This study was aimed at evaluating the possibility to use the Friedrich-Braun fractional derivative rheological model to assess the viscoelastic properties of xanthan gum with rice starch and sweet potato starch. The Friedrich-Braun fractional derivative rheological model allows to describe viscoelastic properties comprehensively, starting from the behaviour characteristic of purely viscous fluids to the behaviour corresponding to elastic solids. The Friedrich-Braun fractional derivative rheological model has one more virtue which distinguishes it from other models, it allows to determine the relationship between stress and strain and the impact of each of them on viscoelastic properties on the tested material. An analysis of the data described using the Friedrich-Braun fractional derivative rheological model allows to state that all the tested mixtures of starch with xanthan gum form macromolecular gels exhibiting behaviour typical of viscoelastic quasi-solid bodies. The Friedrich-Braun fractional derivative rheological model and 8 rheological parameters of this model allow to determine changes in the structure of the examined starch - xanthan gum mixtures. Similarly important is the possibility to find out the trend and changes going on in this structure as well as their causes.

The article presents a constitutive model for Shape Memory Alloys (SMA) along with result of dynamic simulations of SMA model. The applications of devices incorporating SMA in civil engineering focus mostly on mitigation of the seismic hazard effects in new-build and historical buildings or improvement of fatigue resilience. The unique properties of SMA, such as shape memory effect and superelasticity give promising results for such applications. The presented model includes additional phenomenon of SMA – internal loops. The paper shows the method of formulation of physical relations of SMA based on special rheological structure, which includes modified Kepes’s model. This rheological element, introduced as dual-phase plasticity body, is given in the context of martensite phase transformation. One of the advantages of such an approach is a possibility of formulation of constitutive relationships as a set of explicit differential equations. The application of the model is demonstrated on example of dynamic simulations of three dimensional finite element subjected to dynamic excitation.

Hot deformation of metals is a widely used process to produce end products with the desired geometry and required mechanical properties. To properly design the hot forming process, it is necessary to examine how the tested material behaves during hot deformation. Model studies carried out to characterize the behaviour of materials in the hot deformation process can be roughly divided into physical and mathematical simulation techniques.
The methodology proposed in this study highlights the possibility of creating rheological models for selected materials using methods of artificial intelligence, such as neuro-fuzzy systems. The main goal of the study is to examine the selected method of artificial intelligence to know how far it is possible to use this method in the development of a predictive model describing the flow of metals in the process of hot deformation.
The test material was Inconel 718 alloy, which belongs to the family of austenitic nickel-based superalloys characterized by exceptionally high mechanical properties, physicochemical properties and creep resistance. This alloy is hardly deformable and requires proper understanding of the constitutive behaviour of the material under process conditions to directly enable the optimization of deformability and, indirectly, the development of effective shaping technologies that can guarantee obtaining products with the required microstructure and desired final mechanical properties.
To be able to predict the behaviour of the material under non-experimentally tested conditions, a rheological model was developed using the selected method of artificial intelligence, i.e. the Adaptive Neuro-Fuzzy Inference System (ANFIS).
The source data used in these studies comes from a material experiment involving compression of the tested alloy on a Gleeble 3800 thermo-mechanical simulator at temperatures of 900, 1000, 1050, 1100, 1150oC with the strain rates of 0.01 - 100 s-1 to a constant true strain value of 0.9.
To assess the ability of the developed model to describe the behaviour of the examined alloy during hot deformation, the values of yield stress determined by the developed model (ANFIS) were compared with the results obtained experimentally. The obtained results may also support the numerical modelling of stress-strain curves.

The article presents modelling using artificial neural networks (ANN) of the phenomenon of creep of comply polymer SIKA PS which can be used in various applications in civil engineering. Data for modelling was gathered in compressive experiments conveyed under a set of fixed conditions of compressive stress and temperature. Part of the datawas pre-processed by smoothing and rediscretisation and served as inputs and targets for network training and part of the data was left raw as control set for verification of prognosing capability. Assumed neural network architectures were one- and two-layer feedforward networks with Bayesian regularisation as a learning method. Altogether 55 networks with 8 to 12 neurons in varying structural configurations were trained. Fitting and prognosing verification was performed using mean absolute relative error as a measure; also, results were plotted and assessed visually. In result, the research allowed for formulation of a new rheological model for comply polymer SIKA PS in time, stress and temperature field domain with fitting quality of mean absolute relative error 1.3% and prognosis quality of mean absolute relative error 8.73%. The model was formulated with the use of a two-layer network with 5+5 neurons.

Sewage sludge is a two-phase mixture, generated during the treatment of domestic sewage in waste water treatment plants. It consists of 90-99% water and an accumulation of settleable solids. mainly organic that are removed during primary, secondary or advanced wastewater treatment processes. The hydration of the sludge is one of its main properties which determines sludge management and waste disposal cost. The flow properties of the sewage sludge, such as settling properties and concentration of solids. may affect its hydraulics. Application of rheology in wastewater treatment is determined by the flow character of the sludge. The basic purpose of the investigation was to define the rheological properties of sludge taken from secondary settling tanks in a typical municipal wastewater treatment plant. A laboratory investigation was conducted using a coaxial cylinder with a rotating torque and gravimetric concentration of the investigated sludge ranged from 2.21 to 6.56%. Approximation was made after transforming the pseudo-curve obtained from the measurements into the true flow curve, which was made according to the equation provided by Krieger, Elrod, Maron and Svec. In order to describe rheological characteristics the 3-parameter Herschel-Bulkley model was applied. The correlation between rheological parameters -r , k, n and concentration C was calculated as well as between periods of time when the samples of sludge were 'taken. The research has allowed calculating the dimension of the main transport installation pumping sludge and optimizing the pump discharge pressure, when transporting viscous sludge in pipelines. Determination of rheological parameters, especially yield stress tr), is important in sludge management, for instance in designing parameters transporting, storing, spreading.