Publication

Book Chapters

  1. Hu, Z., and Mahadevan, S., “Uncertainty in Structural Response Prediction of Composite Structures Subjected to Blast Loading: Modeling, Quantification, and Reduction”, Blast Mitigation Strategies in Marine Composite and Sandwich Structures, Gopalakrishnan, Srinivasan, Rajapakse, Yapa (Eds.), Springer Transactions in Civil and Environmental Engineering, 2018, ISBN 978-981-10-7170-6.
  2. Vega, M., Hu, Z., Yang, Y., Chadha, M., and Todd, M.D., “Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning”, Structural Health Monitoring Based on Data Science Techniques, Alexandre Cury, Diogo Ribeiro, Filippo Ubertini, and Michael D. Todd (Eds.), Springer Book on Data Science, 2021, ISBN: 978-3-030-81716-9.
  3. Zeng, J., Wu, Z., Vega, M., Todd, M.D.*, and Hu, Z.*, “Fast Probabilistic Damage Detection Using Inverse Surrogate Models“, Data-Centric Structural Health Monitoring, Mohammad Noori, Fuh-Gwo Yuan and Ehsan Noroozinejad Farsangi (Eds.), De Gruyter Book on Data-Centric Engineering, 2023, ISBN 978-3-11-079127-3.
  4. Hu, Z.*, Hu, C., and Hu, W., “A Tutorial on Digital Twins for Predictive Maintenance “, Structural Health Monitoring/Management (SHM) in Aerospace Structures, Fuh-Gwo Yuan (Ed.), Elsevier, Woodhead Publishing Series in Composites Science and Engineering, 2024, ISBN 978-0443154768.

Referred Journal Publications (*Corresponding author)

  1. Zeng, J., Gao, Z., Li, Y., Barbat, S., Lu, J., and Hu, Z.*, Fusion of Multiple Data Sources for Vehicle Crashworthiness Prediction Using CycleGAN and Temporal Convolutional Networks, ASME Journal of Mechanical Design, 2024, 147(2).
  2. Zhao, Y., Chadha, M., Barthlow, D., Yeates, E., Mcknight, C., Memarsadeghi, N., Gugaratshan, G., Todd, M., and Hu, Z.*, Physics-enhanced machine learning models for streamflow discharge forecasting, Journal of Hydroinformatics, 2024, DOI: 10.2166/hydro.2024.061.  
  3. Qian, G., Wu, Z., Hu, Z., and Todd, M., Pitting corrosion diagnostics and prognostics for miter gates using multiscale simulation and image inspection data, Structural Health Monitoring, 2024, DOI: 10.1177/14759217241264291.
  4. Qian, G., Zeng, J., Hu, Z., and Todd, M., Bayesian Model Updating of Multiscale Simulations Informing Corrosion Prognostics Using Conditional Invertible Neural Networks, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2024, DOI: 10.1115/1.4065845.
  5. Zeng, Y., Zeng, J., Todd, M., and Hu, Z.*, Data Augmentation Based on Image Translation for Bayesian Inference-Based Damage Diagnostics of Miter Gates, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2024, DOI: 10.1115/1.4065755.
  6. Thelen, A., Huan, X., Paulson, N., Onori, S., Hu. Z., Hu. C., Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives, npj Materials Sustainability, 2024, DOI: 10.1038/s44296-024-00011-1.
  7. Yin, J., Hu, Z., and Du, X., Uncertainty Quantification with Mixed Data by Hybrid Convolutional Neural Network for Additive Manufacturing, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2024, DOI: 10.1115/1.4065444.
  8. Zeng, J., Zhao, Y., Li, G., Gao, Z., Li, Y., Barbat, S., and Hu, Z.*, Vehicle Crashworthiness Performance Prediction through Fusion of Multiple Data Sources, ASME Journal of Mechanical Design, 2023, 11, DOI: 10.1115/1.4064063.
  9. Nemani, V., Biggio, L., Huan, X., Hu, Z., Fink, O., Tran, A., Wang, Y., Zhang, X., and Hu, C., Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial, Mechanical Systems and Signal Processing, 2023, 205(1), 110796.
  10. Wu, Z., Zeng, J., Hu, Z., and Todd, M., Optimization of unmanned aerial vehicle inspection strategy for infrastructure based on model-enabled diagnostics and prognostics, Mechanical Systems and Signal Processing, 2023, 204(1), 110841.
  11. Zhao, Y., Chadha, M., Olsen, N., Yeates, E., Turner, J., Gugaratshan, G., Qian, G., Todd, M.* and Hu, Z.*, 2023. Machine learning-enabled calibration of river routing model parameters. Journal of Hydroinformatics, DOI:10.2166/hydro.2023.030.
  12. Yin, J., Li, L., Mourelatos, Z., Liu, Y., Gorsich, D., Singh, A., Tau, S., and Hu, Z.*, Reliable Global Path Planning of Off-Road Autonomous Ground Vehicles Under Uncertain Terrain Conditions, IEEE Transactions on Intelligent Vehicles, 2023, DOI: 10.1109/TIV.2023.3317833.
  13. Zeng, J., Todd, M., Hu, Z.*, A Recursive Inference Method Based on Invertible Neural Network for Multi-Level Model Updating Using Video Monitoring Data, Mechanical Systems and Signal Processing, 2023, 203(1), 110736.
  14. Yang, Y., Chadha, M., Hu, Z., and Todd, M., An Optimal Sensor Design Framework Accounting for Sensor Reliability Over The Structural Life Cycle, Mechanical Systems and Signal Processing, 2023, 202(1), 110673.
  15. Yin, J., Hu, Z.*, Mourelatos, Z., Gorsich, D., Singh, A., and Tau, S., Efficient Reliability-Based Path Planning of Off-Road Autonomous Ground Vehicles Through the Coupling of Surrogate Modeling and RRT*, IEEE Transactions on Intelligent Transportation Systems, 2023, DOI: 10.1109/TITS.2023.3296651.
  16. Qian, G., Hu, Z., and Todd, M., Physics-based corrosion reliability analysis of miter gates using multi-scale simulations and adaptive surrogate modeling, Mechanical Systems and Signal Processing, 2023, 200(1), 110619.
  17. Najera, D., Qian, G., Hu, Z., and Todd, M., Corrosion Morphology Prediction of Civil Infrastructure Using a Physics-Constrained Machine Learning Method, Mechanical Systems and Signal Processing, 2023, DOI: 10.1016/j.ymssp.2023.110515.
  18. Najera, D., Hu, Z., Chadha, M., and Todd, M., A Physics-Constrained Bayesian Neural Network for Battery Remaining Useful Life Prediction, Applied Mathematical Modelling, 2023, DOI: 10.1016/j.apm.2023.05.038.
  19. Zeng, J., Li, G., Gao, Z., Li, Y., Sundararajan, S., Barbat, S., and Hu, Z.*, Machine learning enabled fusion of CAE data and test data for vehicle crashworthiness performance evaluation by analysis, Structural and Multidisciplinary Optimization, 2023, DOI: 10.1007/s00158-023-03553-5.
  20. Zeng, J., Wu, Z., Todd, M., and Hu, Z.*, Bayes Risk-Based Mission Planning of Unmanned Aerial Vehicles for Autonomous Damage Inspection, Mechanical Systems and Signal Processing, 2023, DOI: 10.1016/j.ymssp.2022.109958.
  21. Qian, G., Tantratian, K., Chen, L., Hu, Z., and Todd, M., A Probabilistic Computational Framework for the Prediction of Corrosion-Induced Cracking in Large Structures, Scientific Reports, 2022, 12, 20898.
  22. Li, J., Zhang, X., Zhou, Q., Chan, F., and Hu, Z., A feature-level multi-sensor fusion approach for in-situ quality monitoring of selective laser melting, Journal of Manufacturing Processes, 2022, DOI: 10.1016/j.jmapro.2022.10.050.
  23. Zeng, J., Todd, M., and Hu, Z.*, Probabilistic damage detection using a new likelihood‑free Bayesian inference method, Journal of Civil Structural Health Monitoring, 2022, DOI: 10.1007/s13349-022-00638-5 .
  24. Zeng, J., and Hu, Z.*, Automated operational modal analysis using variational Gaussian mixture model, Engineering Structures, 2022, DOI: 10.1016/j.engstruct.2022.115139 .
  25. Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B., Todd, M., Mahadevan, S., Hu, C.*, and Hu, Z.*, A Comprehensive Review of Digital Twin–Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives , Structural and Multidisciplinary Optimization, 2022, in press.
  26. Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B., Todd, M., Mahadevan, S., Hu, C.*, and Hu, Z.*, A Comprehensive Review of Digital Twin — Part 1: Modeling and Twinning Enabling Technologies, Structural and Multidisciplinary Optimization, 2022, in press.
  27. Ramancha, M., Vega, M., Conte, J., Todd, M., and Hu, Z., Bayesian model updating with finite element vs surrogate models: Application to a miter gate structural system, Engineering Structures, 2022, 272(1), 114901.
  28. Wu, Z., Fillmore, T., Vega, M., Hu, Z., and Todd, M., Diagnostics and prognostics of multi-mode failure scenarios in miter gates using multiple data sources and a dynamic Bayesian network, Structural and Multidisciplinary Optimization, 2022, 65, 270, DOI: 10.1007/s00158-022-03381-z.
  29. Fillmore, T., Wu, Z., Vega, M., Hu, Z., and Todd, M., A surrogate model to accelerate non-intrusive global–local simulations of cracked steel structures, Structural and Multidisciplinary Optimization, 2022, 65, 208, DOI: 10.1007/s00158-022-03287-w.
  30. Liu, Y., Barthlow, D., Mourelatos, Z., Zeng, J., Gorsich, D., Singh, A., and Hu, Z.*, Mobility Prediction of Off-Road Ground Vehicles Using a Dynamic Ensemble of NARX Models, ASME Journal of Mechanical Design, 2022, DOI:10.1115/1.4054908.
  31. Lin, Q., Zhou, Q., Hu, J., Cheng, Y., and Hu, Z., A Sequential Sampling Approach for Multi-fidelity Surrogate Modeling-Based Robust Design Optimization, ASME Journal of Mechanical Design, 2022, DOI:10.1115/1.4054939.
  32. Zhao, Y., Jiang, C., Vega, M., Todd, M., and Hu, Z.*, Surrogate Modeling of Nonlinear Dynamic Systems: A Comparative Study, ASME Journal of Computing and Information Science in Engineering, 2022, DOI:10.1115/1.4054039.
  33. Papadimitriou, D., Mourelatos, Z., and Hu, Z., Nonlinear Random Vibrations using Second-Order Adjoint and Projected Differentiation Methods, ASME Journal of Vibration and Acoustics, 2022, DOI: 10.1115/1.4054033.
  34. Jiang, C., Vega, M., Ramancha, M., Todd, M., Conte, J., Parno, M., and Hu, Z.*, Bayesian calibration of multi-level model with unobservable distributed response and application to miter gates, Mechanical Systems and Signal Processing, 2022, 170, 108852.
  35. Liu, Y., Jiang, C., Zhang, X., Mourelatos, Z., Barthlow, D., Gorsich, D., Singh, A., and Hu, Z.*, Reliability-Based Multi-Vehicle Path Planning Under Uncertainty Using a Bio-Inspired Approach, ASME-Journal of Mechanical Design, 2022, DOI: 10.1115/1.4053217.
  36. Mahadevan, S., Nath, P., and Hu, Z., Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2022, DOI: 10.1115/1.4053184.
  37. Yang, Y., Chadha, M., Hu, Z., and Todd, M., An optimal sensor placement design framework for structural health monitoring using Bayes risk, Mechanical Systems and Signal Processing, 2022, 168, 108618.
  38. Jiang, C., Vega, M., Todd, M., and Hu, Z.*, Model Correction and Updating of a Stochastic Degradation Model for Failure Prognostics of Miter Gates, Reliability Engineering & System Safety, 2021, DOI:10.1016/j.ress.2021.108203.
  39. Jiang, C., Liu, Y., Mourelatos, Z., Gorsich, D., Fu, Y., and Hu, Z.*, Efficient Reliability-Based Mission Planning of Off-Road Autonomous Ground Vehicles Using an Outcrossing Approach, ASME-Journal of Mechanical Design, 2021, DOI: 10.1115/1.4052511.
  40. Huang, X., Xie, T., Wang, Z., Chen, L., Zhou, Q., and Hu, Z.*, A Transfer Learning-Based Multi-Fidelity Point-Cloud Neural Network Approach for Melt Pool Modeling in Additive Manufacturing, ASCE-ASME Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2021, DOI: 10.1115/1.4051749.
  41. Chadha, M., Hu, Z., and Todd, M., An alternative quantification of the value of information in structural health monitoring, Structural Health Monitoring, 2021, DOI: 10.1177/14759217211028439.
  42. Lin, Q., Hu, J., Zhou, Q., Cheng, Y., Hu, Z., Couckuyt, I., and Dhaene, T., Multi-output Gaussian process prediction for computationally expensive problems with multiple levels of fidelity, Knowledge-Based Systems, 2021, DOI: 10.1016/j.knosys.2021.107151.
  43. Moustafa, K., Hu, Z.*, Mourelatos, Z., Baseski, I., and Majcher, M., System Reliability Analysis Using Component-Level and System-Level Accelerated Life Testing, Reliability Engineering & System Safety, 2021, DOI: 10.1016/j.ress.2021.107755.
  44. Yang, Y., Chadha, M., Hu, Z., Vega, M., Parno, M., and Todd, M., A probabilistic optimal sensor design approach for structural health monitoring using risk-weighted f-divergence, Mechanical Systems and Signal Processing, 2021, 161, 107920.
  45. Vega, M., Hu, Z., Fillmore, T., Smith, M., and Todd, M., A Novel Framework for Integration of Abstracted Inspection Data and Structural Health Monitoring for Damage Prognosis of Miter Gates, Reliability Engineering & System Safety, 2021, DOI: 10.1016/j.ress.2021.107561.
  46. Wei, X., Zhao, J., He, X., Hu, Z., Du, X., and Han, D. , Adaptive Kriging Method for Uncertainty Quantification of the Photoelectron Sheath and Dust Levitation On the Lunar Surface, ASME Journal of Verification, Validation and Uncertainty Quantification, 2021, DOI: 10.1115/1.4050073.
  47. Jiang, C., Hu, Z.*, Mourelatos, Z., Gorsich, D., Jayakumar, P., Fu, Y., and Majcher, M. , R2-RRT*: Reliability-Based Robust Mission Planning of Off-Road Autonomous Ground Vehicle Under Uncertain Terrain Environment, IEEE Transactions on Automation Science and Engineering, 2021, DOI: 10.1109/TASE.2021.3050762.
  48. Wang, Z., Jiang, C., Liu, P., Yang, W., Zhao, Y., Horstemeyer, M., Chen, L., Hu, Z.*, and Chen L.*, Uncertainty quantification and reduction in metal additive manufacturing, npj Computational Materials, 2020, 6, 175.
  49. Papadimitriou, D., Mourelatos, Z., and Hu, Z., Reliability Analysis and Random Vibration of Nonlinear Systems using the Adjoint Method and Projected Differentiation, ASME Journal of Mechanical Design, 2020, DOI: 10.1115/1.4048958.
  50. Liu, Y., Jiang, C., Mourelatos, Z., Gorsich, D., Jayakumar, P., Fu, Y., Majcher, M., and Hu, Z.* , Simulation-Based Mission Mobility Reliability Analysis of Off-Road Ground Vehicles, ASME Journal of Mechanical Design, 2020, DOI:10.1115/1.4048314.
  51. Vega, M., Hu, Z., and Todd, M., Optimal Maintenance Decisions for Deteriorating Quoin Blocks in Miter Gates Subject to Uncertainty in the Condition Rating Protocol, Reliability Engineering & System Safety, 2020, DOI: 10.1016/j.ress.2020.107147.
  52. Nie, S., Guo, M., Yin, F., Ji, H., Ma, Z., Hu, Z., and Zhou, X., Research on fluid-structure interaction for piston/cylinder tribopair of seawater hydraulic axial piston pump in deep-sea environment, Ocean Engineering, 2020, DOI: 10.1016/j.oceaneng.2020.108222.
  53. Huang, X., Lei, Q., Xie, T., Zhang Y., Hu, Z., and Zhou Q., Deep Transfer Convolutional Neural Network and Extreme Learning Machine for lung nodule diagnosis on CT images, Knowledge-Based Systems, 2020, DOI: 10.1016/j.knosys.2020.106230.
  54. Jiang, C., Hu, Z.*, Liu, Y., Mourelatos, Z., Gorsich, D., and Jayakumar, P., A Sequential Calibration and Validation Framework for Model Uncertainty Quantification and Reduction, Computer Methods in Applied Mechanics and Engineering, 2020, Vol. 368, 113172.
  55. Zhang, X., Hu, Z., and Mahadevan, S., Bilevel Optimization Model for Resilient Configuration of Logistics Service Centers, IEEE Transactions on Reliability, 2020, DOI: 10.1109/TR.2020.2996025.
  56. Li, M., Sadoughi, M., Hu, Z., Hu, C., A Hybrid Gaussian Process Model for System Reliability Analysis, Reliability Engineering & System Safety, 2020, In press.
  57. Chen, X.*, Su, W.*, Kavousifard, A., Skowronska, A., Mourelatos, Z., and Hu, Z.*, Resilient Microgrid System Design for Disaster Impact Mitigation, Sustainable and Resilient Infrastructure, 2020, in press.
  58. Yin, F., Nie, S., Xu, W., Ji, H., Xu, Y., Hu, Z., Guo, M., and Zhou, X., Investigation of the Tribological Behaviour of Cermets Sliding Against Si3N4 for Seawater Hydraulic Components Applications, Surface Topography: Metrology and Properties, 2020, in press.
  59. Papadimitriou, D., Mourelatos, Z., Patil, S., Hu, Z., Tsianika, V., and Geroulas, V., Reliability Analysis on Nonlinear Vibratory Systems Under Non-Gaussian Loads Using a Sensitivity-Based Propagation of Moments, ASME Journal of Mechanical Design, 2020, in press.
  60. Moustafa, K., Hu, Z.*, Mourelatos, Z., Baseski, I., and Majcher, M. Resource Allocation for System Reliability Assessment Using Accelerated Life Testing, ASME Journal of Mechanical Design, 2019, in press.
  61. Hu, Z., Mahadevan, S., Reliability Analysis of a Hypersonic Vehicle Panel with Spatio-Temporal Variability, AIAA Journal, 2019, in press.
  62. Hu, Z.*, Mourelatos, Z., Gorsich, D., Jayakumar, P., and Majcher, M. Testing Design Optimization for Uncertainty Reduction in Generating Off-Road Mobility Map Using a Bayesian Approach, ASME Journal of Mechanical Design, 2019, doi:10.1115/1.4044111.
  63. Wang, Z., Liu, P., Ji, Y., Mahadevan, S., Horstemeyer, M., Hu, Z.*, Chen, L.*, and Chen L-Q, Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-Informed Data-Driven Modeling, JOM: Journal of the Minerals, Metals, and Materials Society, 2019, doi:10.1007/s11837-019-03555-z.
  64. Wang, Z., Liu, P., Xiao, Y., Cui, X., Hu, Z.*, and Chen, L.*, A Data-driven Approach for Process Optimization of Metallic Additive Manufacturing under Uncertainty, ASME Journal of Manufacturing Science and Engineering, 2019, doi:10.1115/1.404379.
  65. Nath, P., Hu, Z., and Mahadevan, S., Uncertainty quantification of grain morphology in laser direct metal deposition, Modelling and Simulation in Materials Science and Engineering, 2019, https://doi.org/10.1088/1361-651X/ab1676.
  66. Hu, Z., and Mahadevan, S., Probability Models for Data-Driven Global Sensitivity Analysis, Reliability Engineering & System Safety, 2019, DOI:10.1016/j.ress.2018.12.003.
  67. Liu, Y., Zhao, Y., Hu, Z.*, Mourelatos, Z., and Papadimitriou, D., Collision-Avoidance Reliability Analysis of Automated Vehicle Based on Adaptive Surrogate Modeling, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2019, doi:10.1115/1.4042974.
  68. Drignei, D., Mourelatos, Z., and Hu, Z., Uncertainty quantification of complement sensitivity indices in dynamic computer models, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2019, doi:10.1115/1.4042924.
  69. Koutsellis, T., Mourelatos, Z., and Hu, Z., Numerical Estimation of Expected Number of Failures for Repairable Systems using a Generalized Renewal Process Model, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2019, doi:10.1115/1.4042848.
  70. Hu, Z., Hu, C., Mourelatos, Z., and Mahadevan, S., Model Discrepancy Quantification in Simulation-based Design of Dynamical Systems, ASME Journal of Mechanical Design, 2018, doi:10.1115/1.4041483.
  71. Li, M., Sadoughi, M., Hu, C., Hu, Z., Eshghi, A., and Lee, S., High-Dimensional Reliability-based Design Optimization Involving Highly Nonlinear Constraints and Computationally Expensive Simulations, ASME Journal of Mechanical Design, 2018.
  72. Neal, K., Hu, Z., Mahadevan, S., and Zumberge, J., Discrepancy Prediction in Dynamical System Models under Untested Input Histories, ASME Journal of Computational and Nonlinear Dynamics, 2018, doi:10.1115/1.4041238.
  73. Hu, Z., and Mourelatos, Z., A Sequential Accelerated Life Testing Framework for System Reliability Assessment With Untestable Components, ASME Journal of Mechanical Design, 2018, 140(10), 101401.
  74. Hu, Z., and Mourelatos, Z., Efficient Global Surrogate Modeling Based on Multi-Layer Sampling, SAE International Journal of Materials and Manufacturing, 2018, in press.
  75. Papadimitriou, D., Mourelatos, Z., and Hu, Z., Reliability Analysis using Second-Order Saddlepoint Approximation and Mixture Distributions, ASME Journal of Mechanical Design, 2018, doi:10.1115/1.4041370.  
  76. Hu, Z., and Mahadevan, S., Bayesian Network Learning for Data-Driven Design, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2018, doi:10.1115/1.4039149.
  77. Hu, Z., and Mahadevan, S., Adaptive Surrogate Modeling for Time-Dependent Multidisciplinary Reliability Analysis, ASME Journal of Mechanical Design, 2017, doi:10.1115/1.4038333.
  78. Hu, Z.,  Zhu, Z., and Du, X., Time-Dependent Reliability Analysis for Bivariate Responses, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2017, doi:10.1115/1.4038318, in press  
  79. Hu, Z., Mahadevan, S., and Ao, D., Uncertainty aggregation and reduction in structure–material performance prediction, Computational Mechanics, 2017, DOI 10.1007/s00466-017-1448-6.
  80. Hu, Z., and Mahadevan, S., A surrogate modeling approach for reliability analysis of a multidisciplinary system with spatio-temporal output, Structural and Multidisciplinary Optimization, 2017, DOI:10.1007/s00158-017-1737-x.  
  81. Hu, Z., and Mahadevan, S., Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities, The International Journal of Advanced Manufacturing Technology, 2017, DOI:10.1007/s00170-017-0703-5.
  82. Nath, P., Hu, Z., and Mahadevan, S., Sensor Placement for Calibration of Spatially Varying Model Parameters, Journal of Computational Physics, 2017, http://doi.org/10.1016/j.jcp.2017.04.033
  83. Ao, D., Hu, Z., and Mahadevan, S., Design of Validation Experiments for Life Prediction Models, Reliability Engineering & System Safety, 2017, http://dx.doi.org/10.1016/j.ress.2017.03.030.
  84. Hu, Z., Ao, D., and Mahadevan, S., Calibration Experimental Design Optimization Considering Field Response and Model Uncertainty, Computer Methods in Applied Mechanics and Engineering, 2017, http://dx.doi.org/10.1016/j.cma.2017.01.007.  
  85. Ao, D., Hu, Z., and Mahadevan, S., Dynamics Model Validation Using Time-Domain Metrics, ASME Journal of Verification, Validation and Uncertainty Quantification, 2017, doi:10.1115/1.4036182.
  86. Hu, Z., Nannapaneni, S., and Mahadevan, S.,Efficient Kriging surrogate modeling approach for system reliability analysis , Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2017, In press.
  87. Hu, Z., and Mahadevan, S., Uncertainty Quantification in Prediction of Material Properties During Additive Manufacturing, Scripta Materialia, 2016, Doi:10.1016/j.scriptamat.2016.10.014
  88. Hu, Z., and Mahadevan, S., Time-Dependent Reliability Analysis Using a Vine-ARMA Load Model, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 3(1), 2017, pp.011007.
  89. Devathi, H., Hu, Z., and Mahadevan, S.,Snap-Through Buckling Reliability Analysis Under Spatiotemporal Variability and Epistemic Uncertainty, AIAA Journal, 2016, DOI: 10.2514/1.J054920.
  90. Hu, Z., and Mahadevan, S., Resilience Assessment Based on Time-Dependent System Reliability Analysis, ASME Journal of Mechanical Design, 2016, DOI: 10.1115/1.4034109.
  91. Nannapaneni, S., Hu, Z., and Mahadevan, S., Uncertainty quantification in reliability estimation with limit state surrogates, Structural and Multidisciplinary Optimization, 2016, DOI: 10.1007/s00158-016-1487-1.
  92. Hu, Z., and Mahadevan, S., A Single-Loop Kriging Surrogate Modeling for Time-Dependent Reliability Analysis, ASME Journal of Mechanical Design, 138.6 (2016): 061406.
  93. Hu, Z., Mahadevan, S., and Du, X., Uncertainty Quantification of Time-Dependent Reliability Analysis in the Presence of Parametric Uncertainty, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2015, doi:10.1115/1.4032307, in press.
  94. Hu, Z., and Mahadevan, S., Accelerated Life Testing (ALT) Design Based on Computational Reliability Analysis, Quality and Reliability Engineering International, 2015, DOI: 10.1002/qre.1929.
  95. Hu, Z., and Du, X., A Random Field Approach to Reliability Analysis With Random and Interval Variables, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2015;1(4):041005-041005-11. doi:10.1115/1.4030437.
  96. Hu, Z., and Du, X.,  Reliability-Based Design Optimization Under Stationary Stochastic Process Loads, Engineering Optimization, 2015, DOI:10.1080/0305215X.2015.1100956.
  97. Hu, Z., and Mahadevan, S., Global Sensitivity Analysis-Enhanced Surrogate (GSAS) Modeling for Reliability Analysis, Structural and Multidisciplinary Optimization, 2015, DOI10.1007/s00158-015-1347-4.
  98. Hu, Z., and Mahadevan, S., Time Dependent System Reliability Analysis Using Random Field Discretization, ASME Journal of Mechanical Design, 137 (10), 101404, 2015.
  99. Hu, Z., and Du, X.,  Mixed Efficient Global Optimization for Time- Dependent Reliability Analysis,  ASME Journal of Mechanical Design, 137(5), 2015, pp.051401.
  100. Hu, Z., and Du, X., First order reliability method for time-variant problems using series expansions, Structural and Multidisciplinary Optimization, 51(1), 2014, pp.1-21.
  101. Hu, Z., and Du, X.,Time-dependent reliability analysis with joint upcrossing rates, Structural and Multidisciplinary Optimization, 2013, in press, DOI 10.1007/s00158-013-0937-2.
  102. Hu, Z., and Du, X., Lifetime cost optimization with time-dependent reliability, Engineering Optimization, 2013, in press, DOI: 10.1080/0305215X.2013.841905.
  103. Hu, Z., and Du, X., Reliability analysis for hydrokinetic turbine blades, Renewable Energy, 48, 251–262, 2012.
  104. Du, X. and Hu, Z., First Order Reliability Method With Truncated Random Variables,  ASME Journal of Mechanical Design, 134 (9), 091005, 2012.
  105. Hu, Z., and Du, X., A Sampling Approach to Extreme Value Distribution for Time-Dependent Reliability Analysis, ASME Journal of Mechanical Design, 135(7), 2013.
  106. Hu, Z., and Du, X., Simulation-based time-dependent reliability analysis for composite hydrokinetic turbine blades, Structural and Multidisciplinary Optimization, 47(5), 765-781, 2013.
  107. Hu, Z., and Du, X., Robust design with imprecise random variables and its application in hydrokinetic turbine optimization, Engineering Optimization, 2013, in press, DOI: 10.1080/0305215X.2013.772603.
  108. Hu, Z., and Du, X., Fatigue reliability analysis for structures with known loading trend, Structural and Multidisciplinary Optimization, 2013, in press, DOI 10.1007/s00158-013-1044-0.  
  109. Zhang, X., Hu, Z. and Du, X., Probabilistic Inverse Simulation and Its Application in Vehicle Accident Reconstruction, ASME Journal of Mechanical Design, 135 (12), 121006, 2013.
  110. Li, H., Hu, Z., Chandrashekhara, K., Du, X., & Mishra, R, Reliability-based fatigue life investigation for a medium-scale composite hydrokinetic turbine blade, Ocean Engineering 89: 230-242, 2014.
  111. Nie S.L., Hu Z., Li Y.P. and Huang G.H., Non-linear programming for filter management in a fluid power system with uncertainty, Proc. Instn. Mech. Engrs., Part A, Journal of Power and Energy (Institution of Mechanical Engineers, UK), 2010, v224 (2), 185-201.
  112. Nie S.L., Hu Z., Li Y.P. and Huang G.H., Improved IFQP for Filters Management in Fluid Power System under Uncertainty, Proc. Instn. Mech. Engrs., Part E, Journal of Process Mechanical Engineering, (Institution of Mechanical Engineers, UK), 2010, v224 (2), 103-118.
  113. Nie S.L., Ji H., Huang Y, Hu Z. and Li Y.P., Robust interval-based minimax-regret analysis method for filter management of fluid power system, Asia-Pacific Journal of Operational Research, 30 (6), 1350021, 2013.
  114. Zheng Y., Nie S.L., H. Ji, and Hu Z., “Application of a Fuzzy Programming through Stochastic Particle Swarm Optimization to Assessment of Filter Management Strategies in Fluid Power System under Uncertainty”, Journal of Optimization Theory and Applications, 157(1), 276-286, 2013.  
  115. Nie S.L., Hu B., Li Y.P., Hu Z. and Huang G.H., An interval-fuzzy parameter integer nonlinear programming method for identification of filter allocation and replacement strategies in fluid power system under uncertainty,International Journal of Systems Science, 2011, v42 (3), 429-448.
  116. Hu, Z, Nie, S.L. Liu, W. and Hu, Z.W, The optimization of fluid power system filter allocation based on the minimax-regret analysis, Chinese journal of hydraulics and pneumatics, 2011, v2, 100-104. (In Chinese).
  117. Ruan, J., Nie, S.L., Liu, W. and Hu, Z, Research on the high-pressure angle of the pre-compression valve plate,Chinese journal of hydraulics and pneumatics, 2010, v9, 74-77. (In Chinese).
  118. Hu, Z.W, Nie, S.L., Ruan, J. and Hu, Z, Research on inner-leakage of water hydraulic piloted relief valve,Chinese journal of hydraulics and pneumatics, 2010, v5, 80-83. (In Chinese)

Referred Conference Publications (Not updated here, please see my google scholar for my conference papers)

https://scholar.google.com/citations?user=Xd7WFRcAAAAJ&hl=en&oi=ao

  1. Hu, Z., Zhu, Z., and Du, X., “Time-dependent reliability analysis for bivariate responses“, The ASME 2015 International Mechanical Engineering Congress and Exposition (IMECE), November 13-19, 2015 in Houston, TX.
  2. Hu, Z. and Du, X., “Efficient Global Optimization Reliability analysis for time-dependent limit-state functions,” The ASME 2014 International Design Engineering Technical Conferences (IDETC) and Computers and Information in Engineering Conference (CIE), August 17-20, 2014 in Buffalo, NY.
  3. Hu, Z. and Du, X., “A Design Oriented Reliability Analysis Methodology for Fatigue Life of Structures under Stochastic Loadings,” The ASME 2013 International Design Engineering Technical Conferences (IDETC) and Computers and Information in Engineering Conference (CIE), August 4-7, 2013 in Portland, OR.
  4. Zhang, X., Hu, Z. and Du, X., “Probabilistic Inverse Simulation and Its Application in Vehicle Accident Reconstruction” The ASME 2013 International Design Engineering Technical Conferences (IDETC) and Computers and Information in Engineering Conference (CIE), August 4-7, 2013 in Portland, OR.  
  5. Kolekar, S.N., Hu, Z., Banerjee, A., and Du, X., “Hydrodynamic Design and Optimization of Hydro-kinetic Turbines using a Robust Design Method” Marine Energy Technology Symposium (METS), 2013.
  6. Hu, Z. and Du, X., “RELIABILITY ANALYSIS FOR HYDROKINETIC TURBINE BLADES UNDER RANDOM RIVER VELOCITY FIELD”, Proceedings of the 7th Annual ISC Research Symposium, ISCRS 2013, April 23, 2013, Rolla, Missouri.
  7. Hu, Z. and Du, X., “Time-Dependent Reliability Analysis by A Sampling Approach to Extreme Values of Stochastic Processes,” The ASME 2012 International Design Engineering Technical Conferences (IDETC) and Computers and Information in Engineering Conference (CIE), August 12-15, 2012 in Chicago, IL.