Evaluation of SPT-N values and internal friction angle correlation using artificial intelligence methods in granular soils
Arda Burak Ekmen
A *
+ Author Affiliations
- Author Affiliations
A Department of Civil Engineering, Harran University, Osmanbey Campus, 63000 Şanlıurfa, Turkey.
Soil Research 61(5) 495-509 https://doi.org/10.1071/SR22226
Submitted: 15 October 2022 Accepted: 15 December 2022 Published: 10 January 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Artificial neural networks (ANNs) and genetic algorithms (GAs) have become widely used in various engineering fields due to their ability to solve complicated issues directly.
Aims: In this study, internal friction angle (ϕ) values for granular soils were calculated using ANNs, GAs, and empirical methods based on standard penetration test (SPT) data to designate the system that produced the best statistical outcomes.
Methods: Utilising the literature, experimentally determined internal friction angle (Eϕ) values were obtained for a significant quantity of standard penetration test data. Analysis of variance was performed to ascertain whether there was a significant correlation between SPT-N60 values and Eϕ. A simulated network was created with ANNs, and a function was obtained with GAs for SPT-N60–ϕ correlation. The outcomes obtained with ANNs and GAs were compared with empirical equations and experimental results. Optimisation analysis was conducted with the novel Improved Goal Attainment method to minimise the margin of error.
Key results: Compared to the GAs and empirical equations, the ANN has been determined to have a reasonable correlation with experimental results.
Conclusions: It was determined that by utilising ANNs, the current empirical equations indicating the relationship between different soil parameters and the data of tests such as SPT and cone penetration test (CPT) could be produced in improved correlations by employing a large number of data sets obtained from different regions.
Implications: Effective predictions can be achieved instead of present methods.
Keywords: artificial neural networks, correlation, empirical equations, genetic algorithm, Goal Attainment method, internal friction angle, optimisation, standard penetration test.
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