Preprints
https://doi.org/10.5194/egusphere-2023-1690
https://doi.org/10.5194/egusphere-2023-1690
18 Sep 2023
 | 18 Sep 2023

NorSand4AI: A Comprehensive Triaxial Test Simulation Database for NorSand Constitutive Model Materials

Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante

Abstract. To learn, humans observe and experience the world, collect data, and establish patterns through repetition. In scientific discovery, these patterns and relationships are expressed as laws and equations, data as properties and variables, and observations as events. Data-driven techniques aim to provide an impartial approach to learning using raw data from actual or simulated observations. In soil science, parametric models known as constitutive models are used to represent the behavior of natural and artificial materials. Creating data-driven constitutive models using deep learning techniques requires large and consistent datasets, which are challenging to acquire through experiments. Synthetic data can be generated using a theoretical function, but there is a lack of literature on high-volume and robust datasets of this kind. Digital soil models can be utilized to conduct numerical simulations that produce synthetic results of triaxial tests, which are regarded as the preferred tests for assessing soil's constitutive behavior. Due to its limitations for modeling real sands, the Modified Cam Clay model has been replaced by the NorSand model in some situations where sand-like materials need to be modelled. Therefore, for a material following the NorSand model, the present paper presents a first-of-its-kind database that addresses the size and complexity issues of creating synthetic datasets for nonlinear constitutive modeling of soils by simulating both drained and undrained triaxial tests of 2000 soil types, each subjected to 40 initial test configurations, resulting in a total of 160000 triaxial test results. Each simulation dataset comprises a 4000 × 10 matrix that can be used for general multivariate forecasting benchmarks, in addition to direct geotechnical and soil science applications.

Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1690', Anonymous Referee #1, 26 Oct 2023
    • AC1: 'Reply on RC1', Luan Carlos de Sena Monteiro Ozelim, 11 Nov 2023
  • RC2: 'Comment on egusphere-2023-1690', Anonymous Referee #2, 27 Oct 2023
    • AC2: 'Reply on RC2', Luan Carlos de Sena Monteiro Ozelim, 11 Nov 2023
Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante

Data sets

Dataset - NorSand4AI: A Comprehensive Triaxial Test Simulation Database for NorSand Constitutive Model Materials Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante https://doi.org/10.5281/zenodo.8170537

Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante

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Short summary
The paper addresses the quantity and complexity of synthetic datasets for nonlinear constitutive modelling of soils following the NorSand model by simulating both drained and undrained triaxial tests of 2000 soil types, with a total of 160000 triaxial test results made available. Each simulation dataset comprises a 4000 by 10 matrix that can be used for general multivariate forecasting benchmarks, apart from direct geotechnical and soil science applications.