dc.identifier.citation |
Juang, C. H., Jiang, T., & Andrus, R. D. (2002). Assessing probability-based methods for liquefaction potential evaluation. Journal of Geotechnical and Geoenvironmental Engineering, 128(7), 580–589. Samui, P., & Sitharam, T. G. (2011). Machine learning modelling for predicting soil liquefaction susceptibility. Natural Hazards and Earth System Science, 11(1), 1–9. https://doi.org/10.5194/nhess-11-1-2011 Robertson, P. K., & Wride, C. E. (1998). Evaluating cyclic liquefaction potential using the cone penetration test. Canadian Geotechnical Journal, 35(3), 442–459. Ahmed, A. A., & Pradhan, B. (2019). Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system. Environmental Monitoring and Assessment, 191(3), 1–17. Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press. Vapnik, V. (1998). Statistical learning theory wiley new york google scholar. Iwasaki, T. (1978). A practical method for assessing soil liquefaction potential based on case studies at various sites in Japan. Proc. Second Int. Conf. Microzonation Safer Construction Research Application, 1978, 2, 885–896. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 144–152. Idriss, I. M., & Boulanger, R. W. (2006). Semi-empirical procedures for evaluating liquefaction potential during earthquakes. Soil Dynamics and Earthquake Engineering, 26(2–4), 115–130. Seed, H. B., & De Alba, P. (1986). Use of SPT and CPT tests for evaluating the liquefaction resistance of sands. Use of in Situ Tests in Geotechnical Engineering, 281–302. Samui, P., & Sitharam, T. G. (2011). Machine learning modelling for predicting soil liquefaction susceptibility. Natural Hazards and Earth System Science, 11(1), 1–9. https://doi.org/10.5194/nhess-11-1-2011 Robertson, P. K., Woeller, D. J., & Finn, W. D. L. (1992). https://doi.org/10.1061/(ASCE)GT.1943-5606.0000631. Canadian Geotechnical Journal, 29(4), 686–695. Keefer, D. K. (1984). Landslides caused by earthquakes. Geological Society of America Bulletin, 95(4), 406–421. P a g e | 46 Shahin, M. A., Jaksa, M. B., & Maier, H. R. (2000). Predicting the settlement of shallow foundations on cohesionless soils using back-propagation neural networks. Department of Civil and Environmental Engineering, University of Adelaide …. Cetin, K. O., Seed, R. B., Kayen, R. E., Moss, R. E. S., Bilge, H. T., Ilgac, M., & Chowdhury, K. (2018). Examination of differences between three SPT-based seismic soil liquefaction triggering relationships. Soil Dynamics and Earthquake Engineering, 113(July 2017), 75–86. https://doi.org/10.1016/j.soildyn.2018.03.013 Andersen, C. M., & Bro, R. (2010). Variable selection in regression—a tutorial. Journal of Chemometrics, 24(11‐12), 728–737. Seed, H. B. (1982). Ground motions and soil liquefaction during earthquakes. Earthquake Engineering Research Insititue. Vapnik, V. (1999). The nature of statistical learning theory. Springer science & business media. Zhou, J., Huang, S., Wang, M., & Qiu, Y. (2021). Performance evaluation of hybrid GA– SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation. Engineering with Computers, 0123456789. https://doi.org/10.1007/s00366-021-01418-3 Idriss, I. M., & Boulanger, R. W. (2010). SPT-based liquefaction triggering procedures. Rep. UCD/CGM-10, 2, 4–13. Haque, D. M. E., Khan, N. W., Selim, M., Kamal, A. S. M. M., & Chowdhury, S. H. (2020). Towards Improved Probabilistic Seismic Hazard Assessment for Bangladesh. Pure and Applied Geophysics, 177(7), 3089–3118. https://doi.org/10.1007/s00024-019-02393-z Seed, H. B., & Idriss, I. M. (1967). Analysis of soil liquefaction: Niigata earthquake. Journal of the Soil Mechanics and Foundations Division, 93(3), 83–108. Lenz, J. A., & Baise, L. G. (2007). Spatial variability of liquefaction potential in regional mapping using CPT and SPT data. Soil Dynamics and Earthquake Engineering, 27(7), 690–702. https://doi.org/10.1016/j.soildyn.2006.11.005 Pham, T. A. (2021). Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering. Computational Intelligence and Neuroscience, 2021. https://doi.org/10.1155/2021/1058825 Zhou, Z., Zhang, R., Wang, Y., Zhu, Z., & Zhang, J. (2018). Color difference classification based on optimization support vector machine of improved grey wolf algorithm. Optik, 170, 17–29. Robertson, P. K., & Campanella, R. G. (1985). Liquefaction potential of sands using the CPT. Journal of Geotechnical Engineering, 111(3), 384–403. Seed, H. B., & Idriss, I. M. (1971). Simplified procedure for evaluating soil liquefaction potential. Journal of the Soil Mechanics and Foundations Division, 97(9), 1249–1273. P a g e | 47 Hsein Juang, C., Chen, C. J., Jiang, T., & Andrus, R. D. (2000). Risk-based liquefaction potential evaluation using standard penetration tests. Can Sci Publ J, 37. Park, S. H., Goo, J. M., & Jo, C. (n.d.). Receiver Operating Characteristic (ROC) Curve: Practical Guide for Radiologists. Korean J Radiol, 5(1). Chadha, J., Jain, A., & Kumar, Y. (2022). Artificial intelligence techniques in wireless sensor networks for accurate localization of user in floor, building and indoor area. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-12979-w Tang, Y., Zhang, Y.-Q., Huang, Z., & Hu, X. (2005). Granular SVM-RFE gene selection algorithm for reliable prostate cancer classification on microarray expression data. Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE’05), 290–293. Fear, C. E., & McRoberts, E. C. (1993). Report on liquefaction potential and catalogue of case records. Geotechnical Engineering Library, Department of Civil Engineering …. Kurup, P. U., & Dudani, N. K. (2002). Neural networks for profiling stress history of clays from PCPT data. Journal of Geotechnical and Geoenvironmental Engineering, 128(7), 569–579. J., C. C., & Hsein, J. C. (2022). Calibration of SPT- and CPT-Based Liquefaction Evaluation Methods. In Innovations and Applications in Geotechnical Site Characterization (pp. 49–64). https://doi.org/doi:10.1061/40505(285)4 Youd, T. L., & Idriss, I. M. (2001). Liquefaction Resistance of Soils: Summary Report from the 1996 NCEER and 1998 NCEER/NSF Workshops on Evaluation of Liquefaction Resistance of Soils. Journal of Geotechnical and Geoenvironmental Engineering, 127(4), 297–313. https://doi.org/10.1061/(asce)1090-0241(2001)127:4(297) Fahim, A. K. F., Rahman, M., Hossain, M., & Kamal, A. S. M. (2022). Liquefaction resistance evaluation of soils using artificial neural network for Dhaka City, Bangladesh. Natural Hazards, 1–31. Caudill, M., & Butler, C. (1992). Understanding neural networks; computer explorations. MIT press. Pal, M. (2006). Support vector machines-based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics, 30(10), 983–996. https://doi.org/10.1002/nag.509 Rahman, M. Z., Siddiqua, S., & Kamal, A. S. M. M. (2015). Liquefaction hazard mapping by liquefaction potential index for Dhaka City, Bangladesh. Engineering Geology, 188, 137–147. https://doi.org/10.1016/j.enggeo.2015.01.012 Seed, H. B., Idriss, I. M., & Arango, I. (1983). Evaluation of liquefaction potential using field performance data. Journal of Geotechnical Engineering, 109(3), 458–482. P a g e | 48 Padmini, D., Ilamparuthi, K., & Sudheer, K. P. (2008). Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. Computers and Geotechnics, 35(1), 33–46. Cetin, K. O., Seed, R. B., Der Kiureghian, A., Tokimatsu, K., Harder, L. F., Kayen, R. E., & Moss, R. E. S. (2004). Standard Penetration Test-Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential. Journal of Geotechnical and Geoenvironmental Engineering, 130(12), 1314–1340. https://doi.org/10.1061/(asce)1090-0241(2004)130:12(1314) Juang, C. H., Yuan, H., Lee, D.-H., & Lin, P.-S. (2003). Simplified cone penetration testbased method for evaluating liquefaction resistance of soils. Journal of Geotechnical and Geoenvironmental Engineering, 129(1), 66–80. |
en_US |