Vibration-based structural damage detection strategy using FRFs and machine learning classifiers

[1]  Lei Wang,et al.  A Kriging-based decoupled non-probability reliability-based design optimization scheme for piezoelectric PID control systems , 2023, Mechanical Systems and Signal Processing.

[2]  Maziar Jamshidi,et al.  Structural damage severity classification from time-frequency acceleration data using convolutional neural networks , 2023, Structures.

[3]  M. Machado,et al.  Multiclass Supervised Machine Learning Algorithms Applied to Damage and Assessment Using Beam Dynamic Response , 2023, Journal of Vibration Engineering & Technologies.

[4]  Lei Wang,et al.  Hybrid Reliability-Based Sequential Optimization for PID Vibratory Controller Design Considering Interval and Fuzzy Mixed Uncertainties , 2023, Applied Mathematical Modelling.

[5]  H. Nick,et al.  Damage identification in steel frames using dual-criteria vibration-based damage detection method and artificial neural network , 2023, Structures.

[6]  Sahar Hassani,et al.  Johansen cointegration of frequency response functions contaminated with nonstationary colored noise for structural damage detection , 2023, Journal of Sound and Vibration.

[7]  T. Schumacher,et al.  Damage Detection in Reinforced Concrete Member Using Local Time-Frequency Transform Applied to Vibration Measurements , 2023, Buildings.

[8]  S. Soroushian,et al.  A rapid machine learning-based damage detection algorithm for identifying the extent of damage in concrete shear-wall buildings , 2023, Structures.

[9]  P. J. Escamilla-Ambrosio,et al.  Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures , 2022, Applied Sciences.

[10]  Shengang Li,et al.  Long-term structural health monitoring for bridge based on back propagation neural network and long and short-term memory , 2022, Structural Health Monitoring.

[11]  Zuocai Wang,et al.  Damage Detection of Steel Truss Bridges Based on Gaussian Bayesian Networks , 2022, Buildings.

[12]  M. Wahab,et al.  A robust FRF Damage Indicator combined with optimization techniques for damage assessment in Complex Truss Structures , 2022, Case Studies in Construction Materials.

[13]  Huu‐Tai Thai,et al.  Machine learning for structural engineering: A state-of-the-art review , 2022, Structures.

[14]  K. Roy,et al.  A State-of-the-Art Review on FRF-Based Structural Damage Detection: Development in Last Two Decades and Way Forward , 2021, International Journal of Structural Stability and Dynamics.

[15]  P. Zakian,et al.  Finite cell method for detection of flaws in plate structures using dynamic responses , 2021 .

[16]  M. Mirtaheri,et al.  A decision‐tree‐based algorithm for identifying the extent of structural damage in braced‐frame buildings , 2021, Structural Control and Health Monitoring.

[17]  S. Putti,et al.  Modal testing and evaluation of cracks on cantilever beam using mode shape curvatures and natural frequencies , 2021 .

[18]  Norhisham Bakhary,et al.  Uncertainties Consideration in Empirical Frequency Response Function Data for Damage Identification Based On Artificial Neural Network , 2021, International Journal of Integrated Engineering.

[19]  Arturo González,et al.  A kNN algorithm for locating and quantifying stiffness loss in a bridge from the forced vibration due to a truck crossing at low speed , 2021, Mechanical Systems and Signal Processing.

[20]  Amir K. Ghorbani-Tanha,et al.  Structural damage identification based on fast S-transform and convolutional neural networks , 2021, Structures.

[21]  U. Baneen,et al.  Detection and localization of multiple small damages in beam , 2021, Advances in Mechanical Engineering.

[22]  D. Mallikarjuna Reddy,et al.  Impact damage assessment in carbon fiber reinforced composite using vibration-based new damage index and ultrasonic C-scanning method , 2020 .

[23]  Paola Festa,et al.  Structural damage detection and localization using decision tree ensemble and vibration data , 2020, Comput. Aided Civ. Infrastructure Eng..

[24]  Shin Yee Khoo,et al.  Damage Sensitive PCA-FRF Feature in Unsupervised Machine Learning for Damage Detection of Plate-Like Structures , 2020, International Journal of Structural Stability and Dynamics.

[25]  Tommy H.T. Chan,et al.  Locating and Quantifying Damage in Deck Type Arch Bridges Using Frequency Response Functions and Artificial Neural Networks , 2020, International Journal of Structural Stability and Dynamics.

[26]  Ye Xia,et al.  Vibration-based damage detection for bridges by deep convolutional denoising autoencoder , 2020, Structural Health Monitoring.

[27]  Onur Avci,et al.  A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications , 2020, ArXiv.

[28]  Jun Li,et al.  Non-probabilistic method to consider uncertainties in frequency response function for vibration-based damage detection using Artificial Neural Network , 2020 .

[29]  N. Khaji,et al.  A stochastic spectral finite element method for solution of faulting-induced wave propagation in materially random continua without explicitly modeled discontinuities , 2019, Computational Mechanics.

[30]  Noureddine Touat,et al.  Damage detection in beam through change in measured frequency and undamaged curvature mode shape , 2019 .

[31]  Gokhan Pekcan,et al.  Vibration‐based structural condition assessment using convolution neural networks , 2018, Structural Control and Health Monitoring.

[32]  Faramarz Khoshnoudian,et al.  A New Damage Index Using FRF Data, 2D-PCA Method and Pattern Recognition Techniques , 2017 .

[33]  Moncef Gabbouj,et al.  Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks , 2017 .

[34]  Anirban Guha,et al.  Damage identification in aluminum beams using support vector machine: Numerical and experimental studies , 2016 .

[35]  Claudomiro Sales,et al.  Machine learning algorithms for damage detection: Kernel-based approaches , 2016 .

[36]  David P. Thambiratnam,et al.  Frequency response function based damage identification using principal component analysis and pattern recognition technique , 2014 .

[37]  M. Cao,et al.  Damage identification for beams in noisy conditions based on Teager energy operator-wavelet transform modal curvature , 2014 .

[38]  David P. Thambiratnam,et al.  Structural damage detection method using frequency response functions , 2014 .

[39]  Simon Chesne,et al.  Damage localization using transmissibility functions: A critical review , 2013 .

[40]  Chitra Nasa,et al.  Evaluation of Different Classification Techniques for WEB Data , 2012 .

[41]  Huajun Li,et al.  Assessment of structural damage using natural frequency changes , 2012 .

[42]  Zhichun Yang,et al.  Structural Damage Detection by Changes in Natural Frequencies , 2010 .

[43]  Eslam Pourbasheer,et al.  Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity. , 2009, European journal of medicinal chemistry.

[44]  Shao-Fei Jiang,et al.  Structural Damage Detection by Integrating Data Fusion and Probabilistic Neural Network , 2006 .

[45]  R. A. Shenoi,et al.  Vibration-based damage identification in beam-like composite laminates by using artificial neural networks , 2003 .

[46]  Hong Hao,et al.  Damage identification of structures with uncertain frequency and mode shape data , 2002 .

[47]  J. Vantomme,et al.  Damage assessment in reinforced concrete beams using eigenfrequencies and mode shape derivatives , 2002 .

[48]  Nuno M. M. Maia,et al.  DAMAGE DETECTION USING THE FREQUENCY-RESPONSE-FUNCTION CURVATURE METHOD , 1999 .

[49]  O. S. Salawu.  Detection of structural damage through changes in frequency: a review , 1997 .

[50]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[51]  Sina Shaffiee Haghshenas,et al.  Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art , 2022, IEEE Access.

[52]  Hee-Chang Eun,et al.  Comparison of Damage Detection Methods Depending on FRFs within Specified Frequency Ranges , 2017 .

[53]  Tito Homem-de-Mello,et al.  Monte Carlo sampling-based methods for stochastic optimization , 2014 .

[54]  Mohamed Othman,et al.  A Naïve-Bayes classifier for damage detection in engineering materials , 2007 .

[55]  Usik Lee,et al.  A frequency response function-based structural damage identification method , 2002 .