Knowledge Based Prediction of Standard Penetration Resistance of Soil Using Geotechnical Database

Abstract

The current study aimed at predicting standard penetration resistance (N) of soil using particle sizes and Atterberg's limits. The geotechnical database was created subsequent to the field and laboratory testing. The sample collection points were distributed in a mesh grid pattern to have uniform sampling consistency. Artificial Neural Networks (ANN) were trained on the database to build a knowledge-based understanding of the interrelation of the given soil parameters. To check the efficacy of the model, the validation was carried out by predicting standard penetration resistance (N) for another 30 samples which were not included in the training data (444 samples). The trained ANN model has been found to predict N values in close agreement with the N values measured in the field. The novelty of the research work is the standard penetration prediction employing basic physical properties of soil. This proves the efficacy of the proposed model for the target civil engineering application. Doi: 10.28991/CEJ-SP2021-07-01 Full Text: PDF

Compaction Quality Control of Coarse-grained Soils Using Dynamic Penetration Test Results through Correlation with Relative Compaction Percentages

“…Accordingly, some studies that the penetration test results should be corrected for different depths. Also, in the standard penetration test (SPT) which is used significantly in literature [7,8]. This correction is referred to as overburden correction [9].…”