CERL

Publications

Book Chapters

  1. Hu, Z., Hu, C. and Hu, W., 2024. A tutorial on digital twins for predictive maintenance. Structural Health Monitoring/Management (SHM) in Aerospace Structures, Fuh-Gwo Yuan (Eds.), Elsevier, pp.453-501.
  2. Zeng, J., Wu, Z., Vega, M.A., Todd, M.D., and Hu, Z., 2023. Fast probabilistic damage detection using inverse surrogate models. Data-Centric Structural Health Monitoring, Mohammad Noori, Fuh-Gwo Yuan, and Ehsan Noroozinejad (Eds.), De Gruyter Book on Data-Centric Engineering.
  3. Vega, M.A., Hu, Z., Yang, Y., Chadha, M. and Todd, M.D., 2022. Diagnosis, prognosis, and maintenance decision making for civil infrastructure: Bayesian data analytics and machine learning (pp. 45-73). Structural Health Monitoring Based on Data Science Techniques, Alexandre Cury, Diogo Ribeiro, Filippo Ubertini, and Michael Todd (Eds.), Springer Book on Data Science.
  4. Hu, Z. and Mahadevan, S., 2018. 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, pp.131-156.

Journal publications

  1. Zhao, Z., Dyer, J., Vega, M., Todd, M.D., and Hu, Z., 2025, A modularized model uncertainty quantification frameworkfor simulating nonlinear dynamic systems, Nonlinear Dynamics, 10.1007/s11071-025-11225-w.
  2. Wang, Z., Guo, H., Zhang, C., Hu, Z., Zhou, F., Sun, Z., Sherony, R. and Bao, S., 2025. Investigating pedestrian crash injury patterns: A comparative study of children and non-children. Accident Analysis & Prevention, 222, p.108223.
  3. Zeng, Y., Chadha, M., Zhao, Z., Miele, S., McKnight, C.J., Memarsadeghi, N.P., Gugaratshan, G., Todd, M.D. and Hu, Z., 2025, Hybrid modeling for streamflow prediction in ungauged rivers with uncertainty quantification: a comparative analysis, Journal of Hydroinformatics, p.jh2025011.
  4. Liu, P., Zhao, W., Wang, Z., Yang, W., Hu, Z., Liu, X., Liu, Y., and Chen, L., 2025, Data-driven design of three-dimensional periodic piezoelectric structures via high-throughput calculations, International Journal of Mechanical Sciences, 304(1).
  5. Kassab, A., Mohanty P., Hu, Z., and Ayoub, G., 2025, Graph Neural Network-Driven Surrogate Modeling for Accelerated Parameter Identification in Semicrystalline Polymers, Computational Materials Science, 258(1), 114090.
  6. Zeng, Y., Zhao, Z., Qian, G., Todd, M.D. and Hu, Z., 2025. Enhancing Bayesian Inference-Based Damage Diagnostics Through Domain Translation With Application to Miter Gates. Journal of Mechanical Design, 147(6), p.061701.
  7. Thompson, T., McMullen, R., Nemani, V., Hu, Z. and Hu, C., 2025. A comparative study of acquisition functions for active learning kriging in reliability-based design optimization. Structural and Multidisciplinary Optimization, 68(3), pp.1-40.
  8. Qian, G., Zeng, J., Hu, Z. and Todd, M.D., 2025. Bayesian Model Updating of Multiscale Simulations Informing Corrosion Prognostics Using Conditional Invertible Neural Networks. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 11(1).
  9. Zeng, Y., Zeng, J., Todd, M.D. and Hu, Z., 2025. Data augmentation based on image translation for bayesian inference-based damage diagnostics of miter gates. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 11(1).
  10. Zeng, J., Gao, Z., Li, Y., Barbat, S., Lu, J. and Hu, Z., 2025. Fusion of Multiple Data Sources for Vehicle Crashworthiness Prediction Using CycleGAN and Temporal Convolutional Networks. Journal of Mechanical Design, 147(2).
  11. Zeng, J., Todd, M.D., Zhao, Z. and Hu, Z., 2024. Model uncertainty quantification of a degradation model of miter gates using normalizing flow-based likelihood-free inference. Structural Health Monitoring, p.14759217241287864.
  12. Zhao, Y., Chadha, M., Barthlow, D., Yeates, E., Mcknight, C.J., Memarsadeghi, N.P., Gugaratshan, G., Todd, M.D. and Hu, Z., 2024. Physics-enhanced machine learning models for streamflow discharge forecasting. Journal of Hydroinformatics, 26(10), pp.2506-2537.
  13. Yin, J., Hu, Z. and Du, X., 2024. Uncertainty quantification with mixed data by hybrid convolutional neural network for additive manufacturing. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 10(3).
  14. Thelen, A., Huan, X., Paulson, N., Onori, S., Hu, Z. and Hu, C., 2024. Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives. npj Materials Sustainability, 2(1), p.14.
  15. Zeng, J., Zhao, Y., Li, G., Gao, Z., Li, Y., Barbat, S. and Hu, Z., 2024. Vehicle crashworthiness performance prediction through fusion of multiple data sources. Journal of Mechanical Design, 146(5).
  16. Chadha, M., Hu, Z., Farrar, C.R. and Todd, M.D., 2024. A value-of-information-based optimal sensor placement design framework for cost-effective structural health monitoring (with application to miter gate monitoring). Structural Health Monitoring, p.14759217241275643.
  17. Qian, G., Wu, Z., Hu, Z. and Todd, M.D., 2024. Pitting corrosion diagnostics and prognostics for miter gates using multiscale simulation and image inspection data. Structural Health Monitoring, p.14759217241264291.
  18. Nemani, V., Biggio, L., Huan, X., Hu, Z., Fink, O., Tran, A., Wang, Y., Zhang, X. and Hu, C., 2023. Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial. Mechanical Systems and Signal Processing, 205, p.110796.
  19. Wu, Z., Zeng, J., Hu, Z. and Todd, M.D., 2023. Optimization of unmanned aerial vehicle inspection strategy for infrastructure based on model-enabled diagnostics and prognostics. Mechanical Systems and Signal Processing, 204, p.110841.
  20. Zeng, J., Todd, M.D. and Hu, Z., 2023. A recursive inference method based on invertible neural network for multi-level model updating using video monitoring data. Mechanical Systems and Signal Processing, 203, p.110736.
  21. Yang, Y., Chadha, M., Hu, Z. and Todd, M.D., 2023. An optimal sensor design framework accounting for sensor reliability over the structural life cycle. Mechanical Systems and Signal Processing, 202, p.110673.
  22. Qian, G., Hu, Z. and Todd, M.D., 2023. Physics-based corrosion reliability analysis of miter gates using multi-scale simulations and adaptive surrogate modeling. Mechanical Systems and Signal Processing, 200, p.110619.
  23. Najera-Flores, D.A., Qian, G., Hu, Z. and Todd, M.D., 2023. Corrosion morphology prediction of civil infrastructure using a physics-constrained machine learning method. Mechanical Systems and Signal Processing, 200, p.110515.
  24. Najera-Flores, D.A., Hu, Z., Chadha, M. and Todd, M.D., 2023. A Physics-Constrained Bayesian neural network for battery remaining useful life prediction. Applied Mathematical Modelling, 122, pp.42-59.
  25. Yin, J., Li, L., Mourelatos, Z.P., Liu, Y., Gorsich, D., Singh, A., Tau, S. and Hu, Z., 2023. Reliable global path planning of off-road autonomous ground vehicles under uncertain terrain conditions. IEEE Transactions on Intelligent Vehicles, 9(1), pp.1161-1174.
  26. Zhao, Y., Chadha, M., Olsen, N., Yeates, E., Turner, J., Gugaratshan, G., Qian, G., Todd, M.D. and Hu, Z., 2023. Machine learning-enabled calibration of river routing model parameters. Journal of Hydroinformatics, 25(5), pp.1799-1821.
  27. Yin, J., Hu, Z., Mourelatos, Z.P., Gorsich, D., Singh, A. and Tau, S., 2023. 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, 24(12), pp.15035-15050.
  28. Zeng, J., Li, G., Gao, Z., Li, Y., Sundararajan, S., Barbat, S. and Hu, Z., 2023. Machine learning enabled fusion of CAE data and test data for vehicle crashworthiness performance evaluation by analysis. Structural and Multidisciplinary Optimization, 66(4), p.96.
  29. Zeng, J., Wu, Z., Todd, M.D. and Hu, Z., 2023. Bayes risk-based mission planning of unmanned aerial vehicles for autonomous damage inspection. Mechanical Systems and Signal Processing, 187, p.109958.
  30. Zeng, J., Todd, M.D. and Hu, Z., 2023. Probabilistic damage detection using a new likelihood-free Bayesian inference method. Journal of Civil Structural Health Monitoring, 13(2), pp.319-341.
  31. Zhao, Y., Jiang, C., Vega, M.A., Todd, M.D. and Hu, Z., 2023. Surrogate modeling of nonlinear dynamic systems: a comparative study. Journal of Computing and Information Science in Engineering, 23(1), p.011001.
  32. Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B.D., Todd, M.D., Mahadevan, S., Hu, C. and Hu, Z., 2023. A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives. Structural and multidisciplinary optimization, 66(1), p.1.
  33. Zeng, J. and Hu, Z., 2022. Automated operational modal analysis using variational Gaussian mixture model. Engineering Structures, 273, p.115139.
  34. Qian, G., Tantratian, K., Chen, L., Hu, Z. and Todd, M.D., 2022. A probabilistic computational framework for the prediction of corrosion-induced cracking in large structures. Scientific Reports, 12(1), p.20898.
  35. Li, J., Zhang, X., Zhou, Q., Chan, F.T. and Hu, Z., 2022. A feature-level multi-sensor fusion approach for in-situ quality monitoring of selective laser melting. Journal of Manufacturing Processes, 84, pp.913-926.
  36. Ramancha, M.K., Vega, M.A., Conte, J.P., Todd, M.D. and Hu, Z., 2022. Bayesian model updating with finite element vs surrogate models: Application to a miter gate structural system. Engineering structures, 272, p.114901.
  37. Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B.D., Todd, M.D., Mahadevan, S., Hu, C. and Hu, Z., 2022. A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies. Structural and Multidisciplinary Optimization, 65(12), p.354.
  38. Lin, Q., Zhou, Q., Hu, J., Cheng, Y. and Hu, Z., 2022. A sequential sampling approach for multi-fidelity surrogate modeling-based robust design optimization. Journal of Mechanical design, 144(11), p.111703.
  39. Papadimitriou, D., Mourelatos, Z.P. and Hu, Z., 2022. Nonlinear Random Vibrations Using Second-Order Adjoint and Projected Differentiation Methods. Journal of Vibration and Acoustics, 144(5), p.051001.
  40. Wu, Z., Fillmore, T.B., Vega, M.A., Hu, Z. and Todd, M.D., 2022. Diagnostics and prognostics of multi-mode failure scenarios in miter gates using multiple data sources and a dynamic Bayesian network. Structural and multidisciplinary optimization, 65(9), p.270.
  41. Liu, Y., Barthlow, D., Mourelatos, Z.P., Zeng, J., Gorsich, D., Singh, A. and Hu, Z., 2022. Mobility prediction of off-road ground vehicles using a dynamic ensemble of NARX models. Journal of Mechanical Design, 144(9), p.091709.
  42. Liu, Y., Jiang, C., Zhang, X., Mourelatos, Z.P., Barthlow, D., Gorsich, D., Singh, A. and Hu, Z., 2022. Reliability-based multivehicle path planning under uncertainty using a bio-inspired approach. Journal of Mechanical Design, 144(9), p.091701.
  43. Fillmore, T.B., Wu, Z., Vega, M.A., Hu, Z. and Todd, M.D., 2022. A surrogate model to accelerate non-intrusive global–local simulations of cracked steel structures. Structural and multidisciplinary optimization, 65(7), p.208.
  44. Jiang, C., Vega, M.A., Ramancha, M.K., Todd, M.D., Conte, J.P., Parno, M. and Hu, Z., 2022. Bayesian calibration of multi-level model with unobservable distributed response and application to miter gates. Mechanical systems and signal processing, 170, p.108852.
  45. Yang, Y., Chadha, M., Hu, Z. and Todd, M.D., 2022. An optimal sensor placement design framework for structural health monitoring using Bayes risk. Mechanical Systems and Signal Processing, 168, p.108618.
  46. Jiang, C., Liu, Y., Mourelatos, Z.P., Gorsich, D., Fu, Y. and Hu, Z., 2022. Efficient reliability-based mission planning of off-road autonomous ground vehicles using an outcrossing approach. Journal of Mechanical Design, 144(4), p.041703.
  47. Mahadevan, S., Nath, P. and Hu, Z., 2022. Uncertainty quantification for additive manufacturing process improvement: Recent advances. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 8(1), p.010801.
  48. Huang, X., Xie, T., Wang, Z., Chen, L., Zhou, Q. and Hu, Z., 2022. A transfer learning-based multi-fidelity point-cloud neural network approach for melt pool modeling in additive manufacturing. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 8(1), p.011104.
  49. Jiang, C., Vega, M.A., Todd, M.D. and Hu, Z., 2022. Model correction and updating of a stochastic degradation model for failure prognostics of miter gates. Reliability Engineering & System Safety, 218, p.108203.
  50. Chadha, M., Hu, Z. and Todd, M.D., 2022. An alternative quantification of the value of information in structural health monitoring. Structural Health Monitoring, 21(1), pp.138-164.
  51. Yang, Y., Chadha, M., Hu, Z., Vega, M.A., Parno, M.D. and Todd, M.D., 2021. A probabilistic optimal sensor design approach for structural health monitoring using risk-weighted f-divergence. Mechanical Systems and Signal Processing, 161, p.107920.
  52. Moustafa, K., Hu, Z., Mourelatos, Z.P., Baseski, I. and Majcher, M., 2021. System reliability analysis using component-level and system-level accelerated life testing. Reliability Engineering & System Safety, 214, p.107755.
  53. Lin, Q., Hu, J., Zhou, Q., Cheng, Y., Hu, Z., Couckuyt, I. and Dhaene, T., 2021. Multi-output Gaussian process prediction for computationally expensive problems with multiple levels of fidelity. Knowledge-Based Systems, 227, p.107151.
  54. Vega, M.A., Hu, Z., Fillmore, T.B., Smith, M.D. and Todd, M.D., 2021. A novel framework for integration of abstracted inspection data and structural health monitoring for damage prognosis of miter gates. Reliability Engineering & System Safety, 211, p.107561.
  55. Papadimitriou, D., Mourelatos, Z.P. and Hu, Z., 2021. Reliability analysis and random vibration of nonlinear systems using the adjoint method and projected differentiation. Journal of Mechanical Design, 143(6), p.061705.
  56. Chen, X., Su, W., Kavousi-Fard, A., Skowronska, A.G., Mourelatos, Z.P. and Hu, Z., 2021. Resilient microgrid system design for disaster impact mitigation. Sustainable and Resilient Infrastructure, 6(1-2), pp.56-72.
  57. Wei, X., Zhao, J., He, X., Hu, Z., Du, X. and Han, D., 2021. Adaptive Kriging method for uncertainty quantification of the photoelectron sheath and dust levitation on the lunar surface. Journal of Verification, Validation and Uncertainty Quantification, 6(1), p.011006.
  58. Liu, Y., Jiang, C., Mourelatos, Z.P., Gorsich, D., Jayakumar, P., Fu, Y., Majcher, M. and Hu, Z., 2021. Simulation-based mission mobility reliability analysis of off-road ground vehicles. Journal of Mechanical Design, 143(3), p.031701.
  59. Jiang, C., Hu, Z., Mourelatos, Z.P., Gorsich, D., Jayakumar, P., Fu, Y. and Majcher, M., 2021. R2-RRT*: Reliability-based robust mission planning of off-road autonomous ground vehicle under uncertain terrain environment. IEEE Transactions on Automation Science and Engineering, 19(2), pp.1030-1046.
  60. Nie, S., Guo, M., Yin, F., Ji, H., Ma, Z., Hu, Z. and Zhou, X., 2021. Research on fluid-structure interaction for piston/cylinder tribopair of seawater hydraulic axial piston pump in deep-sea environment. Ocean Engineering, 219, p.108222.
  61. Vega, M.A., Hu, Z. and Todd, M.D., 2020. Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol. Reliability Engineering & System Safety, 204, p.107147.
  62. 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 Comput Mater 6, 175 (2020). https://doi.org/10.1038/s41524-020-00444-x
  63. Huang, X., Lei, Q., Xie, T., Zhang, Y., Hu, Z. and Zhou, Q., 2020. Deep transfer convolutional neural network and extreme learning machine for lung nodule diagnosis on CT images. Knowledge-Based Systems, 204, p.106230.
  64. Jiang, C., Hu, Z., Liu, Y., Mourelatos, Z.P., Gorsich, D. and Jayakumar, P., 2020. A sequential calibration and validation framework for model uncertainty quantification and reduction. Computer Methods in Applied Mechanics and Engineering, 368, p.113172.
  65. Zhang, X., Hu, Z. and Mahadevan, S., 2020. Bilevel optimization model for resilient configuration of logistics service centers. IEEE Transactions on Reliability, 71(1), pp.469-483.
  66. Papadimitriou, D., Mourelatos, Z.P., Patil, S., Hu, Z., Tsianika, V. and Geroulas, V., 2020. Reliability analysis of nonlinear vibratory systems under non-Gaussian loads using a sensitivity-based propagation of moments. Journal of Mechanical Design, 142(6), p.061704.
  67. NIE, S., LI, S., YIN, F., ZHOU, X. and HU, Z., 2020. Study on fluid-solid coupling of deformation characteristics of piston bush in water hydraulic pumps. China Mechanical Engineering, 31(10), p.1135.
  68. Li, M., Sadoughi, M., Hu, Z. and Hu, C., 2020. A hybrid Gaussian process model for system reliability analysis. Reliability Engineering & System Safety, 197, p.106816.
  69. Moustafa, K., Hu, Z., Mourelatos, Z.P., Baseski, I. and Majcher, M., 2020. Resource Allocation for System Reliability Assessment Using Accelerated Life Testing. Journal of Mechanical Design, 142(3), p.031119.
  70. Hu, Z., Mourelatos, Z.P., Gorsich, D., Jayakumar, P. and Majcher, M., 2020. Testing design optimization for uncertainty reduction in generating off-road mobility map using a Bayesian approach. Journal of Mechanical Design, 142(2), p.021402.
  71. Nie, S., Xu, W., Yin, F., Ji, H., Xu, Y., Hu, Z., Guo, M. and Zhou, X., 2019. Investigation of the tribological behaviour of cermets sliding against Si3N4 for seawater hydraulic components applications. Surface Topography: Metrology and Properties, 7(4), p.045025.
  72. Hu, Z. and Mahadevan, S., 2019. Reliability analysis of a hypersonic vehicle panel with spatio-temporal variability. AIAA Journal, 57(12), pp.5403-5415.
  73. Wang, Z., Liu, P., Ji, Y., Mahadevan, S., Horstemeyer, M.F., Hu, Z., Chen, L. and Chen, L.Q., 2019. Uncertainty quantification in metallic additive manufacturing through physics-informed data-driven modeling. Jom, 71, pp.2625-2634.
  74. Wang, Z., Liu, P., Xiao, Y., Cui, X., Hu, Z. and Chen, L., 2019. A data-driven approach for process optimization of metallic additive manufacturing under uncertainty. Journal of Manufacturing Science and Engineering, 141(8), p.081004.
  75. Hu, Z. and Mahadevan, S., 2019. Probability models for data-driven global sensitivity analysis. Reliability Engineering & System Safety, 187, pp.40-57.
  76. Koutsellis, T., Mourelatos, Z.P. and Hu, Z., 2019. 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, 5(2), p.020904.
  77. Drignei, D., Mourelatos, Z. and Hu, Z., 2019. Uncertainty Quantification of Complement Sensitivity Indices in Dynamic Computer Models. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering, 5(2), p.020901.
  78. Liu, Y., Zhao, Y., Hu, Z., Mourelatos, Z.P. and Papadimitriou, D., 2019. 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, 5(2), p.020906.
  79. Yin, F., Zhou, X., Nie, S., Ji, H. and Hu, Z., 2019. Tribocorrosion behavior of several corrosion-resistant alloys sliding against CF-PEEK: application for hydraulic valve in seawater. International Journal of Electrochemical Science, 14(5), pp.4643-4658.
  80. Li, M., Sadoughi, M., Hu, C., Hu, Z., Eshghi, A.T. and Lee, S., 2019. High-dimensional reliability-based design optimization involving highly nonlinear constraints and computationally expensive simulations. Journal of Mechanical Design, 141(5), p.051402.
  81. Nath, P., Hu, Z. and Mahadevan, S., 2019. Uncertainty quantification of grain morphology in laser direct metal deposition. Modelling and Simulation in Materials Science and Engineering, 27(4), p.044003.
  82. Tsianika, V., Geroulas, V., Papadimitriou, D., Mourelatos, Z., Hu, Z. and Majcher, M., 2019. A Methodology of Design for Fatigue Using an Accelerated Life Testing Approach with Saddlepoint Approximation (No. 2019-01-0159). SAE Technical Paper.
  83. Papadimitriou, D.I., Mourelatos, Z.P. and Hu, Z., 2019. Reliability analysis using second-order saddlepoint approximation and mixture distributions. Journal of Mechanical Design, 141(2), p.021401.
  84. Neal, K., Hu, Z., Mahadevan, S. and Zumberge, J., 2019. Discrepancy prediction in dynamical system models under untested input histories. Journal of Computational and Nonlinear Dynamics, 14(2), p.021009.
  85. Hu, Z., Hu, C., Mourelatos, Z.P. and Mahadevan, S., 2019. Model discrepancy quantification in simulation-based design of dynamical systems. Journal of Mechanical Design, 141(1), p.011401.
  86. Hu, Z. and Mahadevan, S., 2018. Bayesian network learning for data-driven design. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 4(4), p.041002.
  87. Hu, Z. and Mourelatos, Z.P., 2018. A sequential accelerated life testing framework for system reliability assessment with untestable components. Journal of Mechanical Design, 140(10), p.101401.
  88. Hu, Z., Zhu, Z. and Du, X., 2018. Time-dependent system reliability analysis for bivariate responses. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 4(3), p.031002.
  89. Hu, Z. and Mahadevan, S., 2018. Adaptive surrogate modeling for time-dependent multidisciplinary reliability analysis. Journal of Mechanical Design, 140(2), p.021401.
  90. Hu, Z., Mahadevan, S. and Ao, D., 2018. Uncertainty aggregation and reduction in structure–material performance prediction. Computational Mechanics, 61, pp.237-257.
  91. Hu, Z. and Mourelatos, Z., 2018. Efficient global surrogate modeling based on multi-layer sampling. SAE International Journal of Materials and Manufacturing, 11(4), pp.385-400.
  92. Hu, Z. and Mahadevan, S., 2017. Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities. The International Journal of Advanced Manufacturing Technology, 93, pp.2855-2874.
  93. Ao, D., Hu, Z. and Mahadevan, S., 2017. Design of validation experiments for life prediction models. Reliability Engineering & System Safety, 165, pp.22-33.
  94. Hu, Z. and Mahadevan, S., 2017. A surrogate modeling approach for reliability analysis of a multidisciplinary system with spatio-temporal output. Structural and Multidisciplinary Optimization, 56(3), pp.553-569.
  95. Nath, P., Hu, Z. and Mahadevan, S., 2017. Sensor placement for calibration of spatially varying model parameters. Journal of Computational Physics, 343, pp.150-169.
  96. Hu, Z. and Mahadevan, S., 2017. Uncertainty quantification in prediction of material properties during additive manufacturing. Scripta materialia, 135, pp.135-140.
  97. Hu, Z., Ao, D. and Mahadevan, S., 2017. Calibration experimental design considering field response and model uncertainty. Computer Methods in Applied Mechanics and Engineering, 318, pp.92-119.
  98. Hu, Z., Nannapaneni, S. and Mahadevan, S., 2017. Efficient kriging surrogate modeling approach for system reliability analysis. AI EDAM, 31(2), pp.143-160.
  99. Hu, Z. and Mahadevan, S., 2017. 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), p.011007.
  100. Ao, D., Hu, Z. and Mahadevan, S., 2017. Dynamics model validation using time-domain metrics. Journal of Verification, Validation and Uncertainty Quantification, 2(1), p.011004.
  101. Devathi, H., Hu, Z. and Mahadevan, S., 2016. Snap-through buckling reliability analysis under spatiotemporal variability and epistemic uncertainty. AIAA Journal, 54(12), pp.3981-3993.
  102. Nannapaneni, S., Hu, Z. and Mahadevan, S., 2016. Uncertainty quantification in reliability estimation with limit state surrogates. Structural and Multidisciplinary Optimization, 54, pp.1509-1526.
  103. Hu, Z. and Mahadevan, S., 2016. Resilience assessment based on time-dependent system reliability analysis. Journal of Mechanical Design, 138(11), p.111404.
  104. Hu, Z. and Mahadevan, S., 2016. Accelerated life testing (ALT) design based on computational reliability analysis. Quality and Reliability Engineering International, 32(7), pp.2217-2232.
  105. Hu, Z., Mahadevan, S. and Du, X., 2016. 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, 2(3), p.031005.
  106. Hu, Z. and Du, X., 2016. Reliability-based design optimization under stationary stochastic process loads. Engineering Optimization, 48(8), pp.1296-1312.
  107. Hu, Z. and Mahadevan, S., 2016. A single-loop kriging surrogate modeling for time-dependent reliability analysis. Journal of Mechanical Design, 138(6), p.061406.
  108. Hu, Z. and Mahadevan, S., 2016. Global sensitivity analysis-enhanced surrogate (GSAS) modeling for reliability analysis. Structural and Multidisciplinary Optimization, 53, pp.501-521.
  109. Hu, Z. and Du, X., 2015. 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, 1(4), p.041005.
  110. Hu, Z. and Mahadevan, S., 2015. Time-dependent system reliability analysis using random field discretization. Journal of Mechanical Design, 137(10), p.101404.
  111. Hu, Z. and Du, X., 2015. Mixed efficient global optimization for time-dependent reliability analysis. Journal of Mechanical Design, 137(5), p.051401.
  112. Hu, Z. and Du, X., 2015. First order reliability method for time-variant problems using series expansions. Structural and Multidisciplinary Optimization, 51, pp.1-21.
  113. Hu, Z. and Du, X., 2014. Lifetime cost optimization with time-dependent reliability. Engineering Optimization, 46(10), pp.1389-1410.
  114. Li, H., Hu, Z., Chandrashekhara, K., Du, X. and Mishra, R., 2014. Reliability-based fatigue life investigation for a medium-scale composite hydrokinetic turbine blade. Ocean Engineering, 89, pp.230-242.
  115. Hu, Z., Du, X., Conrad, D., Twohy, R. and Walmsley, M., 2014. Fatigue reliability analysis for structures with known loading trend. Structural and Multidisciplinary Optimization, 50, pp.9-23.
  116. Hu, Z., Du, X., Kolekar, N.S. and Banerjee, A., 2014. Robust design with imprecise random variables and its application in hydrokinetic turbine optimization. Engineering Optimization, 46(3), pp.393-419.
  117. Nie, S., Ji, H., Huang, Y., Hu, Z. and Li, Y., 2013. Robust interval-based minimax-regret analysis method for filter management of fluid power system. Asia-Pacific Journal of Operational Research, 30(06), p.1350021.
  118. Zhang, X., Hu, Z. and Du, X., 2013. Probabilistic inverse simulation and its application in vehicle accident reconstruction. Journal of Mechanical Design, 135(12), p.121006.
  119. Hu, Z. and Du, X., 2013. Time-dependent reliability analysis with joint upcrossing rates. Structural and Multidisciplinary Optimization, 48, pp.893-907.
  120. Hu, Z. and Du, X., 2013. A sampling approach to extreme value distribution for time-dependent reliability analysis. Journal of Mechanical Design, 135(7), p.071003.
  121. Hu, Z., Li, H., Du, X. and Chandrashekhara, K., 2013. Simulation-based time-dependent reliability analysis for composite hydrokinetic turbine blades. Structural and Multidisciplinary Optimization, 47, pp.765-781.
  122. Zheng, Y.L., Nie, S.L., Ji, H. and Hu, Z., 2013. 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, pp.276-286.
  123. Hu, Z. and Du, X., 2012. Reliability analysis for hydrokinetic turbine blades. Renewable Energy, 48, pp.251-262.
  124. Du, X. and Hu, Z., 2012. First order reliability method with truncated random variables, Journal of Mechanical Design, 091005.
  125. Nie, S.L., Hu, B., Li, Y.P., Hu, Z. and Huang, G.H., 2011. Identification of filter management strategy in fluid power systems under uncertainty: an interval-fuzzy parameter integer nonlinear programming method. International journal of systems science, 42(3), pp.429-448.
  126. Nie, S.L., Xiong, Z.B., Li, Y.P., Huang, G.H. and Hu, Z., 2010. An improved fuzzy programming model with an L—R fuzzy number for filter management strategies in fluid power systems under uncertainty. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 224(9), pp.2011-2026.
  127. Nie, S.L., Hu, Z., Li, Y.P. and Huang, G.H., 2010. Improved interval-fuzzy quadratic programming for management of filters in a fluid power system under uncertainty. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 224(2), pp.103-118.
  128. Nie, S.L., Hu, Z., Li, Y.P. and Huang, G.H., 2010. Non-linear programming for filter management in a fluid power system with uncertainty. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 224(2), pp.185-201.

Conference proceedings

  1. Zeng, Y., Zhao, Z., Qian, G., Todd, M.D. and Hu, Z., 2024, November. Damage Diagnostics of Miter Gates Using Domain Adaptation and Normalizing Flow-Based Likelihood-Free Inference. In Annual Conference of the PHM Society (Vol. 16, No. 1).
  2. Zeng, Y., Zeng, J., Todd, M.D. and Hu, Z., 2024, August. Augmenting bayesian inference-based damage diagnostics of miter gates based on image translation. In International design engineering technical conferences and computers and information in engineering conference (Vol. 88360, p. V03AT03A041). American Society of Mechanical Engineers.
  3. Qin, C., Zeng, Y., Zhao, Y., Gugaratshan, G. and Hu, Z., 2024, August. Recalibration of Neural Networks Using Transfer Learning for Streamflow Forecasting. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 88360, p. V03AT03A038). American Society of Mechanical Engineers.
  4. Habbal, O., Ullrich, M., Al Nabhani, D., Mohanty, P., Hu, Z., Chehade, A. and Pannier, C., 2024, May. Cyber Physical System for Data-Driven Modeling of Fused Filament Fabrication (FFF) Extrusion Process. In 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS) (pp. 1-6). IEEE.
  5. Zeng, J., Todd, M.D. and Hu, Z., 2024, January. Dynamic State Estimation via Likelihood-Free Inference Based on Conditional Invertible Neural Networks. In IMAC, A Conference and Exposition on Structural Dynamics (pp. 111-114). Cham: Springer Nature Switzerland.
  6. Chadha, M., Hu, Z. and Todd, M.D., 2024, January. Time-Normalized Unitless Metrics for Quantifying the Value of an SHM System Throughout the Structure’s Life Cycle. In IMAC, A Conference and Exposition on Structural Dynamics (pp. 1-4). Cham: Springer Nature Switzerland.
  7. Wu, Z., Hu, Z. and Todd, M.D., 2024, January. Uncertainty Quantification for Deep Learning–Based Automatic Crack Detection in the Underwater Environment. In IMAC, A Conference and Exposition on Structural Dynamics (pp. 133-136). Cham: Springer Nature Switzerland.
  8. Qian, G., Wu, Z., Hu, Z. and Todd, M.D., 2024, January. Multiscale Corrosion Damage Diagnostics and Prognostics for a Miter Gate. In IMAC, A Conference and Exposition on Structural Dynamics (pp. 69-72). Cham: Springer Nature Switzerland.
  9. Qian, G., Wu, Z., Hu, Z. and Todd, M.D., 2024. Pitting Corrosion Monitoring Framework for Miter Gates with Digital Twin. In Proceedings of the 11th European Workshop on Structural Health Monitoring, Potsdam, Germany, June (pp. 10-13).
  10. Chadha, M., Hu, Z., Farrar, C.R. and Todd, M.D., 2024. Risk and Value Informed Structural Health Monitoring System Design for Miter Gates. In 11th European Workshop on Structural Health Monitoring, EWSHM 2024.
  11. Conference papers are not updated here. Please refer to my Google Scholar for my conference paper publications.