Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods
Tài liệu tham khảo
American Society for Testing and Materials, 1996
Aqil, 2007, A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff, Journal of Hydrology, 337, 22, 10.1016/j.jhydrol.2007.01.013
Basma, 2003, Modeling time dependent swell of clays using sequential artificial neural networks, Environmental and Engineering Geoscience, 9, 279, 10.2113/9.3.279
Baykasoglu, 2008, Prediction of compressive and tensile strength of limestone via genetic programming, Expert Systems with Applications, 35, 111, 10.1016/j.eswa.2007.06.006
Canakci, 2009, Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming, Neural Computing & Applications, 18, 1031, 10.1007/s00521-008-0208-0
Chang, 2006, Application of back-propagation networks in debris flow prediction, Engineering Geology, 85, 270, 10.1016/j.enggeo.2006.02.007
Daliakopoulos, 2005, Groundwater level forecasting using artificial neural networks, Journal of Hydrology, 309, 229, 10.1016/j.jhydrol.2004.12.001
Das, 2008, Prediction of residual friction angle of clays using artificial neural network, Engineering Geology, 100, 142, 10.1016/j.enggeo.2008.03.001
Gomez, 2008, Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela, Engineering Geology, 78, 11, 10.1016/j.enggeo.2004.10.004
Güllü, 2007, A neural network approach for attenuation relationships: an application using strong ground motion data from Turkey, Engineering Geology, 93, 65, 10.1016/j.enggeo.2007.05.004
Güllü, 2008, Reply to discussion by H. Sönmez and C. Gökceoglu on “A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey” by H. Güllü and E. Erçelebi, Eng. Geol. 93 (2007) 65–81, Engineering Geology, 97, 94, 10.1016/j.enggeo.2007.08.008
Hatanaka, 1996, Empirical correlation between penetration resistance and effective friction of sandy soil, Soils and Foundations (Japanese Geotechnical Society), 36, 1, 10.3208/sandf.36.4_1
Jaksa, 2008, Future challenges for artificial neural network modeling in geotechnical engineering, 1710
Kanungo, 2006, A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas, Engineering Geology, 85, 347, 10.1016/j.enggeo.2006.03.004
Kayadelen, 2009, Modeling of the angle of shearing resistance of soils using soft computing systems, Expert Systems with Applications, 36, 11814, 10.1016/j.eswa.2009.04.008
Maji, 2008, Prediction of elastic modulus of jointed rock mass using artificial neural networks, Geotechnical and Geological Engineering, 26, 443, 10.1007/s10706-008-9180-9
Malinova, 2004, Artificial neural network modeling of hydrogen storage properties of Mg-based Alloys, Materials Science and Engineering A, 365, 219, 10.1016/j.msea.2003.09.031
Mason, 1996, A neural network model of rainfall-runoff using radial basis functions, Journal of Hydraulic Research, 34, 537, 10.1080/00221689609498476
Moosavi, 2006, Modeling the cyclic swelling pressure of mudrock using artificial neural networks, Engineering Geology, 87, 178, 10.1016/j.enggeo.2006.07.001
Nefeslioglu, 2008, An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps, Engineering Geology, 97, 171, 10.1016/j.enggeo.2008.01.004
Shahin, 2001, Artificial neural network applications in geotechnical engineering, Australian Geomechanics, 36, 49
Sharma, 2009, A correlation between Schmidt hammer rebound numbers with impact strength index, slake durability index and P-wave velocity, International Journal of Earth Sciences (Geol Rundsch), 100, 189, 10.1007/s00531-009-0506-5
Smith, 1986
Tabari, 2009, Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran, Neural Computing & Applications
Tabari, 2011, Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region, Meteorology and Atmospheric Physics, 110, 135, 10.1007/s00703-010-0110-z
Tiryaki, 2008, Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees, Engineering Geology, 99, 51, 10.1016/j.enggeo.2008.02.003
Valverde Ramirez, 2005, Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region, Journal of Hydrology, 301, 146, 10.1016/j.jhydrol.2004.06.028