The impact of uncertain parameters on ratchetting trends in hypoplasticity

summary:Perturbed parameters are considered in a hypoplastic model of granular materials. For fixed parameters, the model response to a periodic stress loading and unloading converges to a limit state of strain. The focus of this contribution is the assessment of the change in the limit strain caused by varying model parameters

On models of long-term behavior of concrete

summary:Long-term behavior of concrete is modeled by several widely accepted models, such as B3, \emph{fib} MC 2010, or ACI 209 whose input parameters and output values are not identical to each other. Moreover, the input and, consequently, the output values are uncertain. In this paper, fuzzy input parameters are considered in uncertainty quantification of each model response and, finally, the sets of responses are analyzed by elementary tools of evidence theory. That is, belief and plausibility functions are proposed to combine evidence from different models

Stress-controlled hysteresis and long-time dynamics of implicit differential equations arising in hypoplasticity

summary:A long-time dynamic for granular materials arising in the hypoplastic theory of Kolymbas type is investigated. It is assumed that the granular hardness allows exponential degradation, which leads to the densification of material states. The governing system for a rate-independent strain under stress control is described by implicit differential equations. Its analytical solution for arbitrary inhomogeneous coefficients is constructed in closed form. Under cyclic loading by periodic pressure, finite ratcheting for the void ratio is derived in explicit form, which converges to a limiting periodic process (attractor) when the number of cycles tends to infinity

Hydrological applications of a model-based approach to fuzzy set membership functions

summary:Since the common approach to defining membership functions of fuzzy numbers is rather subjective, another, more objective method is proposed. It is applicable in situations where two models, say $M_1$ and $M_2$, share the same uncertain input parameter $p$. Model $M_1$ is used to assess the fuzziness of $p$, whereas the goal is to assess the fuzziness of the $p$-dependent output of model $M_2$. Simple examples are presented to illustrate the proposed approach