Publications and Presentations

Zhang, X., & Noshadravan, A.* (2025). Efficient reliability analysis for offshore wind turbines: Leveraging SVM and augmented oversampling technique. Structural Safety, 115, 102597. https://doi.org/10.1016/j.strusafe.2025.102597

Zhang, X.*, Tao, J., & Noshadravan, A.* (2025). Probabilistic Digital Twin for Reliability-based Maintenance Optimization of Offshore Wind Turbines. Renewable Energy, 123777. https://doi.org/10.1016/j.renene.2025.123777

Zhang, X.*, & Noshadravan, A. (2025). Data-Driven Stochastic Approach for Assessing Future Risk of Wave Overtopping in Coastal Defense Structures Under Climate Change. Journal of Engineering Mechanics.  https://doi.org/10.1061/JENMDT.EMENG-8318

Zhang, X., Gulati, J., & Noshadravan, A.* (2025). Stochastic Modeling of Crack Growth and Maintenance Optimization for Metallic Components Subjected to Fatigue-Induced Failure. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 11(2). https://doi.org/10.1115/1.4066080

Barber, S.*, ..., Zhang, X., ... (2025). Fostering open science through a digital open innovation platform–structural health monitoring case study. Wind Energy Science Discussions, 2025, 1-29. https://doi.org/10.5194/wes-2025-122

Banu, S., Zhang, X., Mahgoub, S., Abedi, S., and Noshadravan, A. (2026). Utilization of Machine Learning Techniques and High-Speed Nanoindentation Data for Microstructural Characterization and Mapping of Shale Rocks. (Under Review)

Hu, Y.-T., Zhang, X., Tao, J., and Noshadravan, A. (2026). A Review of Integrated SHM for Next-Generation Offshore Wind Turbines: Sensors, Analytics, and Digital Twins. (Submitted)

Zhang, X., and Noshadravan, A. (2026). Efficient Probabilistic Pre-disaster Damage Prediction on Coastal Residential Buildings Subjected to Hurricane Hazards. (In preparation)

Zhang, X., & Noshadravan, A. (June 2025). Maintenance optimization of floating offshore wind turbines: Digital twins and stochastic modeling for uncertainty management. 14th International Conference on Structural Safety and Reliability (ICOSSAR 2025), Los Angeles, CA. PDF

Banu, S. A., Zhang, X., Mahgoub, S. A., Abedi, S., & Noshadravan, A. (June 2025). Machine Learning-Driven Micromechanical Characterization of Shale Rocks Leveraging High-Speed Nanoindentation Data, in: Proceedings of the 59th U.S. Rock Mechanics/Geomechanics Symposium (ARMA 2025), Santa Fe, NM

Zhang, X., & Noshadravan, A. (September 2023). Efficient lifecycle reliability assessment of offshore wind turbines using digital twin. 14th International Workshop on Structural Health Monitoring (IWSHM 2023), Stanford, CA. PDF 

Zhang, X.*, & Noshadravan, A. (June 2025). Maintenance Optimization of Floating Offshore Wind Turbines: Digital Twins and Stochastic Modeling for Uncertainty Management. 14th International Conference on Structural Safety and Reliability (ICOSSAR’25).

Zhang, X.*, & Noshadravan, A. (May 2024). Risk Assessment of Seawall Overtopping Considering Uncertainties Under Climate Change. Finalist presentation in the PMC student paper competition, Engineering Mechanics Institute and Probabilistic Mechanics & Reliability Conference (EMI/PMC 2024).

Zhang, X.*, & Noshadravan, A. (May 2024). Risk Assessment of Seawall Overtopping Considering Uncertainties Under Climate Change. Engineering Mechanics Institute and Probabilistic Mechanics & Reliability Conference (EMI/PMC 2024).

Cheng, C.-S., Zhang, X.*, & Noshadravan, A. (May 2024). AI-Enhanced Estimation of Post-Disaster Debris Using Aerial Imagery. Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference (EMI/PMC 2024).

Zhang, X.*, Tao, J., & Noshadravan, A. (May 2024). Uncertainty-Aware Digital Twin Framework for Maintenance Optimization of Offshore Wind Turbines. ASME 2024 Verification, Validation, and Uncertainty Quantification Symposium (VVUQ 2024).

Cheng, C.-S., Noshadravan, A., Zhang, X.*, (Oct. 10, 2023). Enhancing Rapid and Automated Disaster Damage Assessment: An Uncertainty-Aware Crowd-AI Teaming System. Urban Climate Solution Workshop, Texas A&M University.

Zhang, X.*, & Noshadravan, A. (Sep. 2023). Reliability-based lifecycle optimization of offshore wind turbines using a digital twin framework. 2nd IACM Mechanistic Machine Learning and Digital Engineering for Computational Science Engineering and Technology 2023. DOI: https://doi.org/10.26226/m.64c26777632e9539aa87d482

Zhang, X.*, & Noshadravan, A. (Sep. 2023). Efficient lifecycle reliability assessment of offshore wind turbines using digital twin. 14th International Workshop on Structural Health Monitoring (IWSHM), 2023.

Zhang, X.*, Khajwal, A.B., & Noshadravan, A. (Jun. 2023). Enhanced Support Vector Machine for efficient reliability analysis of offshore wind turbines. ASCE Engineering Mechanics Institute 2023 Conference (EMI 2023).

Zhang, X.* (2023, May 2). Reliability-based lifecycle optimization of offshore wind turbines using a digital twin framework. Invited presentation at the School of Performance, Visualization, and Fine Arts, Texas A&M University.

Zhang, X.*, Garshasbi, S., Azarijafari, H., & Kirchain, R. (2025, Nov 20). Enhanced fragility model on hazard resilience of masonry structures. Poster presented at the MIT Concrete Sustainability Hub Technical Advisory Group (TAG) Meeting, Massachusetts Institute of Technology, Cambridge, MA.

Zhang, X.*, & Noshadravan, A. Assessing Future Risks of Wave Overtopping in Coastal Defense Structures Under Climate Change. (2025, April 25). Civil & Environmental Engineering Advisory Council (CEEAC) Poster Session, Texas A&M University, College Station, TX.

Zhang, X.*, & Noshadravan, A. (2024, May 1). Assessing Future Risks of Wave Overtopping in Coastal Defense Structures Under Climate Change. Poster presented at the U.S. Department of Transportation (USDOT) Office of Research and Technology (OST-R) Site Visit, University of Oklahoma, Norman, OK.

Zhang, X.*, & Noshadravan, A. (2023, November 10). A probability-based digital twin framework for efficient lifecycle maintenance optimization of offshore wind turbine. Poster presented at the Civil & Environmental Engineering Advisory Council (CEEAC) Poster Session, Texas A&M University, College Station, TX.