Advancements in predicting soil liquefaction susceptibility: a comprehensive analysis of ensemble and deep learning approaches. [PDF]
Ranjan Kumar D, Wipulanusat W.
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Development of pile design methodology for an offshore wind farm in the North Sea [PDF]
Cathie, D +5 more
core
Estimation of liquefaction-induced settlement of shallow foundation by machine learning with imbalanced data. [PDF]
Sargin S +4 more
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Design of Cone Penetration Test Data Relay Transmission by Magnetic Communication. [PDF]
Yu X, Liu S, Pei H.
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An overview of in situ testing and geophysical methods to investigate municipal solid waste landfills. [PDF]
Juarez MB, Mondelli G, Giacheti HL.
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Real-time prediction of soil bearing capacity in clayey soils using drilling parameters and statistical modeling. [PDF]
Pande P +5 more
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Pareto-optimized stacked ensemble machine learning framework for predicting bearing capacity of driven piles from static load test data. [PDF]
Abdellatief M, ElNemr A, Altahrany A.
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Liquidity index of the Lublin loess as a function of cone resistance q<sub>c</sub> from CPTU test. [PDF]
Nepelski K +5 more
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Method for Predicting Void Ratio and Triaxial Friction Angle from Laboratory CPT at Shallow Depths [PDF]
Ibsen, Lars Bo, Larsen, Kim André
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Evaluation of Data Acquisition Areas in Geotechnical Seismic Tests: Insights from Field Applications. [PDF]
Kim G.
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