Hui Wang

Dr. Hui Wang joined the University of Dayton School of Engineering on Aug. 16, 2018, as an assistant professor of 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 of research experiences at the RWTH Aachen University in Germany. His research focuses are the opportunities in the multidisciplinary fields spanning Bayesian machine learning, uncertainty quantification, geotechnical engineering, infrastructure sustainability and resiliency, and engineering risk assessment. He spent more than a decade in applying statistical and machine learning methods to engineering problems across a wide range of disciplines. Several finished research projects include stochastic predictive model of tunnel structure deformation, soil corrosivity assessment and underground pipeline integrity management, three-dimensional stochastic 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.

His scholarly publication includes 22 peer-reviewed articles in highly ranked journals, 12 conference papers and presentations, 2 technical reports, and one invited article in ASCE G-I GeoStrata. He has developed two open-source computational packages (pyCPT and BaySeg), which have already gained increasing global visibility in the communities of computational geosciences and geotechnical site investigation.

He is currently the technical committee member of ISSMGE TC304: Engineering Practice of Risk Assessment and Management and ASCE\G-I TC: Risk Assessment and Management.

He is also the invited reviewer for all major international journals of geotechnical engineering, civil and infrastructure engineering, and engineering geology including ASCE Journal of Engineering Mechanics, ASCE Journal of Geotechnical and Geoenvironmental Engineering, Canadian Geotechnical Journal, Engineering Geology, Gétechnique, Structural Safety, Structure and Infrastructure Engineering.

• Yuan, B., Choo, C. S., Yeo, L. Y., Wang, Y., Yang, Z., Guan, Q., ..., Wang, H., & Chen,
  X. (2025). Physics-informed machine learning in geotechnical engineering: a direction
  paper. Geomechanics and Geoengineering, 1-32.
• Idoughi, A., Sreeharan, S., Zhang, C., Raffoul, J., Wang, H., Hirakawa, Keigo. (2025) In-Scene Calibration of Poisson Noise Parameters For Phase Image Recovery, IEEE Open Journal of Signal Processing (under review)
• Sreeharan, S., Wang, H.*, Hirakawa, K., Li, B. (2024), Bayesian calibration of digital fringe projection systems considering both aleatoric and epistemic uncertainties, Optics and Laser in Engineering (under review)
• Sreeharan, S., Wang, H.*, Hirakawa, K., Li, B. (2024), Aleatoric uncertainty quantification in digital fringe projection systems at a per-pixel basis, Optics and Laser in Engineering 180 (2024). https://doi.org/10.1016/j.optlaseng.2024.108315
• Shakir, R.*, Wang, H. (2023). Estimating of probabilistic CPT based soil profile using unsupervised Gaussian mixture model with different covariance matrices. Arabian Journal of Geosciences 16, 218 (2023). https://doi.org/10.1007/s12517-023-11283-7
• Wei, X., Wang, H.*. (2022). Stochastic stratigraphic modeling using Bayesian machine learning. Engineering Geology, 106789. https://doi.org/10.1016/j.enggeo.2022.106789
• Zhang, D., Dai, H., Wang, H.*, Huang, H., & Liu, D. (2021). Investigating the Effect of Geological Heterogeneity of Strata on the Bearing Capacity of Shallow Foundations Using Markov Random Field. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(4), 04021060. (Editor Choice paper) https://doi.org/10.1061/AJRUA6.0001182
• Wang, X., Wang, H.*, & Liang, R. (2021). Unsupervised and Simultaneous Stratigraphic Interpretation of CPT Soundings at Site Scale. ASCE International Journal of Geomechanics, 21(8), 04021130. https://doi.org/10.1061/(ASCE)GM.1943-5622.0002113
• Zhao, C., Gong, W., Li, T., Juang, C. H., Tang, H., & Wang, H. (2021). Probabilistic characterization of subsurface stratigraphic configuration with modified random field approach. Engineering Geology, 288, 106138. https://doi.org/10.1016/j.enggeo.2021.106138
• Kim, C., Chen, L., Wang, H., Castaneda, H.*, (2021), Global and local parameters for characterizing and modeling external corrosion in underground coated steel pipelines: A review of critical factors, Journal of Pipeline Science and Engineering https://doi.org/10.1016/j.jpse.2021.01.010
• Gong, W.*, Zhao, C., Juang, C. H., Tang, H., Wang, H., & Hu, X. (2020). Stratigraphic uncertainty modelling with random field approach. Computers and Geotechnics, 125, 103681. https://doi.org/10.1016/j.compgeo.2020.103681
• Wang, X., Wang, H.*, Tang, F., Liang, R., Castaneda, H. (2020). Statistical Analysis of Spatial Distribution of External Corrosion Defects in Buried Pipelines Using a Multivariate Poisson-lognormal Model. Structure and Infrastructure Engineering, 1-16. https://doi.org/10.1080/15732479.2020.1766516 
• Wang, H.*, Wellmann, J.F., Zhang, T., Schaaf, A., Kanig, M., Verweij, E. von Hebel, C., van der Kruk, J. (2019). Pattern extraction of topsoil and subsoil heterogeneity and soil‐crop interaction using unsupervised Bayesian machine learning: An application to satellite‐derived NDVI time series and electromagnetic induction measurements. Journal of Geophysical Research: Biogeosciences, 124(6), 1524-1544. https://doi.org/10.1029/2019JG005046
• Gong, W., Tang, H.*, Wang, H., Wang, X., Juang, C. H. (2019). Probabilistic Analysis and Design of Stabilizing Piles in Slope Considering Stratigraphic Uncertainty. Engineering Geology, 259, 105162. https://doi.org/10.1016/j.enggeo.2019.105162
• Wang, H., Yajima, A., Castaneda, H.* (2019). A Stochastic defect growth model for reliability assessment of corroded underground pipelines. Process Safety and Environmental Protection, 123, 179-189. https://doi.org/10.1016/j.psep.2019.01.005
• Wang, H.* (2018). Finding patterns in subsurface using Bayesian machine learning approach. Underground Space, 5(1), 84-92. https://doi.org/10.1016/j.undsp.2018.10.006
• Wang, X., Wang, H.*, Liang, R., (2019). A Semi-Supervised Clustering based Approach for Stratification Identification Using Borehole and Cone Penetration Test Data. Engineering Geology, 248, 102-116. https://doi.org/10.1016/j.enggeo.2018.11.014
• Wang, H.*, Wang, X., Wellmann, J.F., Liang, R., (2018). A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data. Canadian Geotechnical Journal, 56(8), 1184-1205. https://doi.org/10.1139/cgj-2017-0709
• Wang, H.*, Wang, X., Wellmann, J.F., Liang, R. (2018). Bayesian Stochastic Soil Modeling Framework Using Gaussian Markov Random Fields. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 4(2), 04018014. (Editor Choice paper) https://doi.org/10.1061/AJRUA6.0000965
• Wang, X., Wang, H.*, & Liang, R. Y. (2018). A method for slope stability analysis considering subsurface stratigraphic uncertainty. Landslides, 15(5), 925-936. https://doi.org/10.1007/s10346-017-0925-5
• 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. https://doi.org/10.1016/j.strusafe.2017.10.011
• 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, 49(2), 145-177. https://doi.org/10.1007/s11004-016-9663-9
• 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. https://doi.org/10.1016/j.strusafe.2016.06.007
• 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. https://doi.org/10.1016/j.enggeo.2015.12.017
• 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. https://doi.org/10.1007/s11629-016-3951-1
• Wang, H., Yajima, A., Liang, R. Y.*, & Castaneda, H. (2016). Reliability-based temporal and spatial maintenance strategy for integrity management of corroded underground pipelines. Structure and Infrastructure Engineering, 12(10), 1281-1294. https://doi.org/10.1080/15732479.2015.1113300 
• 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. https://doi.org/10.1016/j.strusafe.2015.05.002
• 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. https://doi.org/10.1016/j.strusafe.2015.01.003
• Wang, H., Yajima, A., Liang, R. Y.*, & Castaneda, H. (2015). 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. https://doi.org/10.1111/mice.12096
• Yajima, A., Wang, H., Liang, R. Y., & Castaneda, H.* (2015). A Clustering based method to evaluate Soil Corrosivity for pipeline external integrity management. International Journal of Pressure Vessels and Piping, 126, 37-47. https://doi.org/10.1016/j.ijpvp.2014.12.004
• Wang, H., & Liang, R. Y.* (2014). Predicting Field Performance of Skid Resistance of Asphalt Concrete Pavement. ASCE Pavement Materials, Structures, and Performance (pp. 296-305). https://doi.org/10.1061/9780784413418.030
• Wang, H., Huang, H., & Liang, R. Y.* (2014). Reliability Evaluation of Segment Joints in Metro Tunnel using MCMC Techniques and Bayesian Inferential Structure. ASCE Tunneling and Underground Construction (pp. 308-320). https://doi.org/10.1061/9780784413449.031

• Sreeharan, S., Wang, H.*, Castaneda, H., Wang, L., Wang, H., Development of dynamic database for proactive and predictive risk management of underground pipelines: a comprehensive review, ASCE Pipelines Conference 2025, Tampa, KL.
• Wang, H.*, Ren, Q., Geohazard monitoring using real-time dynamic point clouds. In AMPP Annual conference 2025, Nashille, TN.
• Wang, H.*, Sreeharan, S., Castaneda, H., Indirect inspection-based Bayesian machine learning model for probabilistic coating status and severity interpretation. In AMPP Annual conference 2025, Nashille, TN.
• Wang, H.*, Wei, X., Chen, G., A novel approach for slope reliability analysis considering the stratigraphic uncertainty and property uncertainty. ISGSR 2025, Oslo, Norway.
• Wang, H.*, Ren, Q., Deformation monitoring of geostructures using structure from motion. ISGSR 2025, Oslo, Norway.
• Jiang, Q., Zhang, J., Zhang, D., Wang, H., Three-dimensional stratum uncertainty simulation considering geological uncertainty and spatial variability. ISGSR 2025, Oslo, Norway.
• Zhu, Y., Carpenter, S., Miller, A., Wang, H., Wang, Z. (Mar, 2025) Modeling Spatial Variability of Ground-Motion Site Resonances for the Jackson Purchase Region in the New Madrid Seismic Zone. In Geofrontiers 2025, Louisville, KY.
• Wei, X., and Wang, H. Three-dimensional Stochastic Model for Stratigraphic Uncertainty Quantification Using Bayesian Machine Learning. ISRERM 2024, Hefei, China, October 18-21, 2024.
• Wang, H., and Wei, X. Three-dimensional geological model for stratigraphic uncertainty quantification using Bayesian machine learning. 2FOMLIG & 5MLIGD, Chengdu, China, October 11-13, 2024.
• Zhao, C., Wei, X., Wang, H.*, Gong, W., Liu, Z. (May, 2024) A comparison study between 2D and 3D slope stability analysis considering stratigraphic uncertainty. In Geoshanghai 2024
• Wang, H.*, Wei, X. (May, 2024) Three-dimensional stochastic model for stratigraphic uncertainty quantification using Bayesian machine learning. In Geoshanghai 2024
• Wang, H.*, Wei, X., (Dec, 2023) Stochastic Simulation of Soil Stratigraphic Profile Using Image Warping. In FOMLIG&UUGGP 2024
• Wang, H.*, & Qian, Z. H. (July, 2023) A Method for Probabilistic Assessment of Slope Bearing Capacity of Slopes Considering Stratigraphic Uncertainty. In Geo-Risk 2023 (pp. 155-163).
• Wang, H.*, & Wei, X. (July, 2023) Stochastic Simulation of Soil Stratigraphic Profile Using Image Warping. In Geo-Risk 2023 (pp. 12-24).
• Wang, H.*, (Dec, 2022) Uncertainty Quantification in Data-driven Geotechnical Stratigraphic Modeling. 8th International Symposium for Geotechnical Safety & Risk (ISGSR2022), Newcastle, Australia.
• Wang, H.*, Wei, X., (Dec, 2022) Geological Uncertainty Quantification Using Image Warping and Bayesian Machine Learning. 8th International Symposium for Geotechnical Safety & Risk (ISGSR2022), Newcastle, Australia.
• Wang, H.*, Qian, Z., Liang, R. Y., (Dec, 2022) A Method for Probabilistic Assessment of Slope Bearing Capacity Considering Stratigraphic Uncertainty. 8th International Symposium for Geotechnical Safety & Risk (ISGSR2022), Newcastle, Australia.
• Castaneda, H.*, Chen, L., Wang, H., Sreeharan, S., (Mar, 2022) Classification of Active and Passive Surface Conditions Based on Different Relaxation Response for External Corrosion Coated Pipelines Paper C2022-18052, 1-15, NACE Proceedings San Antonio, 2022.
• Wang, H.*, Wei, X., (Jun, 2022) Quantifying stratigraphic uncertainty using Markov random field. Proceedings of the 13th International Conference on Structural Safety & Reliability, Shanghai, China.
• Megia, M., Wang, H.*, (Jun, 2022) Bayesian Unsupervised Soil Interpretation Applied to Offshore Foundation Engineering. Proceedings of the 13th International Conference on Structural Safety & Reliability, Shanghai, China.
• Wang, H.*, Wei, X., (Mar. 2022) Stochastic Stratigraphic Simulation and Uncertainty Quantification Using Machine Learning. Geo-congress 2022, Charlotte, NC.
• Wang, H.*, Wei, X., (May, 2022) Stratigraphic uncertainty quantification using Bayesian machine learning and Markov random field. Proceedings of the 20th International Conference on Soil Mechanics and Geotechnical Engineering, Sydney, Australia.
• Wang, H.*, Sreeharan, S., Castaneda, H., (Mar., 2021) Mapping Indication Severity Using Bayesian Machine Learning from Inspection Data by considering the impact of Soil Corrosivity. CORROSION 2021, Salt Lake City, UT
• Wang, H.* (2021 May). A Bayesian Machine Learning Based Subsurface Modeling Approach for Uncertainty Quantification in Geotechnical Site Characterization. Proceedings of 16th International Conference of IACMAG, Turin, Italy (peer-reviewed, accepted)
• Wang, H.*, Wellmann, J.F., van der Kruk, J. (Dec., 2019). Pattern extraction of soil heterogeneity and soil-crop interaction using unsupervised Bayesian learning: an application to satellite-derived NDVI time series and electromagnetic induction measurements. AGU 2019 meeting. San Francisco, CA
• Wang, H.* (Oct., 2019). Bayesian Machine Learning for Geological Modeling and Geophysical interpretations. Shale Insights 2019, Pittsburgh, PA (peer-reviewed)
• Wang, H.*, Wellmann, F., Verweij, E., Hebel, C. V., Kruk, J. V. D. (Apr., 2017). Identification and Simulation of Subsurface Soil patterns using hidden Markov random fields and remote sensing and geophysical EMI data sets, EGU, Vienna, Austria.
• Wang, H.*, Wellmann, F. (Apr., 2016). A Bayesian 3D data fusion and unsupervised joint segmentation approach for stochastic geological modeling using Hidden Markov random fields. In EGU General Assembly (Vol. 18, p. 16924), EGU, Vienna, Austria.
• Wang, H.*, Wellmann, J. F. (Oct., 2015). Pattern-based analysis of subsurface heterogeneities and its application to generate spatial property distributions for process simulations. GeoBerlin Annual Meeting of DGGV & DMG, Berlin, Germany. (peer-reviewed)
• Wang H.*, Huang H. and Liang, R. Y. (May, 2014). Reliability Evaluation of Segment Joints in Metro Tunnel using MCMC Techniques and Bayesian Inferential Structure. Tunneling and Underground Construction, Geo-Shanghai conference proceeding. Shanghai, China. (peer-reviewed)
• Yajima, A.*, Wang, H., Castaneda, H., & Liang, R. (Jan., 2014). Application of Cluster Analysis for Soil Corrosivity Assessment. In Transportation Research Board 93rd Annual Meeting (No. 14-3945), Washington, D.C., US. (peer-reviewed)
• Huang H.*, Wang H. and Zhang J. (May, 2012). Reliability Analysis of Shield Tunnel Segment Joint. Proceedings of 5th Asian-Pacific Symposium on Structural Reliability and its Applications (5APSSRA), Singapore. (Peer-reviewed)

• Castaneda, H., Wang, H., (2021). Mapping Indication Severity Using Bayesian Machine Learning from Indirect Inspection Data into Corrosion Severity for Decision-making in Pipeline Maintenance, Final report for USDOT PHMSA. https://rip.trb.org/view/2093162
• Wang, H., Liang, R. Y. (2019). Investigation on Pavement Friction Demand Categories and Highway Condition-based Friction Demand SN Threshold, Final report for Ohio Department of Transportation ROC project. https://trid.trb.org/view/1681052
• Wang, H., Wang, X., Liang, R. Y. (2019). Study of AI Based Methods for Characterization of Geotechnical Site Investigation Data, Final report for Ohio Department of Transportation ROC project. https://trid.trb.org/view/1709029

OPEN SOURCE REPOSITORIES

• pyfringe: A repository for structured-light scanning technic. https://github.com/hwang051785/pyfringe
• pymrf: Stochastic geological modeling using Markov Random Field and Bayesian Machine Learning https://github.com/hwang051785/pymrf
• PipSeg: A repository for processing pipeline inspection data in ECDA practice. https://github.com/hwang051785/pipseg
• BaySeg: A Python library for unsupervised clustering of n-dimensional datasets, designed for the segmentation of one-, two- and three-dimensional data in the field of geological modeling and geophysics. https://github.com/cgre-aachen/bayseg
• pyCPT: A Bayesian unsupervised learning method for geotechnical soil stratification identification. This package presents a novel perspective to understand the spatial and statistical patterns of a cone penetration dataset and an automatic approach to identify soil stratification. https://github.com/hwang051785/pyCPT

PATENTS AS CO-INVENTOR

• Formation resistance action characteristic simulation test part. China Patent 2011103014341, issued on April 10, 2013.
• Test device of vertical structure model of shield tunnel. China Patent 2011103014322, issued on April 10, 2013.

• 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.

• ISSMGE Bright Spark Lecturer Award, 2022
• UD/UDRI Research Fellow, 2019
• Outstanding reviewer for journal Engineering Geology, 2018
• Outstanding reviewer for journal Tunneling and Underground Space Technology, 2017
• The RWTH funding for early career researchers, Germany, 2016
• AICES postdoctoral research associate fellowship, Germany, 2015
• UA Summer college scholarship, U.S., 2015
• National Graduate student award, China, 2009
• First-class scholarship, China, 2006

Undergraduate Courses, 2018-present

• EGR 201 Engineering mechanics: Statics
• CEE 426 Risk and uncertainty analysis for infrastructures
• CEE 221L Computational Lab (AutoCAD and Python programming)

Graduate Courses, 2018-present

• CEE 595 Machine learning and pattern recognition (I): Theory
• CEE 595 Machine learning and pattern recognition (II): Implementations

• 2019 - NSF Grants Conference, Los Angeles, CA
• 2018 - Integrating Curriculum with Entrepreneurial Mindset (ICE) Workshop hosted by The Kern Entrepreneurial Engineering Network (KEEN), Dallas, Texas
• 2008 - E.I.T., NCEES and Michigan State Board of Professional Engineers

Technical Committee membership

• ISSMGE TC304: Engineering Practice of Risk Assessment and Management
• ASCE\G-I TC: Risk Assessment and Management

Society Membership

• International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE)
• American Society of Civil Engineers (ASCE) Geo-Institute (G-I)

Reviewer (selected)

• ASCE Journal of Engineering Mechanics
• ASCE Journal of Geotechnical and Geoenvironmental Engineering
• ASCE Journal of Infrastructure Systems
• ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems
• Bulletin of Engineering Geology and the Environment
• Canadian Geotechnical Journal
• Engineering Geology
• Engineering Structures
• Geophysical Journal International
• Georisk
• Géotechnique
• MDPI Remote Sensing
• MDPI Water
• MDPI Minerals
• Natural Hazards
• Ocean Engineering
• Structural Safety
• Structure and Infrastructure Engineering
• Transportation Geotechnics
• Tunneling and Underground Space Technology
• Underground Space

Proposal review panel

• Department of Defense (DOD) the Strategic Environmental Research and Development Program (SERDP) FY 2019 Core Solicitation

Conference paper reviewer

• Geo-Extreme 2021

• Pattern recognition and Bayesian machine learning in geotechnical engineering
• Subsurface modeling and geotechnical site characterization
• Spatial geotechnical variability
• Uncertainty quantification and reliability
• Underground pipeline integrity assessment