Application of adaptive neuro-fuzzy inference system for numerical interpretation of soil torsional shear test results

Abstract

The aim of this study is to replace the stress-strain constitutive relation within a finite element method (FEM) implementation with an adaptive neuro-fuzzy inference system (ANFIS). The computation is supported by the Compute Unified Device Architecture (CUDA) technology available on modern Nvidia graphics processing units (GPUs). The multi-threaded multiple input / multiple output (MIMO) CUDA-ANFIS model is compared with experimental results of torsional shear (TS) tests. The cylindrical non-cohesive soil samples were tested within the small strain range (0.001–0.1%). Particular attention was paid to a good reflection of the results of laboratory tests in numerical simulations based on FEM with embedded ANFIS. The obtained results confirm the possibility of replacing explicit constitutive relations with a neuro-fuzzy inference system and using this model to determine the soil mechanical properties in engineering practice. The most important finding is that the decrease in the discretization mesh density as well as the decrease in the number of linguistic labels result in better overall system performance. In addition, the created ANFIS algorithm adapted to the GK110 graphics processor architecture speeds up calculations 27 times compared to single-threaded implementation. The presented approach can be used to construct implicit constitutive models based on the numerical interpretation of the results obtained from laboratory and/or field tests.