On the vulnerability of data-driven structural health monitoring models to adversarial attack

Many approaches at the forefront of structural health monitoring rely on cutting-edge techniques from the field of machine learning. Recently, much interest has been directed towards the study of s...

[1]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[2]  Charles R. Farrar,et al.  Structural health monitoring algorithm comparisons using standard data sets , 2009 .

[3]  Gyuhae Park,et al.  Structural Health Monitoring With Autoregressive Support Vector Machines , 2009 .

[4]  Charles R. Farrar,et al.  The fundamental axioms of structural health monitoring , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[5]  Lin Wang,et al.  Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm , 2016 .

[6]  David A. Nix,et al.  Vibration–based structural damage identification , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[7]  Brian Finley,et al.  ICAS: the center of diagnostics and prognostics for the United States Navy , 2001, SPIE Defense + Commercial Sensing.

[8]  Raid Karoumi,et al.  An approach to decision‐making analysis for implementation of structural health monitoring in bridges , 2019, Structural Control and Health Monitoring.

[9]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[10]  K. Worden,et al.  The application of machine learning to structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[11]  Charles R. Farrar,et al.  An Outlier Analysis Framework for Impedance-based Structural Health Monitoring , 2005 .

[12]  ChaYoung-Jin,et al.  Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks , 2017 .

[13]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[14]  Keith Worden,et al.  Experimental validation of a structural health monitoring methodology: Part III. Damage location on an aircraft wing , 2003 .

[15]  Keith Worden,et al.  DAMAGE ASSESSMENT USING NEURAL NETWORKS , 2003 .

[16]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.