Spatial interpolation of SPT data and prediction of consolidation of clay by ANN method

Abstract

Artificial Intelligence (AI) is anticipated to be the future of technology. Hence, AI has been applied in various fields over the years and its applications are expected to grow in number with the passage of time. There has been a growing need for accurate, direct, and quick prediction of geotechnical and foundation engineering models especially since the success of each project relies on numerous amounts of data. In this study, two applications of AI in the field of geotechnical and foundation engineering are presented – spatial interpolation of standard penetration test (SPT) data and prediction of consolidation of clay. SPT and soil profile data may be predicted and estimated at any location and depth at a site that has no available borehole test data using artificial intelligence techniques such as artificial neural networks (ANN) based on available geospatial information from nearby boreholes. ANN can also be used to accelerate the calculation of various theoretical methods such as the one-dimensional consolidation theory of clay with high efficiency by using lesser computation resources. The results of the study showed that ANN can be a valuable, powerful, and practical tool in providing various information that is needed in geotechnical and foundation design.