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Development of equation for determining the compression index of marine clay.
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- Indian Journal of Marine Sciences, 2019, v. 48, n. 11, p. 1796
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- Article
Operational use of machine learning models for sea-level modeling.
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- Indian Journal of Marine Sciences, 2019, v. 48, n. 9, p. 1427
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- Article
Proposed numerical and machine learning models for fiber-reinforced polymer concrete-steel hollow and solid elliptical columns.
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- Frontiers of Structural & Civil Engineering, 2024, v. 18, n. 8, p. 1169, doi. 10.1007/s11709-024-1083-1
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- Article
Prediction of bearing capacity of pile foundation using deep learning approaches.
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- Frontiers of Structural & Civil Engineering, 2024, v. 18, n. 6, p. 870, doi. 10.1007/s11709-024-1085-z
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- Article
Ensemble unit and AI techniques for prediction of rock strain.
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- Frontiers of Structural & Civil Engineering, 2022, v. 16, n. 7, p. 858, doi. 10.1007/s11709-022-0831-3
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- Article
Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach.
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- Frontiers of Structural & Civil Engineering, 2013, v. 7, n. 2, p. 133, doi. 10.1007/s11709-013-0202-1
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- Article
Liquefaction prediction using support vector machine model based on cone penetration data.
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- Frontiers of Structural & Civil Engineering, 2013, v. 7, n. 1, p. 72, doi. 10.1007/s11709-013-0185-y
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- Article
DETERMINATION OF CONTAMINATATED WELLS TO NO<sub>3</sub>-N: A NOVEL VULNERABILITY ASSESSMENT TOOL.
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- Journal of Urban & Environmental Engineering, 2014, v. 8, n. 2, p. 243, doi. 10.4090/juee.2014.v8n2.243249
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APPLICATION OF RELEVANCE VECTOR MACHINE IN SEISMIC ATTENUATION PREDICTION.
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- Journal of Earthquake & Tsunami, 2007, v. 1, n. 4, p. 299, doi. 10.1142/s1793431107000183
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- Article
Novel Deep Learning Approaches for Mapping Variation of Ground Level from Spirit Level Measurements.
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- Geoscientific Model Development Discussions, 2023, p. 1, doi. 10.5194/gmd-2023-62
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- Article
Relevance vector machines approach for long-term flow prediction.
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- Neural Computing & Applications, 2014, v. 25, n. 6, p. 1393, doi. 10.1007/s00521-014-1626-9
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- Article
Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles.
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- Neural Computing & Applications, 2013, v. 23, n. 3/4, p. 1123, doi. 10.1007/s00521-012-1043-x
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- Article
Examining Efficacy of Metamodels in predicting Ground Water Table.
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- International Journal of Performability Engineering, 2015, v. 11, n. 3, p. 275
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- Article
Use of Minimax Probability Machine Regression for Modelling of Settlement of Shallow Foundations on Cohesionless Soil.
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- International Journal of Performability Engineering, 2014, v. 10, n. 3, p. 325
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- Article
Machine Learning Techniques Applied to Uniaxial Compressive Strength of Oporto Granite.
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- International Journal of Performability Engineering, 2014, v. 10, n. 2, p. 189
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- Article
A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data.
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- Sensors (14248220), 2018, v. 18, n. 11, p. 3704, doi. 10.3390/s18113704
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- Article
Support Vector Machine and Relevance Vector Machine for Prediction of Alumina and Pore Volume Fraction in Bioceramics.
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- International Journal of Applied Ceramic Technology, 2013, v. 10, p. E240, doi. 10.1111/j.1744-7402.2012.02810.x
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- Article
Modeling of tensile strength of rocks materials based on support vector machines approaches.
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- International Journal for Numerical & Analytical Methods in Geomechanics, 2013, v. 37, n. 16, p. 2655, doi. 10.1002/nag.2154
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- Article
Determination of liquefaction susceptibility of soil: a least square support vector machine approach.
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- International Journal for Numerical & Analytical Methods in Geomechanics, 2013, v. 37, n. 9, p. 1154, doi. 10.1002/nag.2081
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- Article
Determination of ultimate capacity of driven piles in cohesionless soil: A Multivariate Adaptive Regression Spline approach.
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- International Journal for Numerical & Analytical Methods in Geomechanics, 2012, v. 36, n. 11, p. 1434, doi. 10.1002/nag.1076
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- Article
Application of statistical learning algorithms to ultimate bearing capacity of shallow foundation on cohesionless soil.
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- International Journal for Numerical & Analytical Methods in Geomechanics, 2012, v. 36, n. 1, p. 100, doi. 10.1002/nag.997
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- Article
Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction.
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- Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2012, v. 16, n. 8, p. 1347, doi. 10.1007/s00500-012-0815-7
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Application of deep learning approaches to predict monthly stream flows.
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- Environmental Monitoring & Assessment, 2023, v. 195, n. 6, p. 1, doi. 10.1007/s10661-023-11331-5
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Newly explored machine learning model for river flow time series forecasting at Mary River, Australia.
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- Environmental Monitoring & Assessment, 2020, v. 192, n. 12, p. 1, doi. 10.1007/s10661-020-08724-1
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Prediction of fracture characteristics of high strength and ultra high strength concrete beams based on relevance vector machine.
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- International Journal of Damage Mechanics, 2014, v. 23, n. 7, p. 979, doi. 10.1177/1056789514520796
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- Article
A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features.
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- Sustainability (2071-1050), 2022, v. 14, n. 21, p. 14049, doi. 10.3390/su142114049
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Predicting permeability of tight carbonates using a hybrid machine learning approach of modified equilibrium optimizer and extreme learning machine.
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- Acta Geotechnica, 2022, v. 17, n. 4, p. 1239, doi. 10.1007/s11440-021-01257-y
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- Article
Assessment of rockburst risk using multivariate adaptive regression splines and deep forest model.
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- Acta Geotechnica, 2022, v. 17, n. 4, p. 1183, doi. 10.1007/s11440-021-01299-2
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- Article
A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique.
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- Structural Concrete, 2023, v. 24, n. 4, p. 5538, doi. 10.1002/suco.202200850
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A comprehensive review of machine learning‐based methods in landslide susceptibility mapping.
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- Geological Journal, 2023, v. 58, n. 6, p. 2283, doi. 10.1002/gj.4666
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Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs.
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- Hydrological Processes, 2012, v. 26, n. 9, p. 1361, doi. 10.1002/hyp.8278
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Prediction of Rainfall Using Support Vector Machine and Relevance Vector Machine.
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- Earth Science India, 2011, v. 4, n. 4, p. 188
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Application of Multivariate Adaptive Regression Splines to Evaporation Losses in Reservoirs.
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- Earth Science India, 2011, v. 4, n. 1, p. 15
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- Article
Spatial Variability of Rock Depth using Artificial Intelligence Techniques.
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- Earth Science India, 2010, v. 3, n. 4, p. 195
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- Article
Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth.
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- Mathematical Geosciences, 2010, v. 42, n. 4, p. 433, doi. 10.1007/s11004-010-9268-7
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Machine learning-enhanced Monte Carlo and subset simulations for advanced risk assessment in transportation infrastructure.
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- Journal of Mountain Science, 2024, v. 21, n. 2, p. 690, doi. 10.1007/s11629-023-8388-8
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Determination of reservoir induced earthquake using support vector machine and gaussian process regression.
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- Applied Geophysics: Bulletin of Chinese Geophysical Society, 2013, v. 10, n. 2, p. 229, doi. 10.1007/s11770-013-0381-5
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Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models.
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- Rock Mechanics & Rock Engineering, 2023, v. 56, n. 1, p. 487, doi. 10.1007/s00603-022-03046-9
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- Article
Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms.
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- Water Resources Management, 2020, v. 34, n. 3, p. 1075, doi. 10.1007/s11269-020-02484-w
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Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine.
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- Water Resources Management, 2019, v. 33, n. 11, p. 3965, doi. 10.1007/s11269-019-02346-0
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- Article
Predicting Probability of Liquefaction Susceptibility Based on a Wide Range of CPT Data.
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- International Journal of Geotechnical Earthquake Engineering, 2021, v. 12, n. 2, p. 1, doi. 10.4018/IJGEE.2021070102
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Modelling of Seismic Liquefaction Using Classification Techniques.
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- International Journal of Geotechnical Earthquake Engineering, 2021, v. 12, n. 1, p. 1, doi. 10.4018/IJGEE.2021010102
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- Article
Reliability Analysis of Liquefaction for Some Regions of Bihar.
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- International Journal of Geotechnical Earthquake Engineering, 2018, v. 9, n. 2, p. 23, doi. 10.4018/IJGEE.2018070102
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- Article
Lateral Displacement of Liquefaction Induced Ground Using Least Square Support Vector Machine.
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- International Journal of Geotechnical Earthquake Engineering, 2011, v. 2, n. 2, p. 29, doi. 10.4018/jgee.2011070103
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- Article
Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine.
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- ISRN Soil Science, 2012, p. 1, doi. 10.5402/2012/346439
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- Article
Reliability Analysis of Circular Footing by Using GP and MPMR.
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- International Journal of Applied Metaheuristic Computing, 2021, v. 12, n. 1, p. N.PAG, doi. 10.4018/IJAMC.2021010101
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- Article
Multivariate Adaptive Regression Spline and Least Square Support Vector Machine for Prediction of Undrained Shear Strength of Clay.
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- International Journal of Applied Metaheuristic Computing, 2012, v. 3, n. 2, p. 33, doi. 10.4018/jamc.2012040103
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- Article
Machine Learning Approach for Prediction of Lateral Confinement Coefficient of CFRP-Wrapped RC Columns.
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- Symmetry (20738994), 2023, v. 15, n. 2, p. 545, doi. 10.3390/sym15020545
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- Article
Prediction of swelling pressure of soil using artificial intelligence techniques.
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- Environmental Earth Sciences, 2010, v. 61, n. 2, p. 393, doi. 10.1007/s12665-009-0352-6
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- Article
Prediction of friction capacity of driven piles in clay using the support vector machine.
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- Canadian Geotechnical Journal, 2008, v. 45, n. 2, p. 288, doi. 10.1139/T07-072
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- Article