Modelling the monotonic and cyclic behaviour of sands using Artificial Neural Networks

Weixian Chen,Andrés Alfonso Peña Olarte,Roberto Cudmani,M.A Aguirre,L.A Pugnaloni,R Soto,S Luding

doi:10.1051/epjconf/202124911015

Weixian Chen, Andrés Alfonso Peña Olarte + Show 5 more

Open Access

https://doi.org/10.1051/epjconf/202124911015

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Abstract

In this study artificial neural networks (ANN) are used to simulate the monotonic and cyclic behaviour of sands observed in laboratory tests on Karlsruhe sand under drained and undrained conditions. A genetic algorithm (GA) is used to obtain an optimal framework for the ANN. The results show that the proposed genetic adaptive neural network (GANN) can effectively simulate drained and undrained monotonic triaxial behaviour of saturated sand under isotropic or anisotropic consolidation. The GANN is also able to predict satisfactorily the cyclic behaviour of sands under undrained triaxial test with strain and stress cycles. In addition, GANN is able to distinguish between monotonic drained and undrained conditions by delivering a good prediction when trained with the combined database.

Highlights

Granular soils exhibit under monotonic and cyclic shearing a complex macro-mechanical response
genetic adaptive neural network (GANN) is able to distinguish between monotonic drained and undrained conditions by delivering a good prediction when trained with the combined database
GANN can effectively simulate the monotonic behaviour of sands under drained and undrained triaxial test

Summary

Introduction

Granular soils exhibit under monotonic and cyclic shearing a complex macro-mechanical response. GA has been widely used in geotechnical engineering, e.g. the application in parameter identification and stress analysis such as the prediction of soil permeability coefficient [6], estimation of the pressuremeter modulus and limit pressure [7], and the calibration of constitutive models [8], among others. In this contribution, we apply ANN to simulate both the monotonic and cyclic behaviour of Karlsruhe sand observed in the lab under undrained and drained triaxial conditions [9]. The Genetic Adaptive Neural Network (GANN) is used to optimize the ANN to realistically predict the monotonic and cyclic behaviour observed in the experiments with Karlsruhe sand

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