Sparse Bayesian learning for structural damage detection using expectation–maximization technique

Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works

Equation of State Calculations by Fast Computing Machines

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192 · see citing works

Pattern Recognition and Machine Learning

(2006) · doi:10.1007/978-0-387-45528-0 · Cited by 9,824 · see citing works

Maximum likelihood estimation from incomplete data via the EM algorithm

Dempster, A. P. (1977) · Journal of the Royal Statistical Society Series A (Statistics in Society) · Cited by 4,506 · see citing works

Probability Theory: The Logic of Science

Jaynes, E. T. (2003) · doi:10.1017/cbo9780511790423 · Cited by 4,045 · see citing works

Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review

Los Alamos National Lab., N. (. S., Doebling, S., USDOE, W., Farrar, C., Prime, M., & Shevitz, D. (1996) · doi:10.2172/249299 · Cited by 2,899 · see citing works

Detection of structural damage through changes in frequency: a review

Salawu, O. S. (1997) · Engineering Structures · doi:10.1016/s0141-0296(96)00149-6 · Cited by 2,080 · see citing works

Vibration-based Damage Identification Methods: A Review and Comparative Study

Fan, W., & Qiao, P. (2010) · Structural Health Monitoring · doi:10.1177/1475921710365419 · Cited by 2,047 · see citing works

Model Updating In Structural Dynamics: A Survey

Mottershead, J. E., & Friswell, M. I. (1993) · Journal of Sound and Vibration · doi:10.1006/jsvi.1993.1340 · Cited by 1,691 · see citing works

Iteratively reweighted least squares minimization for sparse recovery

Daubechies, I. C., DeVore, R., Fornasier, M., & Güntürk, C. S. (2009) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20303 · Cited by 1,295 · see citing works

Iteratively reweighted algorithms for compressive sensing

Chartrand, R., & Yin, W. (2008) · 2008 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2008.4518498 · Cited by 1,248 · see citing works

Transitional Markov Chain Monte Carlo Method for Bayesian Model Updating, Model Class Selection, and Model Averaging

Ching, J., & Chen, Y.-C. (2007) · Journal of Engineering Mechanics · doi:10.1061/(asce)0733-9399(2007)133:7(816 · Cited by 848 · see citing works

Bayesian Compressive Sensing Using Laplace Priors

Babacan, S. D., Molina, R., & Katsaggelos, A. K. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2032894 · Cited by 821 · see citing works

Restricted isometry properties and nonconvex compressive sensing

Chartrand, R., & Станева, В. (2008) · Inverse Problems · doi:10.1088/0266-5611/24/3/035020 · Cited by 622 · see citing works

Bayesian system identification based on probability logic

Beck, J. L. (2010) · Structural Control and Health Monitoring · doi:10.1002/stc.424 · Cited by 605 · see citing works

Bayesian Probabilistic Approach to Structural Health Monitoring

Vanik, M. W., Beck, J. L., & Au, S. (2000) · Journal of Engineering Mechanics · doi:10.1061/(asce)0733-9399(2000)126:7(738 · Cited by 440 · see citing works

Bayesian Regularization and Pruning Using a Laplace Prior

Williams, P. M. (1995) · Neural Computation · doi:10.1162/neco.1995.7.1.117 · Cited by 377 · see citing works

An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge

Hoon Sohn, A., Dzwonczyk, M., Straser, E. G., Kiremidjian, A. S., Law, K. H., & Meng, T. H. Y. (1999) · Earthquake Engineering & Structural Dynamics · doi:10.1002/(sici)1096-9845(199908)28:8 · Cited by 344 · see citing works

Long term vibration monitoring of an RC slab: Temperature and humidity effect

Xia, Y., Hao, H., Zanardo, G., & Deeks, A. (2006) · Engineering Structures · doi:10.1016/j.engstruct.2005.09.001 · Cited by 331 · see citing works

State-of-the-art review on Bayesian inference in structural system identification and damage assessment

Huang, Y., Shao, C., Wu, B., Beck, J. L., & Li, H. (2018) · Advances in Structural Engineering · doi:10.1177/1369433218811540 · Cited by 219 · see citing works

Damage Localization by Directly Using Incomplete Mode Shapes

Shi, Z., Law, S., & Zhang, L. (2000) · Journal of Engineering Mechanics · doi:10.1061/(asce)0733-9399(2000)126:6(656 · Cited by 219 · see citing works

A Bayesian probabilistic approach for structure damage detection

Sohn, H., & Law, K. H. (1997) · Earthquake Engineering & Structural Dynamics · doi:10.1002/(sici)1096-9845(199712)26:12 · Cited by 218 · see citing works

Statistical damage identification of structures with frequency changes

Xia, Y., & Hao, H. (2003) · Journal of Sound and Vibration · doi:10.1016/s0022-460x(02)01077-5 · Cited by 191 · see citing works

Damage identification of structures with uncertain frequency and mode shape data

Xia, Y., Hao, H., Brownjohn, J. M. W., & Xia, P. (2002) · Earthquake Engineering &amp; Structural Dynamics · doi:10.1002/eqe.137 · Cited by 171 · see citing works

Output-only modal identification with limited sensors using sparse component analysis

Yang, Y., & Nagarajaiah, S. (2013) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2013.04.004 · Cited by 161 · see citing works

Calculation of Posterior Probabilities for Bayesian Model Class Assessment and Averaging from Posterior Samples Based on Dynamic System Data

Cheung, S. H., & Beck, J. L. (2010) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/j.1467-8667.2009.00642.x · Cited by 156 · see citing works

Assessment of Vibration-based Damage Identification Methods Using Displacement and Distributed Strain Measurements

Adewuyi, A. P., Wu, Z., & Serker, N. H. M. K. (2009) · Structural Health Monitoring · doi:10.1177/1475921709340964 · Cited by 156 · see citing works

Bayesian system identification based on hierarchical sparse Bayesian learning and Gibbs sampling with application to structural damage assessment

Huang, Y., Beck, J. L., & Li, H. (2017) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2017.01.030 · Cited by 147 · see citing works

Monitoring Structural Health Using a Probabilistic Measure

Beck, J. L., Au, S., & Vanik, M. W. (2001) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/0885-9507.00209 · Cited by 147 · see citing works

An improved perturbation method for stochastic finite element model updating

Hua, X., Ni, Y., Chen, Z. Q., & Ko, J. (2007) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.2151 · Cited by 132 · see citing works

Structural damage detection based on <i>l</i> ₁ regularization using natural frequencies and mode shapes

Hou, R., Xia, Y., & Zhou, X. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2107 · Cited by 127 · see citing works

Bayesian Analysis of the Phase II IASC–ASCE Structural Health Monitoring Experimental Benchmark Data

Ching, J., & Beck, J. L. (2004) · Journal of Engineering Mechanics · doi:10.1061/(asce)0733-9399(2004)130:10(1233 · Cited by 112 · see citing works

Identification and Damage Detection of Trusses Using Modal Data

Liu, P.-L. (1995) · Journal of Structural Engineering · doi:10.1061/(asce)0733-9445(1995)121:4(599 · Cited by 112 · see citing works

Hierarchical sparse Bayesian learning for structural damage detection: Theory, computation and application

Huang, Y., Beck, J. L., & Li, H. (2017) · Structural Safety · doi:10.1016/j.strusafe.2016.09.001 · Cited by 111 · see citing works

Selection of regularization parameter for l1-regularized damage detection

Hou, R., Xia, Y., Bao, Y., & Zhou, X. (2018) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2018.02.064 · Cited by 108 · see citing works

Comparative studies on damage identification with Tikhonov regularization and sparse regularization: Damage Detection with Tikhonov Regularization and Sparse Regularization

Zhang, C. D., & Xu, Y. L. (2016) · Structural Control and Health Monitoring · doi:10.1002/stc.1785 · Cited by 106 · see citing works

Structural damage identification via a combination of blind feature extraction and sparse representation classification

Yang, Y., & Nagarajaiah, S. (2014) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.09.009 · Cited by 101 · see citing works

L ₁ regularization approach to structural damage detection using frequency data

Zhou, X.-Q., Xia, Y., & Weng, S. (2015) · Structural Health Monitoring · doi:10.1177/1475921715604386 · Cited by 99 · see citing works

HIERARCHICAL SPARSE BAYESIAN LEARNING FOR STRUCUTRAL HEALTH MONITORING WITH INCOMPLETE MODAL DATA

Huang, Y., & Beck, J. L. (2015) · International Journal for Uncertainty Quantification · doi:10.1615/int.j.uncertaintyquantification.2015011808 · Cited by 87 · see citing works

Markov chain Monte Carlo-based Bayesian method for structural model updating and damage detection

Lam, H., Yang, J., & Au, S. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2140 · Cited by 86 · see citing works

Sparsel1optimization-based identification approach for the distribution of moving heavy vehicle loads on cable-stayed bridges

Bao, Y., Li, H., Chen, Z., Zhang, F., & Guo, A. (2015) · Structural Control and Health Monitoring · doi:10.1002/stc.1763 · Cited by 73 · see citing works

A Bayesian approach to identifying structural nonlinearity using free-decay response: Application to damage detection in composites

Nichols, J. M., Link, W. A., Murphy, K. D., & Olson, C. C. (2010) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2010.02.004 · Cited by 70 · see citing works

Condition Assessment of Shear Connectors in Slab-Girder Bridges via Vibration Measurements

Xia, Y., Hao, H., Deeks, A. J., & Zhu, X. (2007) · Journal of Bridge Engineering · doi:10.1061/(asce)1084-0702(2008)13:1(43 · Cited by 55 · see citing works

The EM Algorithm: A Guided Tour

Couvreur, C. (1997) · Birkhäuser Boston eBooks · doi:10.1007/978-1-4612-1996-5_12 · Cited by 52 · see citing works

Experimental Investigation on Statistical Moment-based Structural Damage Detection Method

Xu, Y. L., Zhang, J., Li, J., & Xia, Y. (2009) · Structural Health Monitoring · doi:10.1177/1475921709341011 · Cited by 45 · see citing works

Bayesian probabilistic approach for model updating and damage detection for a large truss bridge

Mustafa, S., Debnath, N., & Dutta, A. (2015) · International Journal of Steel Structures · doi:10.1007/s13296-015-6016-3 · Cited by 42 · see citing works

Uncertainty Quantification and the Verification and Validation of Computational Models

Hemez, F. (2005) · doi:10.1002/0470869097.ch9 · Cited by 27 · see citing works

Fractal dimension based damage identification incorporating multi-task sparse Bayesian learning

Huang, Y., Li, H., Wu, S., & Yang, Y. (2018) · Smart Materials and Structures · doi:10.1088/1361-665x/aac248 · Cited by 22 · see citing works

Laplace Approximations in B ayesian Lifetime Analysis

Onar, A. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat03867 · Cited by 2 · see citing works

Untitled work

(1998) · doi:10.1061/(asce)0733-9399(1998)124:4(455

Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019

Hou, R., & Xia, Y. (2021) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2020.115741 · Cited by 674 · see citing works

Recent progress and future trends on damage identification methods for bridge structures

An, Y., Chatzi, E., Sim, S., Laflamme, S., Błachowski, B., & Ou, J. (2019) · Structural Control and Health Monitoring · doi:10.1002/stc.2416 · Cited by 339 · see citing works

Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

Zar, A., Hussain, Z., Akbar, M., Rabczuk, T., Lin, Z., Li, S., & Ahmed, B. (2024) · International Journal of Mechanics and Materials in Design · doi:10.1007/s10999-023-09692-3 · Cited by 122 · see citing works

Dimensional decomposition-aided metamodels for uncertainty quantification and optimization in engineering: A review

Zhao, H., Fu, C., Zhang, Y., Zhu, W. D., Lu, K., & Francis, E. M. (2024) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2024.117098 · Cited by 70 · see citing works

Structural damage detection based on variational Bayesian inference and delayed rejection adaptive Metropolis algorithm

Wang, X., Hou, R., Xia, Y., & Zhou, X. (2020) · Structural Health Monitoring · doi:10.1177/1475921720921256 · Cited by 68 · see citing works

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

Karakostas, C., Quaranta, G., Chatzi, E., Zülfikar, A. C., Çetindemir, O., Roeck, G. D., Döhler, M., Limongelli, M. P., Lombaert, G., Apaydın, N. M., et al. (2023) · Bulletin of Earthquake Engineering · doi:10.1007/s10518-023-01819-3 · Cited by 65 · see citing works

Towards probabilistic data‐driven damage detection in SHM using sparse Bayesian learning scheme

Wang, Q., Dai, Y., Ma, Z., Ni, Y., Tang, J., Xu, X., & Wu, Z. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.3070 · Cited by 58 · see citing works

Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability

Wang, Q., Dai, Y., Ma, Z., Wang, J., Lin, J., Ni, Y., Ren, W., Jiang, J. D., Yang, X., & Yan, J.-R. (2023) · Structural Health Monitoring · doi:10.1177/14759217231170316 · Cited by 54 · see citing works

Sparse Bayesian learning for structural damage detection under varying temperature conditions

Hou, R., Wang, X., Xia, Q., & Xia, Y. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106965 · Cited by 50 · see citing works

Substructure approach for Bayesian probabilistic model updating using response reconstruction technique

Ni, P., Li, Q., Han, Q., Xu, K., & Du, X. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109624 · Cited by 47 · see citing works

Full-field displacement measurement of long-span bridges using one camera and robust self-adaptive complex pyramid

Wang, Y., Hu, W., Teng, J., & Xia, Y. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111451 · Cited by 37 · see citing works

Bayesian damage identification based on autoregressive model and MH-PSO hybrid MCMC sampling method

Luo, J., Huang, M., Xiang, C., & Lei, Y. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-021-00541-5 · Cited by 37 · see citing works

Finite element model updating through derivative-free optimization algorithm

Li, D., & Zhang, J. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109726 · Cited by 36 · see citing works

Sparse Bayesian learning with model reduction for probabilistic structural damage detection with limited measurements

Li, J., Huang, Y., & Asadollahi, P. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.113183 · Cited by 34 · see citing works

Laplace approximation in sparse Bayesian learning for structural damage detection

Wang, X., Hou, R., Xia, Y., & Zhou, X. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106701 · Cited by 32 · see citing works

Multi-Role collaborative framework for structural damage identification considering measurement noise effect

Chen, Z., Zhang, Z., Chen, X., Hou, R., Ding, Z., Liu, F., & Yang, Z. (2025) · Measurement · doi:10.1016/j.measurement.2025.117106 · Cited by 27 · see citing works

A feasibility study on void detection of cement-emulsified asphalt mortar for slab track system utilizing measured vibration data

Hu, Q., Shen, Y. J., Zhu, H. P., Lam, H., & Adeagbo, M. O. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.112349 · Cited by 26 · see citing works

Accurate force evaluation in prestressed cable-strut structures: A robust sparse Bayesian learning method with feedback-driven error optimization

Chen, Y., Zhou, H., Gao, J., Shen, Z., Xie, T., & Sareh, P. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.119878 · Cited by 25 · see citing works

Bayesian-based method for the simultaneous identification of structural damage and moving force

Chen, Z., Weng, S., Hong, Y., Li, J., Zhu, H., Yan, Y., & Wu, L. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109742 · Cited by 22 · see citing works

A hybrid sequential sampling strategy for sparse polynomial chaos expansion based on compressive sampling and Bayesian experimental design

Zhang, B.-Y., & Ni, Y. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.114130 · Cited by 19 · see citing works

A robust sparse Bayesian learning method for the structural damage identification by a mixture of Gaussians

Li, R., Zheng, S., Wang, F., Deng, Q., Li, X., Xiao, Y., & Song, X. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110483 · Cited by 18 · see citing works

Structural damage detection with two-stage modal information and sparse Bayesian learning

Zou, Y., Yang, G., Lu, X., He, X., & Cai, C. (2023) · Structures · doi:10.1016/j.istruc.2023.105361 · Cited by 17 · see citing works

Underwater Acoustic Channel Estimation Based on Sparse Bayesian Learning Algorithm

Jia, S., Zou, S., Zhang, X., & Da, L. (2023) · IEEE Access · doi:10.1109/access.2023.3238100 · Cited by 16 · see citing works

Enhanced adaptive sequential Monte Carlo method for Bayesian model class selection by quantifying data fit and information gain

Yang, J., Liu, W., An, Y., & Lam, H. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110792 · Cited by 16 · see citing works

Probabilistic model updating of steel frame structures using strain and acceleration measurements: A multitask learning framework

Yaoyama, T., Itoi, T., & Iyama, J. (2024) · Structural Safety · doi:10.1016/j.strusafe.2024.102442 · Cited by 14 · see citing works

Structural damage detection based on modal feature extraction and multi-objective optimization method for steel structures

Chen, Z., Zhao, D., Chen, Z., & Wang, W. (2022) · Frontiers in Materials · doi:10.3389/fmats.2022.1015322 · Cited by 13 · see citing works

Structural damage detection based on modal strain energy assurance criterion using adaptive region shrinkage assisted IGOA

Chen, Z., Liu, Q., & Pan, C. (2023) · Structures · doi:10.1016/j.istruc.2023.105458 · Cited by 13 · see citing works

Sparse Bayesian learning for damage identification using nonlinear models: Application to weld fractures of steel‐frame buildings

Filippitzis, F., Kohler, M. D., Heaton, T. H., & Beck, J. L. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2870 · Cited by 13 · see citing works

State-of-art review on load identification and response reconstruction to realize digital twin of infrastructures

Li, Y., Luo, L., Wang, X., Zhou, X., Sun, L., & Xia, Y. (2025) · Advances in Structural Engineering · doi:10.1177/13694332251321196 · Cited by 13 · see citing works

Probabilistic estimation of dynamic impact factor for masonry arch bridges using health monitoring data and new finite element method

Nouri, M., & Mohammadzadeh, S. (2020) · Structural Control and Health Monitoring · doi:10.1002/stc.2640 · Cited by 11 · see citing works

A fraction function regularization model for the structural damage identification

Li, R., Song, X., Wang, F., Deng, Q., Li, X., & Xiao, Y. (2023) · Advances in Structural Engineering · doi:10.1177/13694332221151017 · Cited by 11 · see citing works

Bayesian probabilistic damage characterization based on a perturbation model using responses at vibration nodes

Huang, T., & Schröder, K. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106444 · Cited by 11 · see citing works

A Novel Improved Dragonfly Algorithm-Based Structural Damage Identification Approach Using Flexibility Assurance Criterion and Trace Lasso

Chen, Z., Zhao, D., Liu, Q., Zhang, Z., & Yu, L. M. (2023) · International Journal of Structural Stability and Dynamics · doi:10.1142/s021945542450144x · Cited by 8 · see citing works

Vibration-Based Structural Damage Detection Using Sparse Bayesian Learning Techniques

Hou, R., Wang, X., & Xia, Y. (2021) · Structural integrity · doi:10.1007/978-3-030-81716-9_1 · Cited by 6 · see citing works

Time-domain sparse Bayesian learning with PSO algorithm for CA mortar void detection of slab track system

Hu, Q., Zhang, B., & Chen, H. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.120908 · Cited by 4 · see citing works

Structural damage identification based on pattern-coupled sparse Bayesian learning

Lin, J., Hou, R., & Bao, Y. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.113893 · Cited by 4 · see citing works

Damage Detection of an Asymmetrical Frame via Sparse Bayesian Learning with a PSO Algorithm

Hu, Q., Yan, Q., Chen, H., & Guan, Y. (2024) · International Journal of Structural Stability and Dynamics · doi:10.1142/s0219455426500987 · Cited by 4 · see citing works

Physics informed spatial-temporal neural operators for nonlinear structural system response reconstruction and identification

Zhang, X., Wang, Y., Zhu, X., & Li, J. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.121563 · Cited by 3 · see citing works

Sparse Bayesian learning for joint load-parameter-response identification

Luo, L., Sun, L., Li, Y., & Xia, Y. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111831 · Cited by 3 · see citing works

Multitask Sparse Bayesian Machine Learning with Applications in Modeling of Seismic Attenuation and Clay Parameters with Small Data Sets

Gao, J., Wei, S., & Huang, Y. (2025) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-6822 · Cited by 3 · see citing works

Robust post-earthquake structural safety evaluation from limited acceleration measurements using sparse Bayesian broad learning with dimensionally reduced damage-sensitive features

Liu, X., Huang, Y., & Li, H. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.121486 · Cited by 2 · see citing works

A unified equivalent model of the l 0 minimization model for structural damage identification

Song, X., Ma, X., Li, R., Yi, W., Ma, L., Zheng, S., & Wang, K. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113621 · Cited by 2 · see citing works

Efficient sampling-free Bayesian static model updating using uncertain measurement displacements

Xue, K., Huang, B., Chen, H., & Zeng, J. (2025) · Structures · doi:10.1016/j.istruc.2025.110296 · Cited by 1 · see citing works

ANN-based damage detection with two-stage approach and model reduction

Lu, X., Zou, Y., Cai, C., & He, X. (2024) · Structural Health Monitoring · doi:10.1177/14759217241273999 · Cited by 1 · see citing works

Sparse Bayesian Identification of Arx Transfer Functions for Modal Analysis with Uncertainty QuantificationPreprint

Laha, S. K., Swarnakar, B., & Kansabanik, S. K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5347930

An improved robust sparse Bayesian learning method by matrix balancing for accurate identification of structural damage

Li, R., Liu, Z., Song, X., Wang, Y., Yang, F., Zheng, S., & Xiao, Y. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae41da

Accelerated Bayesian Inference of Structural Damage Detection Using a Convolutional Neural Network Surrogate Model

Hou, R., Che, L., Hou, J., Bao, Y., & Xia, Y. (2026) · Structural Control and Health Monitoring · doi:10.1155/stc/1855588

A neural network surrogate model-based time-domain sparse Bayesian learning method for CA mortar void identification of slab track system

Zhang, B., Hu, Q., Liu, Z., & Guan, Y. (2026) · Structural Health Monitoring · doi:10.1177/14759217261478593

Sequential sparse Bayesian broad learning for data assimilation with application to high-speed railway track ageing monitoring

Wang, C., Gao, J., Huang, Y., & Chen, G. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113516

Vibration-Based Structural Damage Detection Using Sparse Bayesian Learning Techniques

Hou, R., Wang, X., & Xia, Y. (2021) · Structural integrity · doi:10.1007/978-3-030-81716-9_1 · Cited by 6 · see citing works

Structural damage detection with two-stage modal information and sparse Bayesian learning

Zou, Y., Yang, G., Lu, X., He, X., & Cai, C. (2023) · Structures · doi:10.1016/j.istruc.2023.105361 · Cited by 17 · see citing works

Sparse Bayesian learning with model reduction for probabilistic structural damage detection with limited measurements

Li, J., Huang, Y., & Asadollahi, P. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.113183 · Cited by 34 · see citing works

Hierarchical sparse Bayesian learning for structural damage detection: Theory, computation and application

Huang, Y., Beck, J. L., & Li, H. (2017) · Structural Safety · doi:10.1016/j.strusafe.2016.09.001 · Cited by 111 · see citing works

Sparse Bayesian learning for damage identification using nonlinear models: Application to weld fractures of steel‐frame buildings

Filippitzis, F., Kohler, M. D., Heaton, T. H., & Beck, J. L. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2870 · Cited by 13 · see citing works