Machine learning algorithms for applications in geotechnical engineering

ABSTRACT: Artificial neural networks (ANNs) applications are increasingly common in all fields of engineering. One of the main obstacles to the development of ANN applications in geotechnical engineering is the need for large datasets. This paper presents two application examples in which numerical methods were used to generate large datasets. The first example involves the determination of grain size distributions from soil pictures based on a dataset that includes 53130 synthetic soil images corresponding to different particle size distributions. The pictures were generated using YADE, a discrete element code. The second application example involves the use of the finite element method to generate a dataset. COMSOL’s MATLAB programming interface was used to generate a large number of finite element simulations to predict the water level in the reservoir of a hypothetical dam based on pore pressure measurements obtained using an array of 15 piezometers.