-Qader,et al. ANALYSIS OF EDGE-DETECTION TECHNIQUES FOR CRACK IDENTIFICATION IN BRIDGES , 2003 .
[24] Joachim Denzler,et al. Deep learning and process understanding for data-driven Earth system science , 2019, Nature.
[25] Björn Stenger,et al. Visual change detection on tunnel linings , 2016, Machine Vision and Applications.
[26] Peng Zhao,et al. Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images , 2018, Comput. Aided Civ. Infrastructure Eng..
[27] Yiqing Xiao,et al. Field measurements of typhoon effects on a super tall building , 2004 .
[28] Ruodan Lu,et al. Detection of Structural Components in Point Clouds of Existing RC Bridges , 2018, Comput. Aided Civ. Infrastructure Eng..
[29] B. F. Spencer,et al. Fatigue life prediction for parallel-wire stay cables considering corrosion effects , 2018, International Journal of Fatigue.
[30] Ilias Bilionis,et al. Automated building image extraction from 360° panoramas for postdisaster evaluation , 2019, Comput. Aided Civ. Infrastructure Eng..
[31] C. Farrar,et al. Estimation of full‐field, full‐order experimental modal model of cable vibration from digital video measurements with physics‐guided unsupervised machine learning and computer vision , 2019, Structural Control and Health Monitoring.
[32] Hui Li,et al. Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio , 2018 .
[33] Yongchao Yang,et al. Time-Frequency Blind Source Separation Using Independent Component Analysis for Output-Only Modal Identification of Highly Damped Structures , 2013 .
[34] Charles R. Farrar,et al. Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification , 2017 .
[35] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[36] Limin Sun,et al. Traffic Sensing Methodology Combining Influence Line Theory and Computer Vision Techniques for Girder Bridges , 2019, J. Sensors.
[37] Lawrence Sirovich,et al. Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[38] Hui Li,et al. Convolutional neural network‐based data anomaly detection method using multiple information for structural health monitoring , 2018, Structural Control and Health Monitoring.
[39] Bart De Moor,et al. Subspace algorithms for the stochastic identification problem, , 1993, Autom..
[40] Jean-Claude Golinval,et al. Physical interpretation of independent component analysis in structural dynamics , 2007 .
[41] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[42] Yu Zhao,et al. Vehicle weight identification system for spatiotemporal load distribution on bridges based on non-contact machine vision technology and deep learning algorithms , 2020 .
[43] Badrinath Roysam,et al. Image change detection algorithms: a systematic survey , 2005, IEEE Transactions on Image Processing.
[44] Jingdao Chen,et al. A framework for real-time pro-active safety assistance for mobile crane lifting operations , 2016 .
[45] Billie F. Spencer,et al. Vision-Based Modal Survey of Civil Infrastructure Using Unmanned Aerial Vehicles , 2019, Journal of Structural Engineering.
[46] Ioannis Brilakis,et al. Digital twinning of existing reinforced concrete bridges from labelled point clusters , 2019, Automation in Construction.
[47] Yongchao Yang,et al. Dynamic Imaging: Real-Time Detection of Local Structural Damage with Blind Separation of Low-Rank Background and Sparse Innovation , 2016 .
[48] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[49] Alessandro De Stefano,et al. Vibration-based monitoring of civil infrastructure: challenges and successes , 2011 .
[50] Namgyu Kim,et al. Deep learning–based autonomous concrete crack evaluation through hybrid image scanning , 2019, Structural Health Monitoring.
[51] Joseph Lardies,et al. Identification of modal parameters using the wavelet transform , 2002 .
[52] Piotr Omenzetter,et al. Application of time series analysis for bridge monitoring , 2006 .
[53] Mohammad R. Jahanshahi,et al. Computer-Aided Approach for Rapid Post-Event Visual Evaluation of a Building Façade , 2018, Sensors.
[54] Rune Brincker,et al. Modal identification of output-only systems using frequency domain decomposition , 2001 .
[55] M. Jancosek,et al. Flexible building primitives for 3D building modeling , 2015 .
[56] Yong Huang,et al. Fractal dimension based damage identification incorporating multi-task sparse Bayesian learning , 2018 .
[57] Billie F. Spencer,et al. Sensor fault management techniques for wireless smart sensor networks in structural health monitoring , 2019, Structural Control and Health Monitoring.
[58] P. Scott Harvey,et al. Vision‐based vibration monitoring using existing cameras installed within a building , 2018, Structural Control and Health Monitoring.
[59] Michele Betti,et al. Neural network based modal identification of structural systems through output-only measurement , 2014 .
[60] Hui Li,et al. Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images , 2019 .
[61] Frédo Durand,et al. Modal identification of simple structures with high-speed video using motion magnification , 2015 .
[62] Paul W. Fieguth,et al. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure , 2015, Adv. Eng. Informatics.
[63] Charles R. Farrar,et al. Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review , 1996 .
[64] Dongmei Chen,et al. Change detection from remotely sensed images: From pixel-based to object-based approaches , 2013 .
[65] Hui Li,et al. Identification of spatio‐temporal distribution of vehicle loads on long‐span bridges using computer vision technology , 2016 .
[66] Liming Zhou,et al. A methodology for obtaining spatiotemporal information of the vehicles on bridges based on computer vision , 2019, Comput. Aided Civ. Infrastructure Eng..
[67] Khalid M. Mosalam,et al. Deep Transfer Learning for Image‐Based Structural Damage Recognition , 2018, Comput. Aided Civ. Infrastructure Eng..
[68] Ching Y. Suen,et al. Historical review of OCR research and development , 1992, Proc. IEEE.
[69] Hui Li,et al. Field monitoring and validation of vortex-induced vibrations of a long-span suspension bridge , 2014 .
[70] Billie F. Spencer,et al. Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring , 2019, Engineering.
[71] Mohammad R. Jahanshahi,et al. Color and depth data fusion using an RGB‐D sensor for inexpensive and contactless dynamic displacement‐field measurement , 2017 .
[72] Tao Cheng,et al. Real-time resource location data collection and visualization technology for construction safety and activity monitoring applications , 2013 .
[73] Charles R. Farrar,et al. Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures , 2018 .
[74] Hui Li,et al. The State of the Art of Data Science and Engineering in Structural Health Monitoring , 2019, Engineering.
[75] Hui Li,et al. Cluster Analysis of Winds and Wind-induced Vibrations on a Long-span Bridge based on Long-term Field Monitoring Data , 2017 .
[76] Hui Li,et al. A machine learning–based approach for adaptive sparse time–frequency analysis used in structural health monitoring , 2020 .
[77] Gaurav S. Sukhatme,et al. A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures , 2009 .