Publications

Refereed Journal Articles (* first or corresponding author)

[J1]. Zhang, Y., Zhou, K., and Tang, J., 2024, “Harnessing collaborative Learning Automata to guide multi-objective optimization for structural damage identification using piezoelectric admittance measurement,” Applied Soft Computing, 160, 111697. Link

[J2]. Zhang, Y., Zhou, K., and Tang, J., 2024, “Piezoelectric impedance-based  high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis,"  Mechanical Systems and Signal Processing, 209, 111093. Link

[J3]. Su, Y., Shi, L., Zhou, K., Bai, G., and Wang, Z., 2024, “Knowledge-reinforced deep networks for bearing fault diagnosis,"  Reliability Engineering & System Safety, 244, 109863. Link

[J4]. Shi, L., Zhou, K., and Wang, Z., 2024, ”Convolutional dimension-reduction with knowledge reasoning for reliability approximations of structures under high-dimensional spatial uncertainties,” ASME Journal of Mechanical Design, 146(7): 071701. Link

[J5]. Liang, M., and Zhou, K.*, 2023, ”Joint loss learning enabled semi-supervised autoencoder for bearing fault diagnosis under limited labeled vibration signals,” Journal of Vibration and Control, in press. Link

[J6]. Zhou, K.*, Wang, Z., Gao, Q., Yuan, S., and Tang, J., 2023, ”Recent advances in uncertainty quantification in structural response characterization and system identification,” Probabilistic Engineering Mechanics, 74, 103507, Link

[J7]. Zhou, K.*, and Tang, J., 2023, “A wavelet neural network informed by time-domain signal preprocessing for bearing remaining useful life prediction,” Applied Mathematical Modeling, 122, 220-241.Link

[J8]. 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

[J9]. 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

[J10]. 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, 126, 49–66. Link

[J11]. 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

[J12]. 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, 66, 16. Link

[J13]. Zhou, K.*, 2022, “Enhanced feature extraction for machinery condition monitoring using recurrence plot and quantification measure,” The International Journal of Advanced Manufacturing Technology. 123, 3421–3436. Link

[J14]. 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

[J15]. 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)

[J16]. 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, 29(13-14), 3165-3174.  Link

[J17]. 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

[J18]. 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

[J19]. 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

[J20]. 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

[J21]. 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

[J22]. 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

[J23]. 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

[J24]. 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

[J25]. 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

[J26]. 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

[J27]. 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

[J28]. 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

[J29]. 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

[J30]. Zhou, K.*, and Wu, Z.Y., 2017, “Strain gauge placement optimization for structural performance assessment,” Engineering Structures, 141(15), 184-197. Link

[J31]. 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

[J32]. 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

[J33]. 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

[J34]. 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

[J35]. 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

[J36]. 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

[J37]. 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

[J38]. 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

Refereed Conference Proceedings (* presenter)

[C1]. Zhang, Y., Zhou, K., and Tang, J., 2024, “Non-probabilistic reliability model for structural damage identification under uncertainty with reduced model,” Proceedings of the SPIE, Smart Structures/ NDE, V12951,1295115. Link

[C2]. Pan, H., Wang, Z., and Zhou, K.*, 2024, “Enhanced seismic wave attenuation in graded seismic metamaterials with novel unit cells,” Proceedings of the SPIE, Smart Structures/ NDE, V12946, 129460R. Link

[C3]. Liu, Y., Zou, S., Gao, Q., and Zhou, K.*, 2024, “Physics-guided data-driven failure identification of underwater mooring systems in offshore infrastructures,” Proceedings of the SPIE, Smart Structures/ NDE, V12951, 1295110. Link

[C4]. Lu, J., Gao, Q., and Zhou, K.*, 2024, “Robotic-enabled inspection of wind turbine blades using image deblurring and deep learning segmentation,” Proceedings of the SPIE, Smart Structures/ NDE, V12951, 129510L. Link

[C5]. Tan, N., Woo, D., Zhou, K., Kazoleas, C., Zhang, J., and Yuan, S., 2024, "On-orbit dynamic thermal modeling of large deployable mesh reflectors," AIAA SciTech Forum, 2024-2040. Link

[C6]. Zhang, Y., Zhou, Q., Zhou, K., and Tang, J., 2023, “Damage detection of a pressure vessel with smart sensing and deep learning,” Proceedings of the MECC (Modeling, Estimation and Control Conference), 56(3), 379-384. Link

[C7]. Zhou, K.*, Zhang, Y., and Tang, J., 2023, “Semi-supervised autoencoder with joint loss learning for bearing fault detection,” Proceedings of the ASME IDETC/CIE, V012T12A021. Link

[C8]. 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

[C9]. 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 

[C10]. 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

[C11]. 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

[C12]. 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

[C13]. 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

[C14]. 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, V002T39A002.  Link

[C15]. Zhou, K.*, and Tang, J., 2020, “Fuzzy classification of gear fault using principal component analysis-based fuzzy neural network,” Proceedings of the ASME ISFA, V001T08A003.  Link

[C16]. Xu, D., Zhou, K., and Tang, J., 2020, “Application of MOSA algorithm in gleeble testing model updating,” Proceedings of the ASME ISFA,  V001T08A006.  Link

[C17]. 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

[C18]. Wu, Z. Y., Zhou, K., Shenton, H. W., and Chajes, M. J., 2017, "Validating strain gauge placement methods for structural health monitoring of large cable support bridge", 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, ISHMII, 414-422. Link 

[C19]. 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

[C20]. 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

[C21]. 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

[C22]. 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

[C23]. 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

[C24]. Zhou, K.*, and Tang, J., 2014, “Robust optimization towards reducing response variation by efficient NURBS finite element inverse analysis,” Proceedings of JSME MOVIC. Link

[C25]. 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

[C26]. Zhou, K.*, and Tang, J., 2013, “Perturbing structural design towards minimizing variation in vibratory response,” Proceedings of ASME DSCC, V2, V002T18A005. Link

[C27]. Zhou, K.*, and Tang, J., 2013, “Design modification of cyclically periodic structure using Gaussian process,” Proceedings of SPIE, Smart Structures/ NDE, V8694, 869411. Link

[C28]. 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

[C29]. 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

[C30]. 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

Conference Abstract and Poster

[CA1]. Dykstra, G., Reynolds, B., Smith, R., Zhou, K., and Liu, Y., 2022, “Machine learning assisted development of electrochemical cortisol sensor based on electropolymerized molecularly imprinted polymer,” AIChE Annual Meeting.

[CA2]. Mavani, D., Zhou, K., and Zou, S., 2024, “A robust linear quadratic control for docking and charging unmanned underwater vehicles powered by wave energy,” UMERC+METS.

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