Reference List for Machine Learning and its Applications in Geotechnical Engineering - PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING

1    Artificial neural networks
[1]    Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2001). Artificial Neural Network Applications in Geotechnical Engineering. Australian Geomechanics, 36(1), 4962.
[2]    Shahin, M. A., Jaksa, M. B. and Maier, H. R. (2008). Invited Paper: State of the Art of Artificial Neural Networks in Geotechnical Engineering.  Electronic Journal of Geotechnical Engineering, 8, 1-26.
[3]    Jaksa, M. B., Maier, H. R. and Shahin, M. A. (2008).  Invited Paper: Future Challenges for Artificial Neural Network Modelling in Geotechnical Engineering. Proc. 12th Int. Assoc. for Computer Methods and Advances in Geotech. Engrg. Conference, Goa, India, October 16, 17101719.
[4]    Shahin, M. A., Jaksa, M. B. and Maier, H. R. (2009).  Invited Paper: Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications. Advances in Artificial Neural Systems, Vol. 2009 (2009), Article ID 308239, 9, DOI: 10.1155/2009/308239.
1.1    Site Characterization
[5]    Zhou, Y., and Wu, X. (1994). Use of neural networks in the analysis and interpretation of site investigation data. Computer and Geotechnics, 16, 105-122.
[6]    Cal, Y. (1995). Soil classification by neural-network. Advances in Engineering Software, 22(2), 95-97.
[7]    Basheer, I. A., Reddi, L. N., and Najjar, Y. M. (1996). Site characterization by neuronets: An application to the landfill siting problem. Ground Water, 34, 610-617.
[8]    Rizzo, D. M., Lillys, T. P., and Dougherty, D. E. (1996). Comparisons of site characterization methods using mixed data. Geotechnical Special Publication, ASCE, 58(1), 157-179.
[9]    Najjar, Y. M., and Basheer, I. A. (1996). Neural network approach for site characterization and uncertainty prediction. Geotechnical Special Publication, ASCE, 58(1), 134-148.
[10]    Juang C. H., Jiang T., and Christopher R. A. (2001). Three-dimensional site characterization: neural network approach. Geotechnique, 51(9), 799809.
1.2    Geomaterial Properties and Behavior Modeling
[11]    Agrawal, G., Weeraratne, S., and Khilnani, K. (1994). Estimating clay liner and cover permeability using computational neural networks. Proc., First Congress on Computing in Civil Engineering., Washington, June 20-22.
[12]    Gribb, M. M., and Gribb, G. W. (1994). Use of neural networks for hydraulic conductivity determination in unsaturated soil. Proc., 2nd Int. Conf. Ground Water Ecology, J. A. Stanford and H. M. Valett, eds., Bethesda MD: Amer, Water Resources Assoc., 155-163.
[13]    Goh, A. T. C. (1995). Modeling soil correlations using neural networks. Journal of Computing in Civil Engineering, ASCE, 9(4), 275-278.
[14]    Ellis, G. W., Yao, C., Zhao, R., and Penumadu, D. (1995). Stress-strain modelling of sands using artificial neural networks. Journal of Geotechnical Engineering., ASCE, 121(5), 429-435.
[15]    Najjar, Y. M., and Basheer, I. A. (1996). Utilizing computational neural networks for evaluating the permeability of compacted clay liners. Geotechnical and Geological Engineering, 14, 193-221.
[16]    Najjar, Y. M., Basheer, I. A., and McReynolds, R. (1996). Neural modeling of Kansan soil swelling. Transportation Research Record, No. 1526, 14-19.
[17]    Romero, S., and Pamukcu, S. (1996). Characterization of granular material by low strain dynamic excitation and ANN. Geotechnical Special Publication, ASTM-ASCE, 58(2), 1134-1148.
[18]    Penumadu, D., and Jean-Lou, C. (1997). Geomaterial modeling using artificial neural networks. Artificial Neural Networks for Civil Engineers: Fundamentals and Applications, ASCE, 160-184.
[19]    Sidarta, D. E., and Ghaboussi, J. (1998). Constitutive modeling of geomaterials from non-uniform material tests. Computers and Geomechanics, 22(10), 53-71.
[20]    Ghaboussi, J., and Sidarta, D. E. (1998). New nested adaptive neural networks (NANN) for constitutive modeling. Computers and Geotechnics, 22(1), 29-52.
[21]    Zhu, J. H., Zaman, M. M., and Anderson, S. A. (1998). Modeling of soil behavior with a recurrent neural network. Canadian Geotechnical Journal, 35(5), 858-872.
[22]    Zhu, J. H., Zaman, M. M., and Anderson, S. A. (1998). Modelling of shearing behavior of a residual soil with recurrent neural network. International Journal of Numerical and Analytical Methods in Geomechanics, 22(8), 671-687.
[23]    Penumadu, D., and Zhao, R. (1999). Triaxial compression behavior of sand and gravel using artificial neural networks (ANN). Computers and Geotechnics, 24, 207-230.
1.3    Pile Capacity and Settlement
[24]    Goh, A. T. C. (1994). Nonlinear modelling in geotechnical engineering using neural networks. Australian Civil Engineering Transactions, CE36(4), 293-297.
[25]    Goh, A. T. C. (1995). Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9, 143-151.
[26]    Goh, A. T. C. (1995). Empirical design in geotechnics using neural networks. Géotechnique, 45(4), 709714.
[27]    Goh, A. T. C. (1996). Pile driving records reanalyzed using neural networks. Journal of Geotechnical Engineering, ASCE, 122(6), 492-495.
[28]    Chan, W. T., Chow, Y. K., and Liu, L. (1995). Neural network: An alternative to pile driving formulas. Computers and Geotechnics, 17, 135-156.
[29]    Lee, I. M., and Lee, J. H. (1996). Prediction of pile bearing capacity using artificial neural networks. Computers and Geotechnics, 18(3), 189-200.
[30]    Abu-Kiefa, M. A. (1998). General regression neural networks for driven piles in cohesionless soils. Journal of Geotechnical and Geoenvironmental Engineering., ASCE, 124(12), 1177-1185
[31]    Teh, C. I., Wong, K. S., Goh, A. T. C., and Jaritngam, S. (1997). Prediction of pile capacity using neural networks. Journal of Computing in Civil Engineering, ASCE, 11(2), 129-138.
[32]    Das, S. K., and Basudhar, P. K. (2006). Undrained lateral load capacity of piles in clay using artificial neural network. Computers and Geotechnics, 33(8), 4549.
[33]    Shahin, M. A. and Jaksa, M. B. (2009). Intelligent Computing for Predicting Axial Capacity of Drilled Shafts. In Contemporary Topics in In Situ Testing, Analysis, and Reliability of Foundations, Geotechnical Special Publication No. 186, ASCE, pp. 2633.
[34]    Pooya Nejad, F. and Jaksa, M. B. (2017). Load-settlement Behavior Modeling of Single Piles Using Artificial Neural Networks and CPT Data.  Computers and Geotechnics, Vol. 89, pp. 921.
1.4    Shallow Foundations
[35]    Sivakugan, N., Eckersley, J. D., and Li, H. (1998). Settlement predictions using neural networks. Australian Civil Engineering Transactions, CE40, 49-52.
[36]    Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2000). Predicting the settlement of shallow foundations on cohesionless soils using back-propagation neural networks. Research Report No. R 167, the University of Adelaide, Adelaide.
[37]    Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2002). Predicting Settlements of Shallow Foundations Using Neural Networks. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 128, (9), 785793.
[38]    Shahin, M. A., Jaksa, M. B. and Maier, H. R. (2002). Artificial Neural Network-Based Settlement Prediction Formula for Shallow Foundations on Granular Soils. Australian Geomechanics, 37, (4), 4552.
[39]    Shahin, M. A., Maier, H. R. and Jaksa, M. B. (2003). Neural and Neurofuzzy Techniques Applied to Modelling Settlement of Shallow Foundations on Granular Soils. International Congress on Modelling and Simulation, MODSIM 2003, D. A. Post (ed.), Townsville, July 1417, 4, 18861891.
[40]    Shahin, M. A., Maier, H. R. and Jaksa, M. B. (2003). Settlement Prediction of Shallow Foundations on Granular Soils Using B-Spline Neurofuzzy Models. Computers and Geotechnics, 30, (8), 637647.
[41]    Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2004). Data division for developing neural networks applied to geotechnical engineering. Journal of Computing in Civil Engineering, 18(2), 105-114.
[42]    Shahin, M. A., Jaksa, M. B. and Maier, H. R. (2005). Neural Network Based Stochastic Design Charts for Settlement Prediction. Canadian Geotechnical Journal, 42, (1): 110120.
[43]    Shahin, M. A., Jaksa, M. B. and Maier, H. R. (2005). Stochastic Simulation of Settlement Prediction of Shallow Foundations Based on a Deterministic Artificial Neural Network Model. In MODSIM 2005 International Congress on Modelling and Simulation, Zerger, A. and Argent, R.M. (eds), Modelling and Simulation Society of Australia and New Zealand, Melbourne, December 1215, pp. 7378. 
[44]    Kuo, Y. L., Jaksa, M. B., Lyamin, A. V. and Kaggwa, W. S. (2009).  ANN-based Model for Predicting the Bearing Capacity of Strip Footing on Multi-layered Cohesive Soil. Computers and Geotechnics, 36 (3), 503516.
1.5    Tensile Capacity of Anchors
[45]    Shahin, M. A. and Jaksa, M. B. (2004). Probabilistic Assessment of the Uncertainty Associated with the Pullout Capacity of Marquee Anchors.  Proc. 9th Australia New Zealand Conference on Geomechanics, Auckland, February 911, Vol. 2, pp. 917921.
[46]    Shahin, M. A. and Jaksa, M. B. (2005). Neural Network Prediction of Pullout Capacity of Marquee Ground Anchors. Computers and Geotechnics, 32(3), 153163.
[47]    Shahin, M. A. and Jaksa, M. B. (2005). Modelling the Pullout Capacity of Marquee Ground Anchors Using Neurofuzzy Technique. In MODSIM 2005 International Congress on Modelling and Simulation, Zerger, A. and Argent, R.M. (eds), Modelling and Simulation Society of Australia and New Zealand, Melbourne, December 1215, pp. 6672.  
[48]    Shahin, M. A. and Jaksa, M. B. (2006). Pullout Capacity of Small Ground Anchors by Direct CPT Methods and Neural Networks. Canadian Geotechnical Journal, 43(6), 626637.
1.6    Liquefaction
[49]    Goh, A. T. C. (1994). Seismic liquefaction potential assessed by neural network. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 120(9), 1467-1480.
[50]    Goh, A. T. C. (1996). Neural-network modeling of CPT seismic liquefaction data. Journal of Geotechnical Engineering, ASCE, 122(1), 70-73.
[51]    Najjar, Y. M., and Ali, H. E. (1998). CPT-based liquefaction potential assessment: A neuronet approach. Geotechnical Special Publication, ASCE, 1, 542-553
[52]    Agrawal, G., Chameau, J. A., and Bourdeau, P. L. (1997). Assessing the liquefaction susceptibility at a site based on information from penetration testing. In Artificial neural networks for civil engineers: fundamentals and applications, N. Kartam, I. Flood, and J. H. Garrett, eds., New York, 185-214.
[53]    Juang, C. H., and Chen, C. J. (1999). CPT-based liquefaction evaluation using artificial neural networks. Computer Aided Civil and Infrastructure Engineering, 14(3), 221-229.
[54]    Goh A. T. C. (2002). Probabilistic neural network for evaluating seismic liquefaction potential. Canadian Geotechnical Journal, 39(2), 21932.
1.7    Soil Retaining Structures
[55]    Goh, A. T. C., Wong, K. S., and Broms, B. B. (1995). Estimation of lateral wall movements in braced excavation using neural networks. Canadian Geotechnical Journal, 32, 1059-1064.
1.8    Slope Stability
[56]    Ni, S. H., Lu, P. C., and Juang, C. H. (1996). A fuzzy neural network approach to evaluation of slope failure potential. Microcomputers in Civil Engineering, 11, 59-66.
[57]    Chok, Y. H., Jaksa, M. B., Kaggwa W. S., Griffiths, D. V. and Fenton, G. A. (2016). Neural Network Prediction of the Reliability of Heterogeneous Cohesive Slopes. International Journal for Numerical & Analytical Methods in Geomechanics, 40, 11:1556-1569.
1.9    Tunnels and Underground Openings
[58]    Lee, C., and Sterling, R. (1992). Identifying probable failure modes for underground openings using a neural network. International Journal Rock Mechanics and Mining Science and Geomechanics Abstracts, 29(1), 49-67.
[59]    Sterling, R. L., and Lee, C. A. (1992). A neural network - expert system hybrid approach for tunnel design. Proc., 33rd United-States Symp. Rock Mechanics, J. R. Tillerson and W. R. Wawerisk, eds., Brookfield VT: Balkema, 501-510.
[60]    Moon, H. K., Na, S. M., and Lee, C. W. (1995). Artificial neural-network integrated with expert-system for preliminary design of tunnels and slopes. Proc., 8th Int. Congress on Rock Mechanics, T. Fujii, ed., Rotterdam: Balkema, 1 and 2, 901-905.
[61]    Shi, J. J., Ortigao, J. A. R., and Bai, J. (1998). Modular neural networks for predicting settlement during tunneling. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 124(5), 389-395.
[62]    Shi, J. J. (2000). Reducing prediction error by transforming I nput data for neural networks. Journal of Computing in Civil Engineering, ASCE, 14(2), 109-116.
1.10    Ground Improvement
[63]    Ranasinghe, R. A. T. M., Jaksa, M. B., Kuo, Y. L. and Pooya Nejad, F. (2017). Application of Artificial Neural Networks for Predicting the Impact of Rolling Dynamic Compaction Using Dynamic Cone Penetrometer Test Results.  Journal of Rock Mechanics and Geotechnical Engineering, 9(2), 340349.
[64]    Ranasinghe, R. A. T. M., Jaksa, M. B., Pooya Nejad, F. and Kuo, Y. L. (2017). Predicting the Effectiveness of Rolling Dynamic Compaction Using Genetic Programming and Cone Penetration Test Data. Proc. of Institution of Civil Engineers Ground Improvement, 170(4), 193207.
2    Support vector machine
2.1    Site Characterization

[65]    Odeh, I. O. A., Chittleborough, D. J., and McBratney, A. B. (1992). Soil pattern recognition with fuzzy-c-means: application to classification and soil-landform interrelationships. Soil Science Society of America Journal, 56(2), 505-516.
[66]    Bhattacharya, B., and Solomatine, D. P. (2006). Machine learning in soil classification. Neural Networks, 19(2), 186-195.
[67]    Sitharam, T. G., Samui, P., and Anbazhagan, P. (2008). Spatial variability of rock depth in Bangalore using geostatistical, neural network and support vector machine models. Geotechnical and Geological Engineering, 26(5), 503-517.
[68]    Yu, L., Porwal, A., Holden, E. J., and Dentith, M. C. (2012). Towards automatic lithological classification from remote sensing data using support vector machines. Computers and Geosciences, 45, 229-239.
2.2    Geomaterial Properties and Behavior Modeling 
[69]    Feng, X. T., Zhao, H., and Li, S. (2004). A new displacement back analysis to identify mechanical geomaterial parameters based on hybrid intelligent methodology. International Journal for Numerical and Analytical Methods in Geomechanics, 28(11), 1141-1165.
[70]    Zhao, H. B., and Yin, S. (2009). Geomechanical parameters identification by particle swarm optimization and support vector machine. Applied Mathematical Modelling, 33(10), 3997-4012.
[71]    Tinoco, J., Correia, A. G., and Cortez, P. (2011). A data mining approach for predicting jet grouting geomechanical parameters. In Road Materials and New Innovations in Pavement Engineering (pp. 97-104).
[72]    Ceryan, N., Okkan, U., Samui, P., and Ceryan, S. (2013). Modeling of tensile strength of rocks materials based on support vector machines approaches. International Journal for Numerical and Analytical Methods in Geomechanics, 37(16), 2655-2670.
[73]    Ceryan, N. (2014). Application of support vector machines and Bayesian neural network machines in predicting uniaxial compressive strength of volcanic rocks. Journal of African Earth Sciences, 100, 634-644.
2.3    Pile Capacity
[74]    Samui, P. (2008). Prediction of friction capacity of driven piles in clay using the support vector machine. Canadian Geotechnical Journal, 45(2), 288-295.
[75]    Pal, M., and Deswal, S. (2008). Modeling pile capacity using support vector machines and generalized regression neural network. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 134(7), 1021-1024.
[76]    Samui, P. (2011). Prediction of pile bearing capacity using support vector machine. International Journal of Geotechnical Engineering, 5(1), 95-102.
[77]    Tinoco, J., Correia, A. G., and Cortez, P. (2014). Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns. Computers and Geotechnics, 55, 132-140.
[78]    Kordjazi, A., Nejad, F. P., and Jaksa, M. B. (2014). Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data. Computers and Geotechnics, 55, 91-102.
[79]    Kordjazi, A., Pooya Nejad, F. and Jaksa, M. B. (2014). Prediction of Ultimate Bearing Capacity of Axially Loaded Piles Using a Support Vector Machine Based on CPT Data. Computers and Geotechnics, 55, (1), 91102.
[80]    Kordjazi, A., Pooya Nejad, F. and Jaksa, M. B. (2015).  Prediction of Load-carrying Capacity of Piles Using a Support Vector Machine and Improved Data Collection. Proc. 12th Australia New Zealand Conference on Geomechanics, ANZ 2015, Wellington, February 2225, 8 pp.
2.4    Settlement of Foundations
[81]    Samui, P., and Sitharam, T. G. (2008). Leastsquare support vector machine applied to settlement of shallow foundations on cohesionless soils. International Journal for Numerical and Analytical Methods in Geomechanics, 32(17), 2033-2043.
[82]    Samui, P. (2008). Support vector machine applied to settlement of shallow foundations on cohesionless soils. Computers and Geotechnics, 35(3), 419-427.
2.5    Liquefaction
[83]    Pal, M. (2006). Support vector machinesbased modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics, 30(10), 983-996.
[84]    Pal, M. (2006). Support vector machinesbased modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics, 30(10), 983-996.
[85]    Goh, A. T., and Goh, S. H. (2007). Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data. Computers and Geotechnics, 34(5), 410-421.
[86]    Samui, P., and Karthikeyan, J. (2013). Determination of liquefaction susceptibility of soil: a least square support vector machine approach. International Journal for Numerical and Analytical Methods in Geomechanics, 37(9), 1154-1161.
2.6    Soil Retaining Walls and Dams
[87]    Zheng, D., Cheng, L., Bao, T., and Lv, B. (2013). Integrated parameter inversion analysis method of a CFRD based on multi-output support vector machines and the clonal selection algorithm. Computers and Geotechnics, 47, 68-77.
[88]    Ji, Z., Wang, B., Deng, S., and You, Z. (2014). Predicting dynamic deformation of retaining structure by LSSVR-based time series method. Neurocomputing, 137, 165-172.
[89]    Rankovi, V., Grujovi, N., Divac, D., and Milivojevi, N. (2014). Development of support vector regression identification model for prediction of dam structural behaviour. Structural Safety, 48, 33-39.
[90]    Fisher, W. D., Camp, T. K., and Krzhizhanovskaya, V. V. (2016). Crack detection in earth dam and levee passive seismic data using support vector machines. Procedia Computer Science, 80, 577-586.
2.7    Slope Stability
[91]    Feng, X. T., Zhao, H., and Li, S. (2004). Modeling non-linear displacement time series of geo-materials using evolutionary support vector machines. International Journal of Rock Mechanics and Mining Sciences, 41(7), 1087-1107.
[92]    Samui, P. (2008). Slope stability analysis: a support vector machine approach. Environmental Geology, 56(2), 255.
[93]    Zhao, H. B. (2008). Slope reliability analysis using a support vector machine. Computers and Geotechnics, 35(3), 459-467.
[94]    Tan, X. H., Bi, W. H., Hou, X. L., and Wang, W. (2011). Reliability analysis using radial basis function networks and support vector machines. Computers and Geotechnics, 38(2), 178-186.
[95]    Li, S., Zhao, H. B., and Ru, Z. (2013). Slope reliability analysis by updated support vector machine and Monte Carlo simulation. Natural Hazards, 65(1), 707-722.
[96]    Samui, P., Lansivaara, T., and Bhatt, M. R. (2013). Least square support vector machine applied to slope reliability analysis. Geotechnical and Geological Engineering, 31(4), 1329-1334.
[97]    Li, B., Li, D., Zhang, Z., Yang, S., and Wang, F. (2015). Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine. Applied Mathematical Modelling, 39(17), 5253-5264.
[98]    Kang, F., and Li, J. (2015). Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes. Journal of Computing in Civil Engineering, 30(3), 04015040.
[99]    Kang, F., Li, J. S., and Li, J. J. (2016). System reliability analysis of slopes using least squares support vector machines with particle swarm optimization. Neurocomputing, 209, 46-56.
[100]    Kang, F., Xu, Q., and Li, J. J. (2016). Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Applied Mathematical Modelling, 40(11), 6105-6120.
[101]    Li, S., Zhao, H., Ru, Z., and Sun, Q. (2016). Probabilistic back analysis based on Bayesian and multi-output support vector machine for a high cut rock slope. Engineering Geology, 203, 178-190.
2.8    Tunnels and Underground Openings
[102]    Yao, B. Z., Yang, C. Y., Yao, J. B., and Sun, J. (2010). Tunnel surrounding rock displacement prediction using support vector machine. International Journal of Computational Intelligence Systems, 3(6), 843-852.
[103]    Jiang, A. N., Wang, S. Y., and Tang, S. L. (2011). Feedback analysis of tunnel construction using a hybrid arithmetic based on support vector machine and particle swarm optimization. Automation in Construction, 20(4), 482-489.
[104]    Zhou, J., Li, X., and Shi, X. (2012). Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Safety Science, 50(4), 629-644.
[105]    Mahdevari, S., Haghighat, H. S., and Torabi, S. R. (2013). A dynamically approach based on SVM algorithm for prediction of tunnel convergence during excavation. Tunnelling and Underground Space Technology, 38, 59-68.
[106]    Mahdevari, S., Shahriar, K., Yagiz, S., and Shirazi, M. A. (2014). A support vector regression model for predicting tunnel boring machine penetration rates. International Journal of Rock Mechanics and Mining Sciences, 72, 214-229.
[107]    Li, X., Li, X., and Su, Y. (2016). A hybrid approach combining uniform design and support vector machine to probabilistic tunnel stability assessment. Structural Safety, 61, 22-42.
2.9    Landslide
[108]    Feng, X. T., Hudson, J. A., Li, S., Zhao, H., Gao, W., and Zhang, Y. (2004). Integrated intelligent methodology for large-scale landslide prevention design. International Journal of Rock Mechanics and Mining Sciences, 41, 750-755.
[109]    Yao, X., Tham, L. G., and Dai, F. C. (2008). Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology, 101(4), 572-582.
[110]    Kavzoglu, T., and Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352-359.
[111]    Marjanovic, M., Bajat, B., and Kovacevic, M. (2009). Landslide susceptibility assessment with machine learning algorithms. In Intelligent Networking and Collaborative Systems, 2009. INCOS'09. International Conference on (pp. 273-278). IEEE.
[112]    Marjanovi, M., Kovaevi, M., Bajat, B., and Voenílek, V. (2011). Landslide susceptibility assessment using SVM machine learning algorithm. Engineering Geology, 123(3), 225-234.
[113]    Xu, C., Dai, F., Xu, X., and Lee, Y. H. (2012). GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145, 70-80.
[114]    Bui, D. T., Pradhan, B., Lofman, O., and Revhaug, I. (2012). Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models. Mathematical Problems in Engineering, 2012.
[115]    Ballabio, C., and Sterlacchini, S. (2012). Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Mathematical Geosciences, 44(1), 47-70.
[116]    Pourghasemi, H. R., Jirandeh, A. G., Pradhan, B., Xu, C., and Gokceoglu, C. (2013). Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science, 122(2), 349-369.
[117]    Li, X. Z., and Kong, J. M. (2014). Application of GASVM method with parameter optimization for landslide development prediction. Natural Hazards and Earth System Sciences, 14(3), 525-533.
[118]    Bui, D. T., Tuan, T. A., Klempe, H., Pradhan, B., and Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2), 361-378.
[119]    Hong, H., Pradhan, B., Jebur, M. N., Bui, D. T., Xu, C., and Akgun, A. (2016). Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environmental Earth Sciences, 75(1), 40.
3    Clustering
3.1    Site Characterization

[120]    Judd, A. G. (1980). The use of cluster analysis in the derivation of geotechnical classifications. Bulletin of the Association of Engineering Geologists, 17(4), 193-211.
[121]    Hegazy, Y. A. (1998). Delineating Geostratigraphy by Cluster Analysis of Piezocone Data. Ph. D Thesis, Georgia Institute of Technology, 464 pp.
[122]    Hegazy, Y. A., and Mayne, P. W. (2002). Objective site characterization using clustering of piezocone data. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 128(12), 986-996.
[123]    Facciorusso, J., and Uzielli, M. (2004). Stratigraphic profiling by cluster analysis and fuzzy soil classification from mechanical cone penetration tests. In Geotechnical and Geophysical Site Characterization (Proceedings ISC-2, Portugal), pp. 905-912, (Millpress: Rotterdam).
[124]    Facciorusso, J., and Uzielli, M. (2004). Stratigraphic profiling by cluster analysis and fuzzy soil classification from mechanical cone penetration tests. Proc. of ISC-2 on Geotechnical and Geophysical Site Characterization, Porto, Millpress, Rotterdam, 905-912.
[125]    Das, S. K., and Basudhar, P. K. (2009). Utilization of self-organizing map and fuzzy clustering for site characterization using piezocone data. Computers and Geotechnics, 36(1-2), 241-248.
[126]    Walvoort, D. J. J., Brus, D. J., and De Gruijter, J. J. (2010). An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means. Computers and Geosciences, 36(10), 1261-1267.
[127]    Ferentinou, M. D., Hasiotis, T., and Sakellariou, M. G. (2010). Clustering of Geotechnical Properties of Marine Sediments Through SelfOrganizing Maps: An Example from the Zakynthos CanyonValley System, Greece. In Submarine Mass Movements and Their Consequences (pp. 43-54). Springer, Dordrecht.
[128]    Bashari, A., Beiki, M., and Talebinejad, A. (2011). Estimation of deformation modulus of rock masses by using fuzzy clustering-based modeling. International Journal of Rock Mechanics and Mining Sciences, 48(8), 1224-1234.
[129]    Ferentinou, M., Hasiotis, T., and Sakellariou, M. (2012). Application of computational intelligence tools for the analysis of marine geotechnical properties in the head of Zakynthos canyon, Greece. Computers and Geosciences, 40, 166-174.
[130]    Riquelme, A. J., Abellán, A., Tomás, R., and Jaboyedoff, M. (2014). A new approach for semi-automatic rock mass joints recognition from 3D point clouds. Computers and Geosciences, 68, 38-52.
[131]    Masoud, A. A. (2016). Geotechnical site suitability mapping for urban land management in Tanta District, Egypt. Arabian Journal of Geosciences, 9(5), 340.
3.2    Liquefaction
[132]    Garcia, S. R., Romo, M. P., and Botero, E. (2008). A neurofuzzy system to analyze liquefaction-induced lateral spread. Soil Dynamics and Earthquake Engineering, 28(3), 169-180.
3.3    Slope Stability
[133]    Tang, X. S., Li, D. Q., Chen, Y. F., Zhou, C. B., and Zhang, L. M. (2012). Improved knowledge-based clustered partitioning approach and its application to slope reliability analysis. Computers and Geotechnics, 45, 34-43.
3.4    Lifeline Engineering
[134]    Toprak, S., Nacaroglu, E., Cetin, O. A., and Koc, A. C. (2009). Pipeline damage assessment using cluster analysis. In TCLEE 2009: Lifeline Earthquake Engineering in a Multihazard Environment (pp. 1-8).
[135]    Jayaram, N., and Baker, J. W. (2010). Efficient sampling and data reduction techniques for probabilistic seismic lifeline risk assessment. Earthquake Engineering and Structural Dynamics, 39(10), 1109-1131.
[136]    Sun, J., Wang, R., Wang, X., Yang, H., and Ping, J. (2014). Spatial cluster analysis of bursting pipes in water supply networks. Procedia Engineering, 70, 1610-1618.
[137]    Lim, H. W., Song, J., and Kurtz, N. (2015). Seismic reliability assessment of lifeline networks using clusteringbased multiscale approach. Earthquake Engineering and Structural Dynamics, 44(3), 355-369.
3.5    Landslide
[138]    Gorsevski, P. V., Gessler, P. E., and Jankowski, P. (2003). Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard. Journal of Geographical Systems, 5(3), 223-251.
[139]    Alimohammadlou, Y., Najafi, A., and Gokceoglu, C. (2014). Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: a case study in Saeen Slope, Azerbaijan province, Iran. Catena, 120, 149-162.
[140]    Melchiorre, C., Matteucci, M., Azzoni, A., and Zanchi, A. (2008). Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology, 94(3-4), 379-400.
[141]    Ding, M., and Hu, K. (2014). Susceptibility mapping of landslides in Beichuan County using cluster and MLC methods. Natural Hazards, 70(1), 755-766.
4    Feature learning (Dimensionality reduction)
4.1    Soil Retaining Walls

[142]    Hashash, Y. M., Levasseur, S., Osouli, A., Finno, R., and Malecot, Y. (2010). Comparison of two inverse analysis techniques for learning deep excavation response. Computers and Geotechnics, 37(3), 323-333.
4.2    Slope Stability
[143]    He, H., Li, S., Sun, H., and Yang, T. (2011). Environmental factors of road slope stability in mountain area using principal component analysis and hierarchy cluster. Environmental Earth Sciences, 62(1), 55-59.
[144]    Crosta, G. B., Frattini, P., and Agliardi, F. (2013). Deep seated gravitational slope deformations in the European Alps. Tectonophysics, 605, 13-33.
4.3    Tunnels and Underground Openings
[145]    Yun, H. B., Park, S. H., Mehdawi, N., Mokhtari, S., Chopra, M., Reddi, L. N., and Park, K. T. (2014). Monitoring for close proximity tunneling effects on an existing tunnel using principal component analysis technique with limited sensor data. Tunnelling and Underground Space Technology, 43, 398-412.
4.4    Landslide 
[146]    Micheletti, N., Foresti, L., Robert, S., Leuenberger, M., Pedrazzini, A., Jaboyedoff, M., and Kanevski, M. (2014). Machine learning feature selection methods for landslide susceptibility mapping. Mathematical Geosciences, 46(1), 33-57.
4.5    Offshore Engineering
[147]    Spaulding, M. L., Grilli, A., Damon, C., and Fugate, G. (2010). Application of technology development index and principal component analysis and cluster methods to ocean renewable energy facility siting. Marine Technology Society Journal, 44(1), 8-23.
4.6    Others
[148]    Blatman, G., and Sudret, B. (2008). Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach. Comptes Rendus Mécanique, 336(6), 518-523.
[149]    Blatman, G., and Sudret, B. (2011). Adaptive sparse polynomial chaos expansion based on least angle regression. Journal of Computational Physics, 230(6), 2345-2367.
[150]    Blatman, G., and Sudret, B. (2010). An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis. Probabilistic Engineering Mechanics, 25(2), 183-197.
5    Outlier detection
5.1    Site Characterization

[151]    Halim, I. S., and Tang, W. H. (1993). Site exploration strategy for geologic anomaly characterization. Journal of geotechnical engineering, 119(2), 195-213.
[152]    Kim, H. S., Chung, C. K., and Kim, H. K. (2016). Geo-spatial data integration for subsurface stratification of dam site with outlier analyses. Environmental Earth Sciences, 75(2), 168.
5.2    Others
[153]    Yuen, K.V., and Ortiz, G.A. (2017). Outlier detection and robust regression for correlated data. Computer Methods in Applied Mechanics and Engineering, 313, 632-646.
6    Bayesian machine learning
6.1    Site Characterization

[154]    Tang, W. H. (1971). A Bayesian evaluation of information for foundation engineering design. In Proc., 1st International Conf. on Applications of Statistics and Probability to Soil and Structural Engineering, Hong Kong.
[155]    Tang, W. H. (1987). Updating anomaly statistics - single anomaly case. Structural Safety, 4(2), 151-163.
[156]    Nobre, M. M. and Sykes, J. F. (1992). Application of Bayesian Kriging to subsurface characterization. Canadian Geotechnical Journal, 29(4): 589-598.
[157]    Honjo, Y., and Kudo, N. (1998). Matching objective and subjective information in geotechnical inverse analysis based on entropy minimization. Proc. International Symp. on Inverse Problems in Engineering Mechanics, Nagano, Japan, 263--271.
[158]    McGrath, T. and Gilbert, R. (1999). Analytical method for designing and analyzing 1D search programs. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 125(12), 1043-1056.
[159]    Ditlevsen, O., Tarp-Johansen, N. J. and Denver, H. (2000). Bayesian soil assessments combining prior with posterior censored samples. Computers and Geotechnics, 26(3-4), 187-198.
[160]    Zhang, L. M., Tang, W. H., Zhang, L. L., and Zheng, J. G., (2004). Reducing uncertainty of prediction from empirical correlations. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 130(5), 526-534.
[161]    Jung, B. C., Gardoni, P., Biscontin, A. (2008). Probabilistic soil identification based on cone penetration tests. Geotechnique, 58(7), 591-603.
[162]    Cetin, K. and Ozan, C. (2009). CPT-Based Probabilistic Soil Characterization and Classification. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 135(1), 84-107.
[163]    Ching, J., Phoon, K. K., and Chen, Y. C., (2010). Reducing shear strength uncertainties in clays by multivariate correlations. Canadian Geotechnical Journal, 47 (1), 1633.
[164]    Wang, Y., Au, S. K. and Cao, Z. J. (2010). Bayesian approach for probabilistic characterization of sand friction angles. Engineering Geology, 114 (34), 354363.
[165]    Ching, J., Chen, J. R., Yeh, J. Y., and Phoon, K. K., (2012). Updating uncertainties in friction angles of clean sands. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 138 (2), 217229.
[166]    Houlsby, N. M. T. and Houlsby, G. T. (2013). Statistical fitting of undrained strength data. Geotechnique, 63(14), 1253-1263.
[167]    Uzielli, M. and Mayne, P. W. (2013). Bayesian characterization of transformation uncertainty for strength and stiffness of sands. Foundation Engineering in the Face of Uncertainty: Honoring Fred H. Kulhawy (GSP 229), ASCE, Reston, VA, 368-384.
[168]    Wang, Y. and Cao, Z. J. (2013) Probabilistic characterization of Youngs modulus of soil using equivalent samples. Engineering Geology, 159, 106118.
[169]    Cao, Z. J. and Wang, Y. (2013) Bayesian approach for probabilistic site characterization using cone penetration tests. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 139 (2), 267276.
[170]    Wang, Y., Huang, K. and Cao, Z. J. (2013) Probabilistic identification of underground soil stratification using cone penetration tests. Canadian Geotechnical Journal, 50 (7), 766776.
[171]    Cao, Z. J. and Wang, Y. (2014) Bayesian model comparison and selection of spatial correlation functions for soil parameters. Structural Safety, 49, 1017.
[172]    Cao, Z. J., Huang, K., and Wang, Y. (2014). Bayesian inverse analysis for geotechnical site characterization using cone penetration test. International Journal of Reliability and Safety, 8(2-4), 97-116.
[173]    Cao, Z. J. and Wang, Y. (2014) Bayesian model comparison and characterization of undrained shear strength. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 140 (6), 04014018, 19.
[174]    Feng, X. and Jimenez, R. (2014). Bayesian prediction of elastic modulus of intact rocks using their uniaxial compressive strength. Engineering Geology, 173(1), 32-40.
[175]    Müller, R., Larsson, S. and Spross, J. (2014). Extended multivariate approach for uncertainty reduction in the assessment of undrained shear strength in clays. Canadian Geotechnical Journal, 51(3), 231-245.
[176]    Wang, Y., Huang, K. and Cao, Z. J. (2014) Bayesian identification of soil strata in London Clay. Geotechnique, 64 (3), 239246.
[177]    Wang, Y., Zhao, T. Y. and Cao, Z. J. (2015) Site-specific probability distribution of geotechnical properties. Computers and Geotechnics, 70, 159168.
[178]    Wang, Y. and Aladejare, A. E. (2015). Selection of site-specific regression model for characterization of uniaxial compressive strength of rock. International Journal of Rock Mechanics and Mining Sciences, 75, 73-81.
[179]    Ching, J., Wu, S. S., and Phoon, K. K. (2015). Statistical characterization of random field parameters using frequentist and Bayesian approaches. Canadian Geotechnical Journal, 53(2), 285-298.
[180]    Cao, Z. J., Wang, Y. and Li, D. Q. (2016). Quantification of prior knowledge in geotechnical site characterization. Engineering Geology, 203, 107116.
[181]    Cao, Z. J., Wang, Y., and Li, D. Q. (2016). Site-specific characterization of soil properties using multiple measurements from different test procedures at different locationsA Bayesian sequential updating approach. Engineering Geology, 211, 150-161.
[182]    Ching, J., and Wang, J. S. (2016). Application of the transitional Markov chain Monte Carlo to probabilistic site characterization. Engineering Geology, 203, 151167.
[183]    Wang, Y. and Aladejare, A. E. (2016). Bayesian characterization of correlation between uniaxial compressive strength and Youngs modulus of rock. International Journal of Rock Mechanics and Mining Sciences, 85, 1019.
[184]    Wang, Y. and Akeju, O. V. (2016). Quantifying the cross-correlation between effective cohesion and friction angle of soil from limited site-specific data. Soils and Foundations, 56(6), 10571072.
[185]    Wang, Y., Akeju, O. V., and Cao, Z. J. (2016). Bayesian Equivalent Sample Toolkit (BEST): an Excel VBA program for probabilistic characterisation of geotechnical properties from limited observation data. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 10(4), 251-268.
[186]    Wang, Y., Cao, Z. J. and Li, D. Q. (2016). Bayesian perspective on geotechnical variability and site characterization. Engineering Geology, 203, 117125.
[187]    Wang, Y. and Zhao, T. (2016). Interpretation of soil property profile from limited measurement data: a compressive sampling perspective. Canadian Geotechnical Journal, 53(9), 1547-1559.
[188]    Wang, Y. and Aladejare, A. E. (2016). Evaluating variability and uncertainty of Geological Strength Index at a specific site. Rock Mechanics and Rock Engineering, 49(9), 35593573.
[189]    Tian, M., Li, D. Q., Cao, Z. J., Phoon, K. K., and Wang, Y. (2016). Bayesian identification of random field model using indirect test data. Engineering Geology, 210, 197-211.
[190]    Wang, Y. and Aladejare, A. E. (2016). Evaluating variability and uncertainty of Geological Strength Index at a specific site. Rock Mechanics and Rock Engineering, 49(9), 35593573.
[191]    Akeju, O. V., Senetakis, K., and Wang, Y. (2017). Bayesian parameter identification and model selection for normalized modulus reduction curves of soils. Journal of Earthquake Engineering, 1-29.
[192]    Aladejare, A. E., and Wang, Y. (2017). Sources of uncertainty in site characterization and their impact on geotechnical reliability-based design. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 3(4), 04017024.
[193]    Ching, J. and Phoon, K. K. (2017). Characterizing uncertain site-specific trend function by sparse Bayesian learning, ASCE Journal of Engineering Mechanics, 143(7), 04017028.
[194]    Wang, Y., Akeju, O. V., and Zhao, T. (2017). Interpolation of spatially varying but sparsely measured geo-data: A comparative study. Engineering Geology, 231, 200-217.
[195]    Wang, Y., Arroyo, M., Cao, Z. J., Ching, J., Länsivaara, T., Orr, T., and Simpson, B. (2017). Selection of characteristic values for rock and soil properties using Bayesian statistics and prior knowledge. Joint TC205/TC304 Working Group on Discussion of statistical/reliability methods for Eurocodes, ISSMGE.
[196]    Wang, Y. and Zhao, T. (2017). Statistical interpretation of soil property profiles from sparse data using Bayesian Compressive Sampling. Geotechnique, 67(6), 523-536.
[197]    Wang, Y. and Zhao, T. (2017). Bayesian assessment of site-specific performance of geotechnical design charts with unknown model uncertainty. International Journal for Numerical and Analytical Methods in Geomechanics, 41(5), 781-800.
[198]    Aladejare, A. E. and Wang, Y. (2017). Evaluation of rock property variability. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 11(1), 22-41.
[199]    Zhao, T., Montoya-Noguera, S., Phoon, K. K., and Wang, Y. (2018). Interpolating spatially varying soil property values from sparse data for facilitating characteristic value selection. Canadian Geotechnical Journal, 55(2), 171-181.
[200]    Huang, J., Zheng, D., Li, D., Kelly, R., and Sloan, S. W. (2017). Probabilistic characterization of 2D soil profile by integrating CPT with MASW data. Canadian Geotechnical Journal, 10.1139/cgj-2017-0429. 
[201]    Ching, J., Phoon, K. K., Beck, J. L., and Huang, Y., (2018). Identifiability of Geotechnical Site-Specific Trend Functions, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2017, 3 (4): 04017021.
[202]    Ching, J., Wu, T., Stuedlein, A., and Bong, T. (2018) Estimating horizontal scale of fluctuation with limited CPT soundings. Geoscience Frontiers, 10.1016/j.gsf.2017.11.008.
[203]    Shen, M. Y., Cao, Z. J., Li, D. Q., and Wang, Y. (2018). Probabilistic characterization of site-specific inherent variability of undrained shear strength using both indirect and direct measurements. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 4(1), 04017038.
[204]    Wang, L., Cao, Z. J., Li, D. Q., Phoon, K. K., and Au, S. K. (2018). Determination of site-specific soil-water characteristic curve from a limited number of test data-A Bayesian perspective, Geoscience Frontiers, 10.1016/j.gsf.2017.10.014. 
[205]    Zhang, L., Li, D. Q., Tang, X. S., Cao, Z. J., and Phoon, K. K. (2018). Bayesian model comparison and characterization of bivariate distribution for shear strength parameters of soil. Computers and Geotechnics, 95, 110-118.
[206]    Wang, Y., Zhao, T., and Phoon, K. K. (2018). Direct simulation of random field samples from sparsely measured geotechnical data with consideration of uncertainty in interpretation. Canadian Geotechnical Journal, https://doi.org/10.1139/cgj-2017-0254.
6.2    Pile Capacity
[207]    Baecher, G. R. and Rackwitz, R. (1982). Factors of safety and pile load tests. International Journal for Numerical and Analytical Methods in Geomechanics, 6(4), 609-624. 
[208]    Najjar, S. and Gilbert, R. (2009). Importance of Lower-Bound Capacities in the Design of Deep Foundations. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 135(7), 890-900.
[209]    Park, J. H., Kim, D. and Chung, C. K. (2012). Implementation of Bayesian theory on LRFD of axially loaded driven piles. Computers and Geotechnics, 42, 73-80.
[210]    Papaioannou, I. and Straub, D. (2012). Reliability updating in geotechnical engineering including spatial variability of soil. Computers and Geotechnics, 42, 44-51.
6.3    Liquefaction
[211]    Juang, C. H., Chen, C. J., Rosowsky, D. V. and Tang, W. H. (2000). CPT-based liquefaction analysis. Part 2. Reliability for design. Geotechnique, 50(5), 593-599.
[212]    Huang, H. W., Zhang, J., and Zhang, L. M. (2012). Bayesian network for characterizing model uncertainty of liquefaction potential evaluation models. KSCE Journal of Civil Engineering, 16(5), 714-722.
[213]    Wang, Y., Fu, C., and Huang, K. (2017). Probabilistic assessment of liquefiable soil thickness considering spatial variability and model and parameter uncertainties. Geotechnique, 67(3), 228-241.
6.4    Embankments
[214]    Honjo, Y., Liu, W. T., and Soumitra, G. (1994). Inverse analysis of an embankment on soft clay by extended Bayesian method. International Journal of Numerical and Analytical Methods in Geomechanics, 18, 709-734.
[215]    Wu, T. H., Zhou, S. Z. and Gale, S. M. (2007). Embankment on sludge: predicted and observed performances. Canadian Geotechnical Journal, 44, 545-563.
[216]    Schweckendiek, T., Vrouwenvelder, A.C.W.M. and Calle, E.O.F. (2014). Updating piping reliability with field performance observations. Structural Safety, 47, 13-23 
6.5    Slope Stability
[217]    Luckman, P. G., Der Kiureghian, A., and Sitar, N. (1987). Use of stochastic stability analysis for Bayesian back calculation of pore pressures acting in a cut slope at failure. Proc., in 5th International Conf. on Application of Statistics and Probability in Soil and Struct. Engr., Vancouver, Canada.
[218]    Reddi, L. N., and Wu, T. H. (1991). Probabilistic analysis of ground-water levels in hillside slopes. ASCE Journal of Geotechnical Engineering, 117(6), 872-890.
[219]    Cheung, R. W. M. and Tang, W. H. (2005). Realistic assessment of slope reliability for effective landslide hazard management. Geotechnique, 55(1), 85 94.
[220]    Zhang, J., Zhang, L. M., and Tang, W. H. (2009). Bayesian framework for characterizing geotechnical model uncertainty. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 135(7), 932--940.
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[223]    Zhang, L. L., Zhang, J., Zhang, L. M., and Tang, W. H. (2010). Back analysis of slope failure with Markov chain Monte Carlo simulation. Computers and Geotechnics, 37(7-8), 905-912
[224]    Wang, Y., Cao, Z. J., and Au, S. K. (2010). Efficient Monte Carlo Simulation of parameter sensitivity in probabilistic slope stability analysis. Computers and Geotechnics, 37(7-8), 1015-1022.
[225]    Samui, P., Lansivaara, T., and Kim, D. (2011). Utilization relevance vector machine for slope reliability analysis. Applied Soft Computing, 11(5), 4036-4040.
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[228]    Hasan, S. and Najjar, S. (2013). Probabilistic back analysis of failed slopes using Bayesian techniques. Geo-Congress 2013: Stability and Performance of Slopes and Embankments III (GSP 231), ASCE, Reston, VA, 1013-1022.
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[230]    Ranalli, M., Medina-Cetina, Z., Gottardi, G. and Nadim, F. (2014). Probabilistic calibration of a dynamic model for predicting rainfall-controlled landslides. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 140(4), 04013039.
[231]    Zhang, J., Huang, H. W., Juang, C. H., and Su, W. W. (2014). Geotechnical reliability analysis with limited data: consideration of model selection uncertainty. Engineering Geology, 181, 27-37.
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6.6    Tunnels and Underground Openings
[234]    Ledesma, A., Gens, A., and Alonso, E. E. (1996). Parameter and variance estimation in geotechnical back analysis using prior information. International Journal of Numerical and Analytical Methods in Geomechanics, 20, 114-141.
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