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An empirical model for predicting the effect of stabilisation on the stress-strain behaviour of compressed earth materials

Lookup NU author(s): Dr Agostino BrunoORCiD



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


© 2021 Elsevier Ltd. This paper proposes a simple empirical model for predicting the effect of stabilisation on the compressive stress – axial strain behaviour of compressed earth materials. The model relies on the definition of two distinct functions that respectively describe the pre- and post-peak behaviour of stabilised earth samples subjected to unconfined compressive strength tests. Overall, the two functions depend on a total of four model parameters with a clear physical meaning. In turn, these model parameters depend on the stabiliser content and this dependency is described by means of three different and alternative stabilisation functions (i.e. relationships between a given model parameter and the stabiliser content). These stabilisation functions enable the prediction of the compressive stress – axial strain behaviour for any level of stabiliser content, thus reducing the need of time consuming and expensive experimental campaigns. The proposed modelling approach has been calibrated and subsequently validated against two sets of experimental data obtained on an engineered soil stabilised either with guar gum or with xanthan gum at different stabiliser contents ranging from 0.5% to 3.0%. Results show the good capability of the proposed model to reproduce the initial modulus of elasticity, the compressive energy dissipated by the earth samples up to their failure, the peak compressive strength and the post-peak softening behaviour.

Publication metadata

Author(s): Bruno AW

Publication type: Article

Publication status: Published

Journal: Journal of Building Engineering

Year: 2021

Volume: 44

Print publication date: 01/12/2021

Online publication date: 17/07/2021

Acceptance date: 12/07/2021

Date deposited: 26/09/2021

ISSN (electronic): 2352-7102

Publisher: Elsevier Ltd


DOI: 10.1016/j.jobe.2021.102980


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