2,188
Views
1
CrossRef citations to date
0
Altmetric
Spotlight Paper

Digital geotechnics: from data-driven site characterisation towards digital transformation and intelligence in geotechnical engineering

ORCID Icon &
Pages 8-32 | Received 07 Jun 2023, Accepted 29 Oct 2023, Published online: 06 Nov 2023

References

  • Alpaydin, E. 2020. Introduction to Machine Learning. Cambridge: MIT Press.
  • Augarde, C. E., S. J. Lee, and D. Loukidis. 2021. “Numerical Modelling of Large Deformation Problems in Geotechnical Engineering: A State-of-the-art Review.” Soils and Foundations 61 (6): 1718–1735. https://doi.org/10.1016/j.sandf.2021.08.007.
  • Baghbani, A., T. Choudhury, S. Costa, and J. Reiner. 2022. “Application of Artificial Intelligence in Geotechnical Engineering: A State-of-the-art Review.” Earth-Science Reviews 228: 103991. https://doi.org/10.1016/j.earscirev.2022.103991.
  • Bea, R. 2006. “Reliability and Human Factors in Geotechnical Engineering.” Journal of Geotechnical and Geoenvironmental Engineering 132 (5): 631–643. https://doi.org/10.1061/(ASCE)1090-0241(2006)132:5(631).
  • Bekele, Y. W. 2021. “Physics-informed Deep Learning for one-Dimensional Consolidation.” Journal of Rock Mechanics and Geotechnical Engineering 13 (2): 420–430. https://doi.org/10.1016/j.jrmge.2020.09.005.
  • Bishop, C. M. 2006. Pattern Recognition and Machine Learning. New York: Springer.
  • Brinkgreve, R. B. J., S. Kumarswamy, W. M. Swolfs, and F. Foria. 2018. PLAXIS 2D Material Models Manual. Delft: PLAXIS BV.
  • BSI. 2009. BS 6031:2009: Code of Practice for Earthworks. London: British Standards Institution.
  • Cai, L., Q. Gong, F. Jiang, M. Yuan, Z. Xiao, S. Zhang, C. Zheng, and Y. Wu. 2023. “The use of the Intelligent Bayesian Network Method Combined with Blockchain Technology in the Optimisation of Tunnel Construction Quality Control.” IET Software 17: 776–786. https://doi.org/10.1049/sfw2.12109.
  • Cao, Z., Y. Wang, and D. Li. 2016. “Quantification of Prior Knowledge in Geotechnical Site Characterization.” Engineering Geology 203: 107–116. https://doi.org/10.1016/j.enggeo.2015.08.018.
  • CEN.2004. EN 1997-1:2004: Eurocode 7: Geotechnical Design–Part 1: General Rules. Brussels: European Committee for Standardization.
  • Chapman, D., S. Providakis, and C. Rogers. 2020. “BIM for the Underground – An Enabler of Trenchless Construction.” Underground Space 5 (4): 354–361. https://doi.org/10.1016/j.undsp.2019.08.001.
  • Ching, J., W. H. Huang, and K. K. Phoon. 2020. “3D Probabilistic Site Characterization by Sparse Bayesian Learning.” Journal of Engineering Mechanics 146 (12): 04020134. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001859.
  • Ching, J., and K. K. Phoon. 2017. “Characterizing Uncertain Site-Specific Trend Function by Sparse Bayesian Learning.” Journal of Engineering Mechanics 143 (7): 04017028. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001240.
  • Ching, J., K. K. Phoon, Z. Yang, and A. W. Stuedlein. 2022. “Quasi-site-specific Multivariate Probability Distribution Model for Sparse, Incomplete, and Three-Dimensional Spatially Varying Soil Data.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 16 (1): 53–76. https://doi.org/10.1080/17499518.2021.1971256.
  • Ching, J., S. Wu, and K. K. Phoon. 2021. “Constructing Quasi-Site-Specific Multivariate Probability Distribution Using Hierarchical Bayesian Model.” Journal of Engineering Mechanics 147 (10): 04021069. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001964.
  • Clayton, C. R. I., M. C. Matthews, and N. E. Simons. 1995. Site Investigation. Cambridge, MA: Blackwell Science.
  • Dagger, R., D. Saftner, and P. Mayne. 2018. “Cone Penetration Test Design Guide for State Geotechnical Engineers.” Minnesota Department of Transportation. Retrieved from the University of Minnesota Digital Conservancy, https://hdl.handle.net/11299/203697.
  • Depina, I., S. Jain, S. Mar Valsson, and H. Gotovac. 2022. “Application of Physics-Informed Neural Networks to Inverse Problems in Unsaturated Groundwater Flow.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 16 (1): 21–36. https://doi.org/10.1080/17499518.2021.1971251.
  • Dumitrescu, B., and P. Irofti. 2018. Dictionary Learning Algorithms and Applications. Berlin.: Springer.
  • Dunnicliff, J. 1993. Geotechnical Instrumentation for Monitoring Field Performance. New York: Wiley.
  • 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?” In Slope Stability 2020: Proceedings of the 2020 International Symposium on Slope Stability in Open Pit Mining and Civil Engineering, edited by PM Dight, 565–576. Perth: Australian Centre for Geomechanics. https://doi.org/10.36487/ACG_repo/2025_34.
  • Gangeh, M. J., A. K. Farahat, A. Ghodsi, and M. S. Kamel. 2015. “Supervised Dictionary Learning and Sparse Representation – A Review.” arXiv preprint arXiv:1502.05928. https://doi.org/10.48550/arXiv.1502.05928.
  • Gerbert, P., S. Castagnino, C. Rothballer, A. Renz, and R. Filitz. 2016. “The Transformative Power of Building Information Modeling.” Boston Consulting Group. Accessed April 18, 2023. [Online]. https://www.bcg.com/publications/2016/engineered-products-infrastructure-digital-transformative-power-building-information-modeling.
  • Ghobakhloo, M., M. Fathi, M. Iranmanesh, P. Maroufkhani, and M. E. Morales. 2021. “Industry 4.0 ten Years on: A Bibliometric and Systematic Review of Concepts, Sustainability Value Drivers, and Success Determinants.” Journal of Cleaner Production 302: 127052. https://doi.org/10.1016/j.jclepro.2021.127052.
  • Gong, H., M. S. Kizil, Z. Chen, M. Amanzadeh, B. Yang, and S. M. Aminossadati. 2019. “Advances in Fibre Optic Based Geotechnical Monitoring Systems for Underground Excavations.” International Journal of Mining Science and Technology 29 (2): 229–238. https://doi.org/10.1016/j.ijmst.2018.06.007.
  • Griffiths, D. V., and G. A. Fenton. 2009. “Probabilistic Settlement Analysis by Stochastic and Random Finite-Element Methods.” Journal of Geotechnical and Geoenvironmental Engineering 135 (11): 1629–1637. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000126.
  • Guan, Z., and Y. Wang. 2022a. “Assessment of Liquefaction-Induced Differential Ground Settlement and Lateral Displacement Using Standard Penetration Tests with Consideration of Soil Spatial Variability.” Journal of Geotechnical and Geoenvironmental Engineering 148 (5): 04022018. https://doi.org/10.1061/(ASCE)GT.1943-5606.0002775.
  • Guan, Z., and Y. Wang. 2022b. “SPT-based Probabilistic Evaluation of Soil Liquefaction Potential Considering Design Life of Civil Infrastructures.” Computers and Geotechnics 148: 104807. https://doi.org/10.1016/j.compgeo.2022.104807.
  • Guan, Q. Z., Z. X. Yang, N. Guo, and Z. Hu. 2023. “Finite Element Geotechnical Analysis Incorporating Deep Learning-Based Soil Model.” Computers and Geotechnics 154: 105120. https://doi.org/10.1016/j.compgeo.2022.105120.
  • Hjørland, B., and H. Albrechtsen. 1995. “Toward a new Horizon in Information Science: Domain-Analysis.” Journal of the American Society for Information Science 46 (6): 400–425. https://doi.org/10.1002/(SICI)1097-4571(199507)46:6<400::AID-ASI2>3.0.CO;2-Y.
  • Honjo, Y. 2011. “Challenges in Geotechnical Reliability Based Design.” Geotechnical Safety and Risk. ISGSR 2011, 11–28. https://hdl.handle.net/20.500.11970/99549.
  • Hu, Y., and Y. Wang. 2020. “Probabilistic Soil Classification and Stratification in a Vertical Cross-Section from Limited Cone Penetration Tests Using Random Field and Monte Carlo Simulation.” Computers and Geotechnics 124: 103634. https://doi.org/10.1016/j.compgeo.2020.103634.
  • Hu, Y., Y. Wang, T. Zhao, and K. K. Phoon. 2020. “Bayesian Supervised Learning of Site-Specific Geotechnical Spatial Variability from Sparse Measurements.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 6 (2): 04020019. https://doi.org/10.1061/AJRUA6.0001059.
  • Huang, M. L., D. A. Sun, C. H. Wang, and Y. Keleta. 2021. “Reliability Analysis of Unsaturated Soil Slope Stability Using Spatial Random Field-Based Bayesian Method.” Landslides 18 (3): 1177–1189. https://doi.org/10.1007/s10346-020-01525-0.
  • Institution of Civil Engineers. 2017. “State of the Nation Report: Digital Transformation.” https://www.ice.org.uk/getattachment/news-and-insight/policy/state-of-thenation-2017-digital-transformation/ICE-SoN-Report-Web-Updated.pdf.aspx.
  • 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.
  • Institution of Civil Engineers. 2020. “Blockchain Technology in the Construction Industry. Digital Transformation: Overcoming Cultural and Behavioural Barriers.” https://www.ice.org.uk/media/ay2a1gtx/bluebeam-whitepaper-digital.pdf.
  • Jaksa, M., and Z. Liu. 2021. “Editorial for Special Issue “Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering.” Geosciences 11 (10): 399. https://doi.org/10.3390/geosciences11100399.
  • Ji, J., C. Zhang, Y. Gao, and J. Kodikara. 2019. “Reliability-based Design for Geotechnical Engineering: An Inverse FORM Approach for Practice.” Computers and Geotechnics 111: 22–29. https://doi.org/10.1016/j.compgeo.2019.02.027.
  • Jiang, S. H., D. Q. Li, L. M. Zhang, and C. B. Zhou. 2014. “Slope Reliability Analysis Considering Spatially Variable Shear Strength Parameters Using a non-Intrusive Stochastic Finite Element Method.” Engineering Geology 168: 120–128. https://doi.org/10.1016/j.enggeo.2013.11.006.
  • 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: 103946. https://doi.org/10.1016/j.tust.2021.103946.
  • Karniadakis, G. E., I. G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, and L. Yang. 2021. “Physics-informed Machine Learning.” Nature Reviews Physics 3 (6): 422–440. https://doi.org/10.1038/s42254-021-00314-5.
  • Kreutz-Delgado, K., J. F. Murray, B. D. Rao, K. Engan, T. W. Lee, and T. J. Sejnowski. 2003. “Dictionary Learning Algorithms for Sparse Representation.” Neural Computation 15 (2): 349–396. https://doi.org/10.1162/089976603762552951.
  • LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–444. https://doi.org/10.1038/nature14539.
  • Li, D., Y. Chen, W. Lu, and C. Zhou. 2011. “Stochastic Response Surface Method for Reliability Analysis of Rock Slopes Involving Correlated non-Normal Variables.” Computers and Geotechnics 38 (1): 58–68. https://doi.org/10.1016/j.compgeo.2010.10.006.
  • Li, D. Q., D. Zheng, Z. J. Cao, X. S. Tang, and K. K. Phoon. 2016. “Response Surface Methods for Slope Reliability Analysis: Review and Comparison.” Engineering Geology 203: 3–14. https://doi.org/10.1016/j.enggeo.2015.09.003.
  • Liu, X., Y. Wang, R. C. Koo, and J. S. Kwan. 2022. “Development of a Slope Digital Twin for Predicting Temporal Variation of Rainfall-Induced Slope Instability Using Past Slope Performance Records and Monitoring Data.” Engineering Geology 308: 106825. https://doi.org/10.1016/j.enggeo.2022.106825.
  • Loehr, J. E., A. Lutenegger, B. L. Rosenblad, A. Boeckmann, and P. Brinckerhoff. 2016. Geotechnical Site Characterization Geotechnical Engineering Circular No. 5 (No. FHWA-NHI-16-072). National Highway Institute (US).
  • Low, B. K., and K. K. Phoon. 2015. “Reliability-Based Design and Its Complementary Role to Eurocode 7 Design Approach.” Computers and Geotechnics 65: 30–44. https://doi.org/10.1016/j.compgeo.2014.11.011.
  • Ma, Z., and G. Mei. 2021. “Deep Learning for Geological Hazards Analysis: Data, Models, Applications, and Opportunities.” Earth-Science Reviews 223: 103858. https://doi.org/10.1016/j.earscirev.2021.103858.
  • Marr, W. A. 2006. “Geotechnical Engineering and Judgment in the Information Age.” In GeoCongress 2006: Geotechnical Engineering in the Information Technology Age, 1–17. https://doi.org/10.1061/40803(187)4.
  • Mayne, P. W., and J. Peuchen. 2013. “Unit Weight Trends with Cone Resistance in Soft to Firm Clays.” In Geotechnical and Geophysical Site Characterization: Proceedings of the 4th International Conference on Site Characterization ISC-4. Vol. 1, edited by Roberto Quental Coutinho and Paul W. Mayne, 903–910. London: Taylor & Francis Books Ltd.
  • 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 (2): 495–518. https://doi.org/10.1007/s00521-019-04109-9.
  • Molnar, C. 2019. “Interpretable Machine Learning.” https://christophm.github.io/interpretable-ml-book/.
  • Murphy, K. P. 2022. Probabilistic Machine Learning: An Introduction. Cambridge, MA: MIT Press.
  • Niazi, F. 2021. CPT-Based Geotechnical Design Manual, Volume 1: CPT Interpretation—Estimation of Soil Properties. https://doi.org/10.5703/1288284317346.
  • Olshausen, B. A., and D. J. Field. 1996. “Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images.” Nature 381 (6583): 607–609. https://doi.org/10.1038/381607a0.
  • Orr, T. L. L. 2005. “Design Examples for the Eurocode 7 Workshop.” Proceedings of the International Workshop on the Evaluation of Eurocode 7, Trinity College, Dublin, 67–74.
  • Özkahriman, F. 2004. “CPT Based Compressibility Assessment of Soils.” Master’s thesis, Middle East Technical University.
  • Pamukcu, S., and L. Cheng. 2017. Underground Sensing: Monitoring and Hazard Detection for Environment and Infrastructure. London: Academic Press.
  • Pathmanandavel, S., and C. J. MacRobert. 2020. “Digitisation, Sustainability, and Disruption – Promoting a More Balanced Debate on Risk in the Geotechnical Community.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 14 (4): 246–259. https://doi.org/10.1080/17499518.2020.1790012.
  • Peng, M., R. Sun, J. F. Chen, S. Rajesh, L. M. Zhang, and S. B. Yu. 2020. “System Reliability Analysis of Geosynthetic Reinforced Soil Slope Considering Local Reinforcement Failure.” Computers and Geotechnics 123: 103563. https://doi.org/10.1016/j.compgeo.2020.103563.
  • Perera, S., S. Nanayakkara, M. N. N. Rodrigo, S. Senaratne, and R. Weinand. 2020. “Blockchain Technology: Is it Hype or Real in the Construction Industry?” Journal of Industrial Information Integration 17: 100125. https://doi.org/10.1016/j.jii.2020.100125.
  • Phoon, K. K. 2008. Reliability-based Design in Geotechnical Engineering: Computations and Applications. London: CRC Press.
  • Phoon, K. K. 2017. “Role of Reliability Calculations in Geotechnical Design.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 11 (1): 4–21. https://doi.org/10.1080/17499518.2016.1265653.
  • Phoon, K. K. 2018. “Probabilistic Site Characterization.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 4 (4): 02018002. https://doi.org/10.1061/AJRUA6.0000992.
  • Phoon, K. K., Z. J. Cao, J. Ji, Y. F. Leung, S. Najjar, T. Shuku, C. Tang, Z. Y. Yin, Y. Ikumasa, and J. Ching. 2022. “Geotechnical Uncertainty, Modeling, and Decision Making.” Soils and Foundations 62 (5): 101189. https://doi.org/10.1016/j.sandf.2022.101189.
  • Phoon, K. K., Z. Cao, Z. Liu, and J. Ching. 2023. “Report for ISSMGE TC309/TC304/TC222 Third ML Dialogue on “Data-Driven Site Characterization (DDSC)” 3 December 2021, Norwegian Geotechnical Institute, Oslo, Norway.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 17 (1): 227–238. https://doi.org/10.1080/17499518.2022.2105366.
  • Phoon, K. K., J. Ching, and Z. Cao. 2022. “Unpacking Data-Centric Geotechnics.” Underground Space 7 (6): 967–989. https://doi.org/10.1016/j.undsp.2022.04.001.
  • Phoon, K. K., J. Ching, and T. Shuku. 2022. “Challenges in Data-Driven Site Characterization.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 16 (1): 114–126. https://doi.org/10.1080/17499518.2021.1896005.
  • 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. https://doi.org/10.3850/978-981-11-2725-0SL-cd.
  • Phoon, K. K., and J. V. Retief. 2016. Reliability of Geotechnical Structures in ISO2394. London: CRC Press.
  • Phoon, K. K., T. Shuku, J. Ching, and I. Yoshida. 2022. “Benchmark Examples for Data-Driven Site Characterisation.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 16 (4): 599–621. https://doi.org/10.1080/17499518.2022.2025541.
  • Phoon, K. K., and W. Zhang. 2022. “Future of Machine Learning in Geotechnics.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 17: 7–22. https://doi.org/10.1080/17499518.2022.2087884.
  • Phoon, K. K., L. M. Zhang, and Z. J. Cao. 2023. “Special Issue on ‘Machine Learning and AI in Geotechnics’.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 17 (1): 1–6. https://doi.org/10.1080/17499518.2023.2185938.
  • Providakis, S., C. D. 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: 103049. https://doi.org/10.1016/j.tust.2019.103049.
  • Robertson, P. K. 1990. “Soil Classification Using the Cone Penetration Test.” Canadian Geotechnical Journal 27 (1): 151–158. https://doi.org/10.1139/t90-014.
  • Robertson, P. K., and K. Cabal. 2010. Guide to Cone Penetration Testing for Geotechnical Engineering. California: Gregg Drilling & Testing, Inc.
  • Robertson, P. K., and C. E. Wride. 1998. “Evaluating Cyclic Liquefaction Potential Using the Cone Penetration Test.” Canadian Geotechnical Journal 35 (3): 442–459. https://doi.org/10.1139/t98-017.
  • Rubinstein, R., A. M. Bruckstein, and M. Elad. 2010. “Dictionaries for Sparse Representation Modeling.” Proceedings of the IEEE 98 (6): 1045–1057. https://doi.org/10.1109/JPROC.2010.2040551.
  • Schwab, K. 2017. The Fourth Industrial Revolution. New York: World Economic Forum/Penguin.
  • Sharon, R., and E. Eberhardt. 2020. Guidelines for Slope Performance Monitoring. Clayton: CSIRO Publishing.
  • Shi, C., and Y. Wang. 2021a. “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): 04021082. https://doi.org/10.1061/(ASCE)GT.1943-5606.0002583.
  • Shi, C., and Y. Wang. 2021b. “Nonparametric and Data-Driven Interpolation of Subsurface Soil Stratigraphy from Limited Data Using Multiple Point Statistics.” Canadian Geotechnical Journal 58 (2): 261–280. https://doi.org/10.1139/cgj-2019-0843.
  • Shi, C., and Y. Wang. 2021c. “Smart Determination of Borehole Number and Locations for Stability Analysis of Multi-Layered Slopes Using Multiple Point Statistics and Information Entropy.” Canadian Geotechnical Journal 58 (11): 1669–1689. https://doi.org/10.1139/cgj-2020-0327.
  • Shi, C., and Y. Wang. 2022a. “Assessment of Reclamation-Induced Consolidation Settlement Considering Stratigraphic Uncertainty and Spatial Variability of Soil Properties.” Canadian Geotechnical Journal 59 (7): 1215–1230. https://doi.org/10.1139/cgj-2021-0349.
  • Shi, C., and Y. Wang. 2022b. “Data-driven Construction of Three-Dimensional Subsurface Geological Models from Limited Site-Specific Boreholes and Prior Geological Knowledge for Underground Digital Twin.” Tunnelling and Underground Space Technology 126: 104493. https://doi.org/10.1016/j.tust.2022.104493.
  • Shi, C., and Y. Wang. 2023. “Data-driven Spatio-Temporal Analysis of Consolidation for Rapid Reclamation.” Géotechnique, https://doi.org/10.1680/jgeot.22.00016.
  • Shuku, T., and K. K. Phoon. 2021. “Three-dimensional Subsurface Modeling Using Geotechnical Lasso.” Computers and Geotechnics 133: 104068. https://doi.org/10.1016/j.compgeo.2021.104068.
  • Simpson, B. 2011. “Reliability in Geotechnical Design – Some Fundamentals.” Paper presented at the proceedings, third international symposium on Geotechnical Safety & Risk, Federal Waterways Engineering and Research Institute, Germany, June 2–3, 393–399.
  • Soga, K., E. Alonso, A. Yerro, K. Kumar, and S. Bandara. 2016. “Trends in Large-Deformation Analysis of Landslide Mass Movements with Particular Emphasis on the Material Point Method.” Géotechnique 66 (3): 248–273. https://doi.org/10.1680/jgeot.15.LM.005.
  • Soga, K., A. Ewais, J. Fern, and J. Park. 2019. “Advances in Geotechnical Sensors and Monitoring.” Geotechnical Fundamentals for Addressing New World Challenges, 29–65. https://doi.org/10.1007/978-3-030-06249-1_2
  • Soga, K., and J. Schooling. 2016. “Infrastructure Sensing.” Interface Focus 6 (4): 20160023. https://doi.org/10.1098/rsfs.2016.0023.
  • Srivastava, A., and G. S. Babu. 2009. “Effect of Soil Variability on the Bearing Capacity of Clay and in Slope Stability Problems.” Engineering Geology 108 (1-2): 142–152. https://doi.org/10.1016/j.enggeo.2009.06.023.
  • Sudhakaran, S. P., A. K. Sharma, and S. Kolathayar. 2018. “Soil Stabilization Using Bottom ash and Areca Fiber: Experimental Investigations and Reliability Analysis.” Journal of Materials in Civil Engineering 30 (8): 04018169. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002326.
  • Tang, C. S. C., and S. P. Y. Cheung. 2011. Review of Real-Time Data Transmission Systems for Slope Instrumentation. GEO Report No. 262. Hong Kong: Geotechnical Engineering Office, The Government of the Hong Kong Special Administrative Region.
  • Tian, H. M., Z. J. Cao, D. Q. Li, W. Du, and F. P. Zhang. 2022. “Efficient and Flexible Bayesian Updating of Embankment Settlement on Soft Soils Based on Different Monitoring Datasets.” Acta Geotechnica 17 (4): 1273–1294. https://doi.org/10.1007/s11440-021-01378-4.
  • Tian, H., D. Q. Li, Z. J. Cao, and W. Du. 2023. “Auxiliary Bayesian Updating of Embankment Settlement Based on Finite Element Model and Response Surface Method.” Engineering Geology 323: 107244. https://doi.org/10.1016/j.enggeo.2023.107244.
  • Tian, H., and Y. Wang. 2023. “Data-driven and Physics-Informed Bayesian Learning of Spatiotemporally Varying Consolidation Settlement from Sparse Site Investigation and Settlement Monitoring Data.” Computers and Geotechnics 157: 1–18. https://doi.org/10.1016/j.compgeo.2023.105328.
  • Tošić, I., and P. Frossard. 2011. “Dictionary Learning.” IEEE Signal Processing Magazine 28 (2): 27–38. https://doi.org/10.1109/MSP.2010.939537.
  • Wang, Y. 2011. “Reliability-based Design of Spread Foundations by Monte Carlo Simulations.” Géotechnique 61 (8): 677–685. https://doi.org/10.1680/geot.10.P.016.
  • Wang, Y., O. V. Akeju, and T. Zhao. 2017. “Interpolation of Spatially Varying but Sparsely Measured geo-Data: A Comparative Study.” Engineering Geology 231: 200–217. https://doi.org/10.1016/j.enggeo.2017.10.019.
  • Wang, Y., Z. Cao, and D. Li. 2016. “Bayesian Perspective on Geotechnical Variability and Site Characterization.” Engineering Geology 203: 117–125. https://doi.org/10.1016/j.enggeo.2015.08.017.
  • Wang, Y., Y. Hu, and T. Y. Zhao. 2020. “Cone Penetration Test (CPT)-Based Subsurface Soil Classification and Zonation in two-Dimensional Vertical Cross Section Using Bayesian Compressive Sampling.” Canadian Geotechnical Journal 57 (7): 947–958. https://doi.org/10.1139/cgj-2019-0131.
  • Wang, Y., C. Shi, and X. Li. 2022. “Machine Learning of Geological Details from Borehole Logs for Development of High-Resolution Subsurface Geological Cross-Section and Geotechnical Analysis.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 16 (1): 2–20. https://doi.org/10.1080/17499518.2021.1971254.
  • Wang, Z. Z., C. Xiao, S. H. Goh, and M. X. Deng. 2021. “Metamodel-based Reliability Analysis in Spatially Variable Soils Using Convolutional Neural Networks.” Journal of Geotechnical and Geoenvironmental Engineering 147 (3): 04021003. https://doi.org/10.1061/(ASCE)GT.1943-5606.0002486.
  • Wang, Y., W. Zhang, X. Qi, and J. Ching. 2022. “Data Analytics in Geotechnical and Geological Engineering.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 16 (1): 1–1. https://doi.org/10.1080/17499518.2022.2038205.
  • Wang, Y., and T. Zhao. 2017. “Statistical Interpretation of Soil Property Profiles from Sparse Data Using Bayesian Compressive Sampling.” Géotechnique 67 (6): 523–536. https://doi.org/10.1680/jgeot.16.P.143.
  • Wang, Y., T. Zhao, and K. K. Phoon. 2018. “Direct Simulation of Random Field Samples from Sparsely Measured Geotechnical Data with Consideration of Uncertainty in Interpretation.” Canadian Geotechnical Journal 55 (6): 862–880. https://doi.org/10.1139/cgj-2017-0254.
  • Wood, D. M. 2004. Geotechnical Modelling (Applied Geotechnics; V. 1). London: Taylor & Francis Group/Books.
  • Xiao, T., D. Q. Li, Z. J. Cao, S. K. Au, and K. K. Phoon. 2016. “Three-dimensional Slope Reliability and Risk Assessment Using Auxiliary Random Finite Element Method.” Computers and Geotechnics 79: 146–158. https://doi.org/10.1016/j.compgeo.2016.05.024.
  • Yoshida, I., Y. Tomizawa, and Y. Otake. 2021. “Estimation of Trend and Random Components of Conditional Random Field Using Gaussian Process Regression.” Computers and Geotechnics 136: 104179. https://doi.org/10.1016/j.compgeo.2021.104179.
  • Yuen, K. V., J. Ching, and K. K. Phoon. 2021. “Bayesian Learning Methods for Geotechnical Data.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 7 (1): 02020002-1. https://doi.org/10.1061/AJRUA6.0001102.
  • Zdravkovic, L., and J. Carter. 2008. “Contributions to Géotechnique 1948–2008: Constitutive and Numerical Modelling.” Géotechnique 58 (5): 405–412. https://doi.org/10.1680/geot.2008.58.5.405.
  • Zdravković, L., D. M. Potts, and D. M. Taborda. 2021. “Integrating Laboratory and Field Testing Into Advanced Geotechnical Design.” Geomechanics for Energy and the Environment 27: 100216. https://doi.org/10.1016/j.gete.2020.100216.
  • Zhang, W., X. Gu, L. Han, J. Wu, Z. Xiao, M. Liu, and L. Wang. 2022. “A Short Review of Probabilistic Slope Stability Analysis Considering Spatial Variability of Geomaterial Parameters.” Innovative Infrastructure Solutions 7 (4): 249. https://doi.org/10.1007/s41062-022-00845-5.
  • Zhang, W., H. Li, Y. Li, H. Liu, Y. Chen, and X. Ding. 2021. “Application of Deep Learning Algorithms in Geotechnical Engineering: A Short Critical Review.” Artificial Intelligence Review, 5633–5673. https://doi.org/10.1007/s10462-021-09967-1.
  • Zhang, P., Z. Y. Yin, and B. Sheil. 2023. “A Physics-Informed Data-Driven Approach for Consolidation Analysis.” Géotechnique, https://doi.org/10.1680/jgeot.22.00046.
  • Zhao, T., Y. Hu, and Y. Wang. 2018. “Statistical Interpretation of Spatially Varying 2D geo-Data from Sparse Measurements Using Bayesian Compressive Sampling.” Engineering Geology 246: 162–175. https://doi.org/10.1016/j.enggeo.2018.09.022.
  • Zhao, T., S. Montoya-Noguera, K. K. Phoon, and Y. Wang. 2018. “Interpolating Spatially Varying Soil Property Values from Sparse Data for Facilitating Characteristic Value Selection.” Canadian Geotechnical Journal 55 (2): 171–181. https://doi.org/10.1139/cgj-2017-0219.
  • Zhao, T., and Y. Wang. 2020a. “Non-parametric Simulation of non-Stationary non-Gaussian 3D Random Field Samples Directly from Sparse Measurements Using Signal Decomposition and Markov Chain Monte Carlo (MCMC) Simulation.” Reliability Engineering & System Safety 203: 107087. https://doi.org/10.1016/j.ress.2020.107087.
  • Zhao, T., and Y. Wang. 2020b. “Interpolation and Stratification of Multilayer Soil Property Profile from Sparse Measurements Using Machine Learning Methods.” Engineering Geology 265: 105430. https://doi.org/10.1016/j.enggeo.2019.105430.
  • Zhou, Z., D. Q. Li, T. Xiao, Z. J. Cao, and W. Du. 2021. “Response Surface Guided Adaptive Slope Reliability Analysis in Spatially Varying Soils.” Computers and Geotechnics 132: 103966. https://doi.org/10.1016/j.compgeo.2020.103966.
  • Zhu, H., A. Garg, X. B. Yu, and H. W. Zhou. 2022. “Editorial for Internet of Things (IoT) and Artificial Intelligence (AI) in Geotechnical Engineering.” Journal of Rock Mechanics and Geotechnical Engineering 14 (4): 1025–1027. https://doi.org/10.1016/j.jrmge.2022.07.001.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.