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Gaussian process regression (GPR) 2

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effective stress parameter 1

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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2, Pages 214-223 doi: 10.1007/s11709-021-0800-2

Abstract: In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structuraldrilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR

Keywords： concrete structure GPR damage classification convolutional neural network transfer learning

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Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 2, Pages 133-136 doi: 10.1007/s11709-013-0202-1

Abstract: This article examines the capability of Gaussian process regression (GPR) for prediction of effectiveGPR method proceeds by parameterising a covariance function, and then infers the parameters given theInput variables of GPR are net confining pressure ( ), saturated volumetric water content ( ), residualThe developed GPR gives the variance of predicted .The results show that the developed GPR is reliable model for prediction of of unsaturated soil.

Keywords： unsaturated soil effective stress parameter Gaussian process regression (GPR) artificial neural network

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Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural network and Gaussian process regression

Frontiers in Energy doi: 10.1007/s11708-023-0906-4

Abstract: , a two-phase RUL early prediction method combining neural network and Gaussian process regression (GPRIn the second phase, GPR uses the DEM as the initial parameter to predict each test set’s early RUL (

Keywords： lithium-ion batteries RUL prediction double exponential model neural network Gaussian process regression (GPR