Future of machine learning in geotechnics

References

• Abubakar, A., M. J. Brædstrup, H. Di, A. T. Diaz, S. Freeman, S. Hviid, K. H. Karkov, et al. 2021. Deep Learning Applications for Wind Farms Site Characterization and Monitoring. SEG Technical Program Expanded Abstracts, 3009-3013.
   Google Scholar
• American Society of Civil Engineers. 2019. Future World Vision: Infrastructure Reimagined. https://cdn.asce.org/fwv/files/pdfs/asce-future-world-vision-final-report-updated-may-2019.pdf
   Google Scholar
• Antoljak, S. 2020. Geotechnical BIM in 2020. Proceedings, Geo-Congress 2020, Minneapolis, Minnesota. doi:10.1061/9780784482810.097.
   Google Scholar
• Azmoon, B., A. Biniyaz, Z. Liu, and Y. Sun. 2021. “Image-data-driven Slope Stability Analysis for Preventing Landslides Using Deep Learning.” IEEE Access 9: 150623–150636.
   Google Scholar
• Berdigylyjov, M., and H. Popa. 2018. The Implementation and Role of Geotechnical Data in BIM Process. Proc., E3S Web of Conferences, 85, 08009. doi:10.1051/e3sconf/20198508009.
   Google Scholar
• Chahal, H., H. Toner, and I. Rahkovsky. 2021. Small Data’s Big AI Potential. Centre for Security and Emerging Technology.
   Google Scholar
• Chao, Z., G. Ma, Y. Zhang, Y. Zhu, and H. Hu. 2018. The Application of Artificial Neural Network in Geotechnical Engineering. IOP Conference Series: Earth and Environmental Science, 189, 022054.
   Google Scholar
• Chapman, D., S. Providakis, and C. Rogers. 2020. “BIM for the Underground – an Enabler for Trenchless Construction.” Underground Space 5 (4): 354–361.
   Google Scholar
• Ching, J., D. Q. Li, and K. K. Phoon. 2016. “Statistical Characterization of Multivariate Geotechnical Data.” Chap. 4, Reliability of Geotechnical Structures in ISO2394, 89–126. CRC Press/Balkema.
   Google Scholar
• Ching, J., and K. K. Phoon. 2014. “Transformations and Correlations among Some Clay Parameters – the Global Database.” Canadian Geotechnical Journal 51 (6): 663–685.
   Web of Science ®Google Scholar
• Ching, J., K. K. Phoon, Y. H. Ho, and M. C. Weng. 2021. “Quasi-site-specific Prediction for Deformation Modulus of Rock Mass.” Canadian Geotechnical Journal 58 (7): 936–951.
   Google Scholar
• Ching, J., S. Wu, and K. K. Phoon. 2021. “Constructing Quasi-Site-Specific Multivariate Probability Distribution Using Hierarchical Bayesian Model. Journal of Engineering Mechanics.” ASCE 147 (10): Article 04021069.
   Google Scholar
• Christensen, C. W., E. J. Harrison, A. A. Pfaffhuber, and A. K. Lund. 2021. “A Machine Learning–Based Approach to Regional-Scale Mapping of Sensitive Glaciomarine Clay Combining Airborne Electromagnetics and Geotechnical Data.” Near Surface Geophysics 19 (5): 523–539.
   Google Scholar
• Cristian, G. L. 2019. “Site Characterization Using Kriging and Machine Learning Approaches.” Masters thesis, NTNU.
   Google Scholar
• Ebid, A. M. 2021. “35 Years of (AI) in Geotechnical Engineering: State of the art.” Geotechnical and Geological Engineering 39: 637–690.
   Web of Science ®Google Scholar
• Elmo, D., and D. Stead. 2020. Disrupting Rock Engineering Concepts: Is there Such a Thing as a Rock Mass Digital Twin and are Machines Capable of Learning Rock Mechanics? Slope Stability 2020: Proceedings of the 2020 International Symposium on Slope Stability in Open Pit Mining and Civil Engineering, Perth, Australia, 565–576.
   Google Scholar
• Faramarzi, A. 2020. “Editorial for Themed Issue on “Application of Machine Learning in Smart Infrastructure and Construction”.” Proceedings of the Institution of Civil Engineers – Smart Infrastructure and Construction 173 (4): 73–73.
   Google Scholar
• Goh, A. T. C., R. H. Zhang, W. Wang, L. Wang, H. Liu, and W. G. Zhang. 2020. “Numerical Study of the Effects of Groundwater Drawdown on Ground Settlement for Excavation in Residual Soils.” Acta Geotechnica 15: 1259–1272.
   Web of Science ®Google Scholar
• Gondar, J., A. Pinto, and C. Fartaria. 2019. The Use of BIM Technology in Geotechnical Engineering. Proc., XVII European Conference on Soil Mechanics and Geotechnical Engineering (ECSMGE 2019), Reykjavik, Iceland. doi:10.32075/17ECSMGE-2019-0530.
   Google Scholar
• Institution of Civil Engineers. 2017. State of the Nation Report: Digital Transformation. https://www.ice.org.uk/getattachment/news-and-insight/policy/state-of-the-nation-2017-digital-transformation/ICE-SoN-Report-Web-Updated.pdf.aspx.
   Google Scholar
• Institution of Civil Engineers. 2018. Blockchain Technology in the Construction Industry. https://myice.ice.org.uk/ICEDevelopmentWebPortal/media/Documents/News/Blog/Blockchain-technology-in-Construction-2018-12-17.pdf.
   Google Scholar
• Jaksa, M. B., and Z. Q. Liu. 2021. “Editorial for Special Issue on “Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering”.” Geosciences 11 (10): 399.
   Google Scholar
• Jaksa, M. B., H. R. Maier, and M. A. Shahin. 2008. Future Challenges for Artificial Neural Network Modelling in Geotechnical Engineering. Proceedings, 12th International Conference of International Association for Computer Methods and Advances in Geomechanics (IACMAG), October 1–6, 2008, Goa, India, 1710–1719.
   Google Scholar
• Javadi, A., and M. Rezania. 2009. “Applications of Artificial Intelligence and Data Mining Techniques in Soil Modeling.” Geomechanics and Engineering 1 (1): 53–74.
   Web of Science ®Google Scholar
• Jong, S. C., D. E. L. Ong, and E. Oh. 2021. “State-of-the-art Review of Geotechnical-Driven Artificial Intelligence Techniques in Underground Soil-Structure Interaction.” Tunnelling and Underground Space Technology 113: Article 103946.
   Google Scholar
• Lin, Q., T. Ci, L. Wang, S. K. Mondal, H. Yin, and Y. Wang. 2022. “Transfer Learning for Improving Seismic Building Damage Assessment.” Remote Sensing 14: 201. doi:10.3390/rs14010201.
   Web of Science ®Google Scholar
• Ma, Z., and G. Mei. 2021. “Deep Learning for Geological Hazards Analysis: Data, Models, Applications, and Opportunities.” Earth-Science Reviews 223: Article 103858.
   Google Scholar
• Marr, W. A. 2006. Geotechnical Engineering and Judgment in the Information Age. Proceedings, Geo-Congress 2006: Geotechnical Engineering in the Information Technology Age, Atlanta, GA.
   Google Scholar
• Meng, J., H. Mattsson, and J. Laue. 2021. “Three-dimensional Slope Stability Predictions Using Artificial Neural Networks.” International Journal for Numerical and Analytical Methods in Geomechanics 45 (13): 1988–2000.
   Google Scholar
• Misra, S., H. Li, and J. He. 2019. Machine Learning for Subsurface Characterization. Elsevier.
   Google Scholar
• Mitchell, J. K., and J. Kopmann. 2013. The Future of Geotechnical Engineering. Report CGPR #70. Virginia Tech Center for Geotechnical Practice and Research.
   Google Scholar
• Moayedi, H., M. Mosallanezhad, A. S. A. Rashid, W. A. W. Jusoh, and M. A. Muazu. 2020. “A Systematic Review and Meta-Analysis of Artificial Neural Network Application in Geotechnical Engineering: Theory and Applications.” Neural Computing and Applications 32: 495–518.
   Google Scholar
• Morgenroth, J., U. T. Khan, and M. A. Perras. 2019. “An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design.” Geosciences 9 (12): 504. doi:10.3390/geosciences9120504.
   Web of Science ®Google Scholar
• National Research Council. 2006. Geological and Geotechnical Engineering in the New Millennium- Opportunities for Research and Technological Innovation. Washington, D.C.: The National Academies Press.
   Google Scholar
• Phoon, K. K. 2017. “Role of Reliability Calculations in Geotechnical Design.” Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards 11 (1): 4–21.
   Web of Science ®Google Scholar
• Phoon, K. K. 2018. “Editorial for Special Collection on Probabilistic Site Characterization.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 4 (4): Article 02018002.
   Google Scholar
• Phoon, K. K. 2020a. “The Story of Statistics in Geotechnical Engineering.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 14 (1): 3–25.
   Google Scholar
• Phoon, K. K. 2020b. “The Goldilocks Dilemma – Too Little or Too Much Data?” GeoStrata 24 (1): 14–15.
   Google Scholar
• Phoon, K. K., Z. Cao, Z. Liu, and J. Ching. Forthcoming. “Report for ISSMGE TC309/TC304/TC222 Third ML Dialogue on “Data-Driven Site Characterization (DDSC).” Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards.
   Google Scholar
• Phoon, K. K., and J. Ching. 2021. “Project DeepGeo - Data-Driven 3D Subsurface Mapping.” Journal of GeoEngineering 16 (2): 47–59. http://yo-1.ct.ntust.edu.tw/jge/files/articlefiles/v16i2202106031836319463.pdf
   Google Scholar
• Phoon, K. K., J. Ching, and Z. Cao. Forthcoming. “Unpacking Data-Centric Geotechnics. Underground Space.”
   Google Scholar
• Phoon, K. K., J. Ching, and T. Shuku. 2022. “Challenges in Data-Driven Site Characterization.” Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards 16 (1): 114–126. doi:10.1080/17499518.2021.1896005.
   Google Scholar
• Phoon, K. K., J. Ching, and Y. Wang. 2019. Managing Risk in Geotechnical Engineering – From Data to Digitalization. Proceedings, 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019), Taipei, Taiwan, 13–34. http://rpsonline.com.sg/proceedings/isgsr2019/pdf/SL.pdf.
   Google Scholar
• Phoon, K. K., T. Shuku, J. Ching, and Y. Ikumasa. 2022. “Benchmark Examples for Data-Driven Site Characterization.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, doi:10.1080/17499518.2022.2025541.
   Google Scholar
• Providakis, S., C. Rogers, and D. N. Chapman. 2019. “Predictions of Settlement Risk Induced by Tunnelling Using BIM and 3D Visualization Tools.” Tunnelling and Underground Space Technology 92: Article 103049.
   Google Scholar
• Qi, C., and X. Tang. 2018. “Slope Stability Prediction Using Integrated Metaheuristic and Machine Learning Approaches: A Comparative Study.” Computers & Industrial Engineering 118: 112–122.
   Web of Science ®Google Scholar
• Shahin, M. A. 2013. “Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions.” Chapter 8, Metaheuristics in Water, Geotechnical and Transport Engineering. Elsevier, 169–204.
   Google Scholar
• Shahin, M. A., M. B. Jaksa, and H. R. Maier. 2001. “Artificial Neural Network Applications in Geotechnical Engineering.” Australian Geomechanics 36 (1): 49–62.
   Google Scholar
• Shahin, M. A., M. B. Jaksa, and H. R. Maier. 2008. “State of the art of Artificial Neural Networks in Geotechnical Engineering.” Electronic Journal of Geotechnical Engineering 8 (1): 1–26.
   Google Scholar
• Shahri, A. A., J. Spross, F. Johansson, and S. Larsson. 2019. “Landslide Susceptibility Hazard map in Southwest Sweden Using Artificial Neural Network.” Catena 183: Article 104225.
   Google Scholar
• Sharma, A., J. Ching, and K. K. Phoon. 2022. “A Hierarchical Bayesian Similarity Measure for Geotechnical Site Retrieval.” Journal of Engineering Mechanics, ASCE. under review.
   Google Scholar
• Sheil, B. B., S. K. Suryasentana, M. A. Mooney, and H. Zhu. 2020. “Machine Learning to Inform Tunnelling Operations: Recent Advances and Future Trends.” Proceedings of the Institution of Civil Engineers – Smart Infrastructure and Construction 173 (4): 74–95.
   Google Scholar
• Shi, C., and Y. Wang. 2021. “Development of Subsurface Geological Cross-Section from Limited Site-Specific Boreholes and Prior Geological Knowledge Using Iterative Convolution XGBoost.” Journal of Geotechnical and Geoenvironmental Engineering 147 (9): Article 04021082.
   Google Scholar
• Syed, F. I., A. Alshamsi, A. K. Dahaghi, and S. Neghabhan. 2020. “Application of ML & AI to Model Petrophysical and Geo-mechanical Properties of Shale Reservoirs – A Systematic Literature Review.” Petroleum. In press.
   Google Scholar
• Tang, M. 2021. “Deep-Learning-Based Surrogate Modelling of Flow and Coupled Flow-Geomechanics for Data Assimilation in Subsurface Systems.” PhD thesis, Stanford University.
   Google Scholar
• Tang, C., and K. K. Phoon. 2021. Model Uncertainties in Foundation Design. Boca Raton: CRC Press.
   Google Scholar
• Tawelian, L. R., and S. B. Mickovski. 2016. “The Implementation of Geotechnical Data Into the BIM Process.” Procedia Engineering 143: 734–741.
   Google Scholar
• Transportation Research Board. 1999. Use of Artificial Neural Networks in Geomechanical and Pavement Systems. Transportation Research Circular E-C012, Washington D. C.
   Google Scholar
• Valeria, N., V. Roberta, C. Vittoria, and A. Domenico. 2019. A New Frontier of BIM Process: Geotechnical BIM. Proc., XVII European Conference on Soil Mechanics and Geotechnical Engineering (ECSMGE 2019), Reykjavik, Iceland. doi:10.32075/17ECSMGE-2019-0682.
   Google Scholar
• Vardanega, P. J., and M. D. Bolton. 2016. “Design of Geostructural Systems.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2 (1): Article 04015017.
   PubMedGoogle Scholar
• Vick, S. G. 2002. Degrees of Belief: Subjective Probability and Engineering Judgment. Reston, VA: ASCE Press.
   Google Scholar
• Wang, Y., X. Qi, W. Zhang, and J. Ching. 2022. “Editorial for Special Issue on “Data Analytics in Geotechnical and Geological Engineering”.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 16 (1): 1.
   Google Scholar
• Wang, H., X. Wang, and R. Liang. 2020. Study of AI Based Methods for Characterization of Geotechnical Site Investigation Data. FHWA/OH-2020-3. Ohio Department of Transportation.
   Google Scholar
• Wang, H., L. M. Zhang, H. Luo, J. He, and R. W. M. Cheung. 2021a. “AI-powered Landslide Susceptibility Assessment in Hong Kong. Engineering Geology.” Engineering Geology 288: Article 106103.
   Google Scholar
• Wang, H., L. M. Zhang, L. Wang, J. He, and H. Luo. 2021b. “An Automated Snow Mapper Powered by Machine Learning.” Remote Sensing 13 (23): 4826. doi:10.3390/rs13234826.
   Google Scholar
• Xiao, T., H. F. Zou, K. S. Yin, Y. Du, and L. M. Zhang. 2021. “Machine Learning-Enhanced Soil Classification by Integrating Borehole and CPTU Data with Noise Filtering.” Bulletin of Engineering Geology and the Environment 80 (12): 9157–9171.
   Google Scholar
• Xie, Y., M. Ebad Sichani, J. E. Padgett, and R. Desroches. 2020. “The Promise of Implementing Machine Learning in Earthquake Engineering: A State-of-the-Art Review.” Earthquake Spectra 36 (4): 1769–1801.
   Web of Science ®Google Scholar
• Yang, Y., and G. Mei. 2021. “Deep Transfer Learning Approach for Identifying Slope Surface Cracks.” Applied Sciences 11 (23). doi:10.3390/app112311193.
   Google Scholar
• Yousefpour, N., and S. Fallah. 2018. Application of Machine Learning in Geotechnics. Proceedings, Civil Engineering Research in Ireland Conference, Dublin, Ireland.
   Google Scholar
• Yuen, K. V., J. Y. Ching, and K. K. Phoon. 2021. “Editorial for Special Collection on “Bayesian Learning Methods for Geotechnical Data”.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems: Part A 7 (1). doi:10.1061/AJRUA6.0001102.
   Google Scholar
• Zhang, W., J. Ching, A. T. C. Goh, and A. Y. F. Leung. 2021a. “Big Data and Machine Learning in Geoscience and Geoengineering: Introduction.” Geoscience Frontiers 12: 327–329.
   Web of Science ®Google Scholar
• Zhang, W., H. Li, Y. Li, H. Liu, Y. Chen, and X. Ding. 2021b. “Application of Deep Learning Algorithms in Geotechnical Engineering: A Short Critical Review.” Artificial Intelligence Review 54: 5633–5673.
   Web of Science ®Google Scholar
• Zhang, W. G., and K. K. Phoon. 2022. “Editorial for Special Issue on “Advances and Applications of Deep Learning and Soft Computing in Geotechnical Underground Engineering”.” Journal of Rock Mechanics and Geotechnical Engineering. doi:10.1016/j.jrmge.2022.01.001.
   Google Scholar
• Zhang, J., K. K. Phoon, D. Zhang, H. Huang, and C. Tang. 2021a. “Deep Learning-Based Evaluation of Factor of Safety with Confidence Interval for Tunnel Deformation in Spatially Variable Soil.” Journal of Rock Mechanics and Geotechnical Engineering 13 (6): 1358–1367.
   Web of Science ®Google Scholar
• Zhang, J., K. K. Phoon, D. Zhang, H. Huang, and C. Tang. 2021b. “Novel Approach to Estimate Vertical Scale of Fluctuation Based on CPT Data Using Convolutional Neural Networks.” Engineering Geology 294: Article 106342.
   Google Scholar
• Zhang, J., C. Wu, Y. Wang, Y. Ma, Y. Wu, and X. Mao. 2018. “The BIM-Enabled Geotechnical Information Management of a Construction Project.” Computing 100 (1): 47–63.
   Google Scholar
• Zhang, W., C. Wu, H. Zhong, Y. Li, L. Wang. 2021c. “Prediction of Undrained Shear Strength Using Extreme Gradient Boosting and Random Forest Based on Bayesian Optimization.” Geoscience Frontiers 12: 469–477.
   Web of Science ®Google Scholar