Developing multivariate adaptive regression splines model for predicting the undrained shear strength of clayey soil from cone penetration test data

2022 ◽  
Author(s):  
Dai Yu
Keyword(s):  
Shear Strength ◽  
Clayey Soil ◽  
Undrained Shear Strength ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Penetration Test ◽  
Cone Penetration ◽  
Adaptive Regression ◽  
Adaptive Regression Splines ◽  
Undrained Shear

Using multivariate adaptive regression splines (MARS) to identify relationships between soil and corn (Zea mays L.) production properties

2005 ◽  
Vol 85 (5) ◽  
pp. 625-636 ◽  
Author(s):  
K. M. Turpin ◽  
D. R. Lapen ◽  
E. G. Gregorich ◽  
G. C. Topp ◽  
M. Edwards ◽  
...  
Keyword(s):  
Zea Mays L ◽  
Production Systems ◽  
Multivariate Adaptive Regression Splines ◽  
Corn Yield ◽  
Data Sets ◽  
Regression Splines ◽  
Cone Penetration ◽  
Crop Establishment ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

Over-application of agricultural fertilizers can contribute to degradation of surface water quality. Factors governing crop establishment and yield must be identified in order to efficiently manage N application rates in corn (Zea mays L.) production systems. Spatial data sets of corn establishment and grain yields, and soil physical and chemical parameters were obtained for two corn production systems on a poorly drained clay loam soil in eastern Ontario, Canada, during low yielding conditions in 2000. The multivariate adaptive regression splines (MARS) automated regression data mining method was used to determine the dominant factors affecting both crop establishment and yield from these data sets. The analysis using MARS suggests that soil water content and cone penetration resistance are more important than elevation and spring mineral soil N (NH4+ + NO3−) in predicting crop establishment and grain yield. The MARS approach proved to be a useful method for identifying relationships between potential yield-governing variables. It also helped elucidate potential cause and effect processes, and in so doing, helped identify areas within the field where soil physical parameters may have been more important than nutrients in governing corn yield. Key words: Mulitvariate adaptive regression splines, corn yield, cone penetration resistance, soil water content, soil N, topography

scholarly journals Cone Penetration Test in Stiff Over Soft Clay in Centrifuge Test

2019 ◽  
Author(s):  
Qiang Xie ◽  
Yuxia Hu ◽  
Mark Cassidy ◽  
Alireza Salehi
Keyword(s):  
Shear Strength ◽  
Layer Thickness ◽  
Soft Clay ◽  
Undrained Shear Strength ◽  
Centrifuge Test ◽  
Clay Layer ◽  
Penetration Test ◽  
Cone Penetration ◽  
Cone Resistance ◽  
Undrained Shear

Abstract This paper describes a numerical study on soil characterization of stiff over soft clays in centrifuge test using cone penetration test (CPT), especially when the top stiff layer is thin relative to the centrifuge cone size. An extensive parametric study was conducted using large deformation finite element (LDFE) analysis, with the cone penetrating continuously from the soil surface. The LDFE model has been validated against existing physical test data with very good agreement. Since the bottom soft clay was normally thick enough to fully mobilise the ultimate cone resistance, its undrained shear strength can be interpreted by the existing approach for cone deep penetration in a uniform clay layer. Thus, the challenge was to interpret the strength of the top stiff layer, where the layer thickness was not thick enough to fully mobilise its ultimate resistance. Both top layer thickness ratios (to the cone diameter) and layer strength ratios were considered in the parametric study. Based on the results from LDFE analyses, the interpretation formula of the undrained shear strength in the top stiff layer was proposed as a set of new bearing factors. The proposed cone bearing factor was a function of the ratio of the measured peak cone resistance in the top layer to the stable/ultimate cone resistance in the bottom layer and the ratio between the top layer thickness to the cone diameter. The formula can be used directly when the top layer thickness was known based on the sample preparation. However, the layer interface can be identified based on the study here, if the top layer thickness was not certain. A design flow chart was provided for interpretations of top clay layer strength and top layer thickness based on the cone resistance profile obtained from CPT test.

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