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A Model to Predict the Standard Penetration Test N60 Value from Cone Penetration Test Data
This research uses gene expression programming (GEP) in the development of a model that considers the cone penetration (CPT) data and predicts the N60 value. Some 140 CPT-SPT pairs of data points for sand, sandy, silt, and silty sand soils were used in the GEP model. The results have revealed that the GEP model can predict the standard penetration (SPT) N60 values with remarkable accuracy with high R2 values and low error for the training dataset. A comparison in terms of statistical performance between artificial neural network models (ANN) developed previously and the developed GEP model has been presented. The GEP model showed better results than the ANN model in predicting the N60 value.
A Model to Predict the Standard Penetration Test N60 Value from Cone Penetration Test Data
This research uses gene expression programming (GEP) in the development of a model that considers the cone penetration (CPT) data and predicts the N60 value. Some 140 CPT-SPT pairs of data points for sand, sandy, silt, and silty sand soils were used in the GEP model. The results have revealed that the GEP model can predict the standard penetration (SPT) N60 values with remarkable accuracy with high R2 values and low error for the training dataset. A comparison in terms of statistical performance between artificial neural network models (ANN) developed previously and the developed GEP model has been presented. The GEP model showed better results than the ANN model in predicting the N60 value.
A Model to Predict the Standard Penetration Test N60 Value from Cone Penetration Test Data
Soil Mech Found Eng
Al Bodour, W. (Autor:in) / Tarawneh, B. (Autor:in) / Murad, Y. (Autor:in)
Soil Mechanics and Foundation Engineering ; 59 ; 437-444
01.11.2022
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
A Model to Predict the Standard Penetration Test $ N_{60} $ Value from Cone Penetration Test Data
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