Journal Articles

[J1]. Zhou, K., Wang, Z., Ni, Y.Q., Zhang, Y., and Tang, J., 2023 “Unmanned aerial vehicle-based computer vision for structural vibration measurement and condition assessment: A concise survey,” Journal of Infrastructure Intelligence and Resilience, 2(2), 100031, Link.

[J2]. Cao, P., Zhang, S., Wang, Z., and Zhou, K., 2023, “Damage identification using piezoelectric electromechanical impedance: A brief review from a numerical framework perspective”, Structures, 50, 1906-1921, Link

[J3]. Ball, A., Zhou, K., Xu, D., Zhang, D., and Tang, J., 2023 “Directed Gaussian process metamodeling with improved Firefly Algorithm (iFA) for composite manufacturing uncertainty propagation analysis,” The International Journal of Advanced Manufacturing Technology. Link.

[J4]. Zhou, K., Edward D., and Tang, J., 2023, “Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations”, Mechanical Systems and Signal Processing, 185, 109772. Link

[J5]. Cao, P., Zhang, Y., Zhou, K., and Tang, J., 2023, “A reinforcement learning hyper-heuristic in multi-objective optimization with application to structural damage identification,” Structural and Multidisciplinary Optimization. Link

[J6]. Zhou, K., 2022, “Enhanced feature extraction for machinery condition monitoring using recurrence plot and quantification measure,” The International Journal of Advanced Manufacturing Technology. Link

[J7]. Zhou, K., Zhang, Y., Shuai, Q., and Tang, J., 2022, “Probabilistic multi-objective inverse analysis for damage identification using piezoelectric impedance measurement under uncertainties,” Frontiers in Built Environment, 86. Link

[J8]. Dykstra, G., Reynolds, B., Smith, R., Zhou, K., and Liu, Y., 2022, “Electropolymerized molecularly imprinted polymer synthesis guided by an integrated data-driven framework for cortisol detection,” ACS Applied Materials & Interfaces, 14(22), 25972–25983. Link (selected as supplementary journal cover)

[J9]. Liang, M., and Zhou, K., 2022, “A hierarchical deep learning framework for combined rolling bearing fault localization and identification with data fusion,” Journal of Vibration and Control, in press. Link

[J10]. Zhou, K., Enos, R., Xu, D., Zhang, D., and Tang, J., 2022, “Hierarchical multi-response Gaussian processes for uncertainty analysis with multi-scale composite manufacturing simulation,” Computational Materials Science, 207, 111257. Link

[J11]. Zhou, K., Enos, R., Zhang, D., and Tang, J., 2022, “Uncertainty analysis of spring-in angles associated with composites manufacturing utilizing physics-guided Gaussian process meta-modeling,” Composite Structures, 280, 114816. Link

[J12]. Liang, M., and Zhou, K., 2022, “Probabilistic bearing fault diagnosis using Gaussian process with tailored feature extraction,” The International Journal of Advanced Manufacturing Technology, 119, 2059-2076. Link

[J13]. Zhou, K., and Liu, Y., 2021, “Early-stage gas identification using convolutional long short-term neural network with sensor array time series data,” Sensors, 21(14), 4826. Link

[J14]. Zhou, K., Sun, H., Enos, R., Zhang, D., and Tang, J., 2021, Harnessing deep learning for physics-informed prediction of composite strength with microstructural uncertainties,” Computational Materials Science, 107, 110663. Link

[J15]. Zhou, K., and Tang, J., 2021, “Harnessing fuzzy neural network for gear fault diagnosis with limited data labels,” The International Journal of Advanced Manufacturing Technology, 115, 1005-1019. Link

[J16]. Zhou, K., and Tang, J., 2021, “Computational inference of vibratory system with incomplete modal information using parallel, interactive and adaptive Markov chain Monte Carlo,” Journal of Sound and Vibration, 511, 116331. Link

[J17]. Miller, L., Zhou, K., Tang, J., Frame, L., Sheeley, C., Hebert, R., Narayan, L.R., Alpay, P., and Kim, J., 2021, “Thermomechanical finite element simulation and correlation analysis for orthogonal cutting of normalized AISI 9310 steels,” The International Journal of Advanced Manufacturing Technology, V114(11-12), 3337-3356. Link

[J18]. Zhou, K., and Tang, J., 2021, “Characterization of dynamic response variation using multi-fidelity data fusion through composite neural network,” Engineering Structures, 232, 111878. Link

[J19]. Zhou, K., and Tang, J., 2021, “Structural model updating using adaptive multi-response Gaussian process meta-modeling,” Mechanical Systems and Signal Processing, 147, 107121. Link

[J20]. Zhou, K., and Tang, J., 2021, “Uncertainty quantification of mode shape variation utilizing multi-level multi-response Gaussian process,” ASME Journal of Vibration and Acoustics, 143(1), 011003. Link

[J21]. Wu, Z.Y., Zhou, K., Harry W. Shenton III and Michael J. Chajes, 2019, “Development of sensor placement optimization tool and application to large cable support bridge,” Journal of Civil Structural Health Monitoring, 9(1), 77-90. Link

[J22]. Zhou, K., and Tang, J., 2018, “Uncertainty quantification in structural dynamic analysis using two-level Gaussian processes and Bayesian inference,” Journal of Sound and Vibration, 412(6), 95-115. Link

[J23]. Zhou, K., and Wu, Z.Y., 2017, “Strain gauge placement optimization for structural performance assessment,” Engineering Structures, 141(15), 184-197. Link

[J24]. Shuai, Q., Zhou, K., Zhou, S., and Tang, J., 2017, “Fault identification using piezoelectric impedance measurement and model-based intelligent inference with pre-screening,” Smart Materials and Structures, 26(4), 045007. Link

[J25]. Zhou, K., Wu, Z.Y., Yi, X.H., Zhu, D.P., Narayan, R., and Zhao, J., 2017, “Generic framework of sensor placement optimization for structural health monitoring,” ASCE Journal of Computing in Civil Engineering, 31(4), 04017018. Link

[J26]. Zhou, K., Hedge, A., Cao, P., and Tang, J., 2016, “Design optimization towards alleviating forced response variation in cyclically periodic structure using Gaussian Process,” ASME Journal of Vibration and Acoustics, 139(1), 011017. Link

[J27]. Zhou, K., and Tang, J., Christenson, R., 2016, “Rapid identification of properties of column-supported bridge-type structure by using vibratory response,” Journal of Vibration and Control, 22(5), 1415-1430. Link

[J28]. Zhou, K., Liang, G., and Tang, J., 2016, “Component mode synthesis order-reduction for dynamic analysis of structure modeled with NURBS finite element,” ASME Journal of Vibration and Acoustics, 138(2), 021016. Link

[J29]. Zhou, K., and Tang, J., 2016, “Highly efficient probabilistic finite element model updating using intelligent inference with incomplete modal information,” ASME Journal of Vibration and Acoustics, 138(5), 051016. Link

[J30]. Zhou, K., and Tang, J., 2015, “Reducing dynamic response variation using NURBS finite element-based geometry perturbation,” ASME Journal of Vibration and Acoustics, 137(6), 061008. Link

[J31]. Liu, Y., Zhou, K., and Lei, Y., 2015, “Using Bayesian inference framework towards identifying gas species and concentration from high temperature resistive sensor array data,” Journal of Sensors, V2015, 351940. Link

Conference Proceedings

[C1]. Zhang, Y., Dupont, J., Wang, T., Zhou, K., and Tang, J., 2023, “Structural fault identification using piezo-electric impedance sensing with enhanced optimization and enriched measurements,” Proceedings of the SPIE, Smart Structures/ NDE, V12486, 1248610. Link

[C2]. Zhou, K., and Tang, J., 2022, “Probabilistic gear fault diagnosis using Bayesian convolutional neural network,” Proceedings of the MECC (Modeling, Estimation and Control Conference), 55(37), 795-799. Link 

[C3]. Zhou, K., Zhang, Y., and Tang, J., 2022, “Order-reduced modeling-based multi-level damage identification using piezoelectric impedance measurement”, Proceedings of the MoViC (International Conference on Vibration and Control), 55(27), 341-346. Link.

[C4]. Zhou, K., 2022, “A deep long short-term memory network for bearing fault diagnosis under time-varying condition,” Proceedings of the ASME IDETC/CIE, V010T10A016. Link

[C5]. Zhang, Y., Zhou, K., and Tang, J., 2022, “Memetic optimizer for structural damage identification using electromechanical admittance”, Proceedings of the ASME IDETC/CIE, V010T10A023. Link.

[C6]. Zhang, Y., Zhou, K., and Tang, J., 2022, “Structural damage identification using inverse analysis through optimization with sparsity,” Proceedings of SPIE, Smart Structures/ NDE, V12046, 1204606. Link

[C7]. Zhou, K., and Tang, J., 2020, ”Physics based multi-fidelity fusion for efficient characterization of mode shape variation under uncertainties,” Proceedings of the ASME DSCC. Link

[C8]. Zhou, K., and Tang, J., 2020, “Fuzzy classification of gear fault using principal component analysis-based fuzzy neural network,” Proceedings of the ASME ISFA. Link

[C9]. Xu, D., Zhou, K., and Tang, J., 2020, “The application of MOSA algorithm in gleeble testing model updating,” Proceedings of the ASME ISFA. Link

[C10]. Zhou, K., Li, R., Chen, W.J., Tang, J., and Zhang, D., 2019, “Uncertainty quantification of the dimensional variations of a curved composite flange,” American Society of Composites (ASC) 34th Technical Conference. Link

[C11]. Zhou, K., Liang, G., and Tang, J., 2016, “Vibration analysis of structure with uncertainty using two-level Gaussian processes and Bayesian inference”, Proceedings of MOVIC RASD. Link

[C12]. Zhou, K., Wu, Z.Y., and Cashany, M., 2016, “Accelerated finite element model calibration by substructure analysis with parallel genetic algorithm optimization,” 8th International Conference on Bridge Maintenance & Safety & Management. Link

[C13]. Zhou, K., Cao, P., and Tang, J., 2015, “Efficient uncertainty quantification in structural dynamic analysis using two-level Gaussian processes,” Proceedings of ASME IDETC/CIE, V8, V008T13A102. Link

[C14]. Shuai, Q., Zhou, K., and Tang, J., 2015, “Structural damage identification using piezoelectric impedance and Bayesian inference,” Proceedings of SPIE, Smart Structures/ NDE, V9435, 94351R. Link

[C15]. Zhou, K., Shuai, Q., and Tang, J., 2014, “Adaptive damage detection using tunable piezoelectric admittance sensor and intelligent inference,” Proceedings of ASME SMASIS, V1, V001T05A007. Link

[C16]. Zhou, K., and Tang, J., 2014, “Robust optimization towards reducing response variation by efficient NURBS finite element inverse analysis,” Proceedings of JSME MOVIC. Link

[C17]. Zhou, K., Liang, G., and Tang, J., 2014, “Efficient model updating using Bayesian probabilistic framework based on measured vibratory response,” Proceedings of SPIE, Smart Structures/ NDE, V9063, 90631T. Link

[C18]. Zhou, K., and Tang, J., 2013, “Perturbing structural design towards minimizing variation in vibratory response,” Proceedings of ASME DSCC, V2, V002T18A005. Link

[C19]. Zhou, K., and Tang, J., 2013, “Design modification of cyclically periodic structure using Gaussian process,” Proceedings of SPIE, Smart Structures/ NDE, V8694, 869411. Link

[C20]. Zhou, K., and Tang, J., 2012, “Towards alleviating vibration response variation based on reduced order modeling and analysis,” Proceedings of ASME DSCC, V3, pp.701-708. Link

[C21]. Zhou, K., Tang, J., and Christenson, R., 2012, “Rapid identification of structural properties based on mass response method,” Proceedings of SPIE, Smart Structures/ NDE, V8347, 834703. Link

[C22]. Christenson, R., Tang, J., Zhou, K., and Kim, S.J.., 2011, “Rapid and robust evaluation of bridge load-carrying capacity post-disaster,” The DHS Science Conference. Link

Technical Report

[R1]. Zhou, K., Cashany, M., and Wu, Z.Y., 2015, “Substructure analysis framework for accelerated finite element modeling,” Technical Report, Bentley Systems, Incorporated, Applied Research Group, Watertown, CT, USA. (41 pages) Link

Dataset

[D1]. Zhou, K., and Tang, J., 2022, “Gear Dataset”, Mendeley Data, V1, DOI: 10.17632/87y47nvsf4.1. Link