Publications

Peer-reviewed Publications (Corresponding author*, student in italic) 

1. Wei, X., & Wang, H.* Stochastic Stratigraphic Simulation Using Image Warping from Sparse Data. Frontiers in Built Environment, 11, 1651919. https://doi.org/10.3389/fbuil.2025.1651919 

2. 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. https://doi.org/10.1080/17486025.2025.2502029

3. 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  DOI: 10.1109/OJSP.2025.3579650 

4. Sreeharan, S., Wang, H.*, Hirakawa, K., Li, B. (2025), Bayesian calibration of digital fringe projection systems considering both aleatoric and epistemic uncertainties, Optics and Lasers in Engineering, 193, 109098. https://doi.org/10.1016/j.optlaseng.2025.109098 

5. 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

6. 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

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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

31. 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

32. 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

33. 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

Conference papers (Corresponding author*, student in italic)

1. 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. 

2. Wang, H.*, Ren, Q., Geohazard monitoring using real-time dynamic point clouds. In AMPP Annual conference 2025, Nashville, TN.

3. 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, Nashville, TN.

4. Wang, H.*, Wei, X., Chen, G., A novel approach for slope reliability analysis considering the stratigraphic uncertainty and property uncertainty. ISGSR 2025, Oslo, Norway.

5. Wang, H.*, Ren, Q., Deformation monitoring of geostructures using structure from motion. ISGSR 2025, Oslo, Norway.

6. Jiang, Q., Zhang, J., Zhang, D., Wang, H., Three-dimensional stratum uncertainty simulation considering geological uncertainty and spatial variability. ISGSR 2025, Oslo, Norway.

7. 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.

8. 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.

9. 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.

10. 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

11. Wang, H.*, Wei, X. (May, 2024) Three-dimensional stochastic model for stratigraphic uncertainty quantification using Bayesian machine learning. In Geoshanghai 2024

12. Wang, H.*, Wei, X., (Dec, 2023) Stochastic Simulation of Soil Stratigraphic Profile Using Image Warping. In FOMLIG&UUGGP 2024

13. 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).

14. Wang, H.*, & Wei, X. (July, 2023) Stochastic Simulation of Soil Stratigraphic Profile Using Image Warping. In Geo-Risk 2023 (pp. 12-24).

15. Wang, H.*, (Dec, 2022) Uncertainty Quantification in Data-driven Geotechnical Stratigraphic Modeling. 8th International Symposium for Geotechnical Safety & Risk (ISGSR2022), Newcastle, Australia.

16. 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.

17. 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.

18. 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.

19. 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.

20. 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.

21. Wang, H.*, Wei, X., (Mar. 2022) Stochastic Stratigraphic Simulation and Uncertainty Quantification Using Machine Learning. Geo-congress 2022, Charlotte, NC.

22. 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.

23. 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

24. 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)

25. 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

26. Wang, H.* (Oct., 2019). Bayesian Machine Learning for Geological Modeling and Geophysical interpretations. Shale Insights 2019, Pittsburgh, PA (peer-reviewed)

27. 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.

28. 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.

29. 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)

30. 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)

31. 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)

32. 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)

Book chapters (Corresponding author*, student in italic)

1. Wang, H.*, Castaneda, H., Sreeharan, S., Knowledge- and Data-driven External Corrosion Modeling in Pipelines. Oil and Gas Pipelines: Integrity and Safety Handbook, Second Edition Wiley, Hoboken, NJ, 2024

2. Castaneda, H.*, Wang, H., Rosas, O., External Corrosion of Pipelines in Soil. Oil and Gas Pipelines: Integrity and Safety Handbook, Second Edition Wiley, Hoboken, NJ, 2024

3. Wang, H.*, Wei, X., Data-Driven Geological Modeling and Uncertainty Quantification Using Bayesian Machine Learning and Stochastic Simulation.  Machine Learning for Data-Centric Geotechnics, Wiley, Hoboken, NJ, 2025

Technical reports

1. 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

2. 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

3. 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

Invited Article

1. Wang, H., Wang, X., Liang, R., Merklin, C., Taliaferro, S, When AI Meets DIGGS: The Birth of a New Site Characterization Paradigm, ASCE G-I GeoStrata, 01/2021.

Invited talks & workshops

1. Three-dimensional Stochastic Model for Stratigraphic Uncertainty Quantification Using Bayesian Machine Learning. Invited Talk, GeoShanghai 2024, May 27-29, 2024 in Shanghai, China.

2. Stochastic Simulation of Soil Stratigraphic Profile Using Image Warping. Invited Talk, Joint Workshop on Future of Machine Learning in Geotechnics (FOMLIG) & Use of Urban Geoinformation for Geotechnical Practice (UUGGP), 5-6 December 2023 in Okayama, Japan

3. Uncertainty quantification in data-driven geotechnical stratigraphic modeling, Keynote, Bright Spark Lecture at ISGSR 2022, 14 - 16 December 2022 - Newcastle, Australia

4. Machine Learning for Pipeline Corrosion Study, Online workshop speaker, Sponsored by PHMSA USDOT, Sep. 22, 2021.

5. Machine Learning Enhanced Subsurface Modeling and Uncertainty Quantification Paradigm, Invited talk, Iowa State University, Apr. 2021.

6. Laboratory and Field Geotechnical Characterization for Improved Steel Corrosion Modeling, Invited speaker and panelist, the National Academy of Science, online workshop, Mar 9-10, 2021.

7. AI Based Methods for Characterization of Geotechnical Site Investigation Data, 2019 Midwest Geotechnical Conference, Columbus, OH, Oct. 4, 2019.

8. Applications of Machine learning in civil engineering: pipeline corrosion and 3D geological modeling, talk at department of geotechnical engineering, Tongji University, Shanghai, China, 2017.07.08

9. Pattern based spatial data analysis using machine learning techniques, talk at Institute of Bio- and Geosciences, Jülich research center, Jülich, Germany. 2016.09.15

10. Geological modeling and uncertainty quantification using a three-dimensional stochastic segmentation approach, International young scholars’ forum, SYSU, Zhuhai, China, 2015.12.22

11. Predictive modeling of external corrosion using machine learning and stochastic processes, talk at AICES seminar, RWTH Aachen University, Aachen, Germany. 2015.04.17

OPEN-SOURCE repositories

1. pyfringe: A repository for structured-light scanning technic. https://github.com/hwang051785/pyfringe

2. pymrf: Stochastic geological modeling using Markov Random Field and Bayesian Machine Learning https://github.com/hwang051785/pymrf

3. PipSeg: A repository for processing pipeline inspection data in ECDA practice. https://github.com/hwang051785/pipseg

4. 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

5. 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

1. Formation Resistance Action Characteristic Simulation Test Part (China Patent 2011103014341, issued on April 10, 2013).

2. Test Device of Vertical Structure Model of Shield Tunnel (China Patent 2011103014322, issued on April 10, 2013).