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Hui Wang

Assistant Professor

Full-Time Faculty

School of Engineering: Department of Civil and Environmental Engineering and Engineering Mechanics

Contact

Email: Hui Wang
Phone: 937-229-3847
Website: Visit Site
Kettering Laboratories Room 422

Bio

Dr. Hui Wang joined the University of Dayton School of Engineering on August 16, 2018, as assistant professor with the Department of Civil and Environmental Engineering and Engineering Mechanics. He received his Ph.D. degree at the University of Akron and had three years research experiences at the RWTH Aachen University in Germany. His research focuses on the opportunities in the multidisciplinary fields spanning machine learning, infrastructure sustainability and resiliency, and risk assessment. He spent eight years in applying statistical and machine learning methods to engineering problems across a wide range of disciplines. Several finished research projects include predictive model of tunnel structure deformation, soil corrosivity assessment and underground pipeline integrity management, three-dimensional subsurface modeling, and large-scale pattern recognition using remote sensing and geophysical measurements. Moreover, he has worked closely with experts worldwide in the fields of statistics, chemical engineering, mechanical engineering, civil engineering and geosciences in most of his previous and on-going projects.

Selected Publications

  • Wang, H., Wang, X., Wellmann, J. F., Liang, R. (2017). A Bayesian stochastic soil modeling framework using Gaussian Markov random fields. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems.
  • Wang, X., Wang, H., & Liang, R. Y. (2017) A method for slope stability analysis considering subsurface stratigraphic uncertainty. Landslides, 1-12.
  • Wang, X., Wang, H., Liang, R. Y., Zhu, H., & Di, H. (2017). A hidden Markov random field model-based approach for probabilistic site characterization using multiple cone penetration test data. Structural Safety, 70, 128-138.
  • Wang, H., Wellmann, J. F., Li, Z., Wang, X., & Liang, R. Y. (2016). A segmentation approach for stochastic geological modeling using hidden Markov random fields. Mathematical Geosciences, 1-33.
  • Wang, X., Li, Z., Wang, H., Rong, Q., & Liang, R. Y. (2016). Probabilistic analysis of shield-driven tunnel in multiple strata considering stratigraphic uncertainty. Structural Safety, 62, 88-100.
  • Li, Z., Wang, X., Wang, H., & Liang, R. Y. (2016). Quantifying stratigraphic uncertainties by stochastic simulation techniques based on Markov random field. Engineering Geology, 201, 106-122.
  • Wang, X. R., Rong, Q. G., Sun, S. L., & Wang, H. (2016). Stability analysis of slope in strain-softening soils using local arc-length solution scheme. Journal of Mountain Science, 14(1), 175-187.
  • Wang, H., Yajima, A., Liang, R. Y., & Castaneda, H. (2015). A clustering approach for assessing external corrosion in a buried pipeline based on hidden Markov random field model. Structural Safety, 56, 18-29.
  • Wang, H., Yajima, A., Liang, R. Y., & Castaneda, H. (2015). Reliability based temporal and spatial maintenance strategy for integrity management of corroded underground pipelines. Structure and Infrastructure Engineering.
  • Wang, H., Yajima, A., Liang, R. Y., & Castaneda, H. (2015). A Bayesian model framework for calibrating ultrasonic in-line inspection data and estimating actual external corrosion depth in buried pipeline utilizing a clustering technique. Structural Safety, 54, 19-31.
  • Wang, H., Yajima, A., Liang, R. Y., & Castaneda, H. (2014). Bayesian modeling of external corrosion in underground pipelines based on the integration of Markov chain Monte Carlo techniques and clustered inspection data. Computer‐Aided Civil and Infrastructure Engineering, 30(4), 300-316.
  • Yajima, A., Wang, H., Liang, R. Y., & Castaneda, H. (2014). A clustering-based method to evaluate soil corrosivity for pipeline external integrity management. International Journal of Pressure Vessels and Piping, 126, 37-47
  • Wang, H., & Liang, R. Y. (2014). Predicting field performance of skid resistance of asphalt concrete pavement. In Pavement Materials, Structures, and Performance, 296-305.
  • Wang, H., Huang, H., & Liang, R. Y. (2014). Reliability evaluation of segment joints in metro tunnel using MCMC techniques and Bayesian inferential structure. In Tunneling and Underground Construction, 308-320.

Selected Research and Work

  • Stochastic simulation and algorithms development for engineering systems and uncertainty quantification using random field theories, Bayesian inferential frameworks and machine learning techniques.
  • Machine learning enhanced design and maintenance methods for infrastructure systems using simulation, generative design and 3-D printing.
  • Machine learning application in emergency response and risk assessment by combining remote sensing and spatial data analysis.

Selected Honors and Awards

  • The RWTH funding for early career researchers, RWTH Aachen University
  • AICES postdoctoral research associate fellowship, RWTH Aachen University

Courses Taught

  • Statics
  • Applied machine learning and pattern recognition (I) – theories
  • Applied machine learning and pattern recognition (II) – implementation

Professional Activities

Member:

  • American Society of Civil Engineers (ASCE)
  • National Association of Corrosion Engineers (NACE)

Reviewer:

  • Structural Safety
  • Engineering Geology
  • Structure and Infrastructure Engineering
  • Remote Sensing
  • ASCE Journal of Geotechnical and Geoenvironmental Engineering
  • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems
  • Tunneling and Underground Space Technology
  • Bulletin of Engineering Geology and the Environment
  • MDPI Water
  • MDPI Minerals

Research Interests

  • Uncertainty quantification of engineering systems
  • Data mining and spatial data analysis
  • Applied machine learning and spatial – temporal predictive model development
  • Stochastic numerical simulation