Abstract
A constitutive model for the stress-strain relationship of single forest root system was developed in order to provide theoretical foundations for the mechanisms of soil-reinforcement by root system and offer a reliable basis for the analysis of root tensile strength character. This study started a general form of linear and non-linear stress-strain relation that was mathematically defined by four boundary conditions observed in typical tensile tests of single roots. The parameters of the model were determined by experiment data and had definite physical meaning. The model was verified by experiment data, which showed that the calculated values were in good agreement with the experimental single root tensile test results. The constitutive model was validated and found to be feasible for modeling single root tensile stress.
Highlights
Roots are key plant organs that perform vital functions such as absorbing and storing soil moisture, nutrients, and energy, as well as anchoring the plant to the soil
It is a safe assumption that the root deformation is mainly linear at 40% of the tensile strength limit for Pinus tabulaeformis root system
The ultimate aim of our research is to study the role of tree roots in the slope stabilization, the specific aim of this paper is to build a constitutive model of single root system with a clear physical meaning through the rigorous mathematical derivation
Summary
Roots are key plant organs that perform vital functions such as absorbing and storing soil moisture, nutrients, and energy, as well as anchoring the plant to the soil. Some models have been developed to describe the constitutive relationship(the stress-strain relationship) of single root system. Most models use different mathematical functions, such as linear, hyperbolic, polynomial, or power function [11,12,13] to describe the relationship between stress and strain. Those functional constitutive relationships are empirical in nature, since mathematical functions are selected based on the best curve fitting to experimental data. The applicability and accuracy of those models are greatly limited [13,14]
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