The Discrete and Continuous Retardation and Relaxation Spectrum Method for Viscoelastic Characterization of Warm Mix Crumb Rubber-Modified Asphalt Mixtures

Wang

et al. 2020

Materials

Self Cite

To identify the most accurate approach for constructing of the dynamic modulus master curves for warm mix crumb rubber modified asphalt mixtures and assess the feasibility of predicting the phase angle master curves from the dynamic modulus ones. The SM (Sigmoidal model) and GSM (generalized sigmoidal model) were utilized to construct the dynamic modulus master curve, respectively. Subsequently, the master curve of phase angle could be predicted from the master curve of dynamic modulus in term of the K-K (Kramers–Kronig) relations. The results show that both SM and GSM can predict the dynamic modulus very well, except that the GSM shows a slightly higher correlation coefficient than SM. Therefore, it is recommended to construct the dynamic modulus master curve using GSM and obtain the corresponding phase angle master curve in term of the K-K relations. The Black space diagram and Wicket diagram were utilized to verify the predictions were consistent with the LVE (linear viscoelastic) theory. Then the master curve of storage modulus and loss modulus were also obtained. Finally, the creep compliance and relaxation modulus can be used to represent the creep and relaxation properties of warm-mix crumb rubber-modified asphalt mixtures.

Section: Comparing To the Creep Compliancementioning

confidence: 99%

Section: Comparing To the Relaxation Modulusmentioning

confidence: 99%

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Predict the Phase Angle Master Curve and Study the Viscoelastic Properties of Warm Mix Crumb Rubber-Modified Asphalt Mixture

Wang

et al. 2020

Materials

Self Cite

“…As a typical static or quasi‐static loading test, the creep experiment illustrates the time‐dependent strain response of viscoelastic material under unit stress, and it is widely used to evaluate the rheological performance of products based on starch (Asyraf et al, 2019; Sanz et al, 2017; Zhang et al, 2020). Onyango et al (2010) investigated how the creep‐recovery characteristics of bread affected gluten‐free batter by regulating the quantity and diversity of gluten proteins.…”

Section: Introductionmentioning

confidence: 99%

Bread staling prediction with a multiobjective particle swarm optimization‐based bread constitutive modeling method

Huang

Journal of Texture Studies

et al. 2023

Aiming at the complex and cumbersome problems of current bread staling detection technology, a food constitutive modeling method based on the multiobjective particle swarm optimization (MOPSO) was proposed, which can quickly and efficiently identify the creep test parameters for bread, and predict the viscoelastic parameters of bread staling using the analyzed viscoelastic parameters, resulting in convenient and efficient detection of bread staling. Firstly, airflow-laser detection technology was used to carry out rapid, efficient, and non-destructive bread rheological tests to obtain bread creep test data. The MOPSO based on the Pareto set was then used to identify the generalized Kelvin model, and the discrimination accuracy was evaluated by using the inversion results established by the viscoelastic parameters, which resulted in efficient discrimination of creep test data of starch-based products represented by bread. Finally, using extreme learning machine regression (ELM), a prediction model between the analysis results and the moisture content of bread staling was established, and the prediction effect of the analysis results on bread staling was verified. The experimental results show that, when compared to finite element analysis (FEA) and non-linear regression (NLR) to identify creep parameters, the MOPSO overcomes the shortcomings of easy falling into the local optimal solution, is easy to implement, has strong global search ability, and is suitable for the analysis of highdimensional viscoelastic models of complex foods. The correlation coefficient (R) of the prediction set established by the 12-membered viscoelastic parameters in the prediction model established by multi-element viscoelastic parameters and bread moisture content was 0.847, and the root mean square error (RMSE) was 0.021. This demonstrated that, when combined with MOPSO, airflow-laser detection technology could effectively identify the viscoelastic parameters of bread and establish a method suitable for monitoring bread staling in industrial production. The results of this study provide a reference for the identification of viscoelastic parameters of complex foods and to detect bread staling quickly and efficiently.

“…Aiming at this issue, Sun et al [26] presented a numerical approach to determining a continuous spectrum from the other. Bhattacharjee et al [27] and Zhang et al [28] calculated the two continuous spectra from storage modulus and storage compliance separately based on the Sigmoidal function and the generalized Sigmoidal function; however, due to the inconsistency of the model parameters of the storage modulus and storage compliance, the LVE relationship cannot be strictly satisfied.…”

Section: Introductionmentioning

confidence: 99%

Fast-Acquiring High-Quality Prony Series Parameters of Asphalt Concrete through Viscoelastic Continuous Spectral Models

Sun

2022

Materials

Prony series representations have been extensively applied to characterizing the time-domain linear viscoelastic (LVE) material functions for asphalt concrete. However, existing methods that can generate high-quality Prony series parameters (i.e., discrete spectra) mostly involve complicated programming algorithms, which poses a challenge for quick access of Prony series parameters. Also, very limited research has been devoted to establishing methods for simultaneously determining both retardation and relaxation spectra. To resolve these issues, this study presented a practical approach to fast acquiring high-quality Prony series parameters for both relaxation modulus and creep compliance of asphalt concrete by using the complex modulus test data. The approach adopts the analytical representations of the continuous relaxation and retardation spectra from the Havriliak-Negami (HN) and 2S2P1D complex modulus models to directly determine the discrete spectra, and the elastic constants, Ee and Dg, for both LVE modulus and compliance functions are further calculated by fitting the corresponding generalized Maxwell model representations to smoothed data from the storage modulus representations of the HN and 2S2P1D complex modulus models. In this way, all the procedures in the proposed method can be easily implemented in Microsoft Excel. The results showed that the HN and 2S2P1D models yielded slightly different continuous spectral patterns at shorter relaxation times and longer retardation times. However, at the region covered by the test data, the continuous spectra of the two complex modulus models were very close to each other. Thus, the two models can generate comparable Prony series parameters within the time or frequency range covered by the test data. Considering that the quality of the resulting Prony series parameters are closely related to the master curve models used for presmoothing, the HN and 2S2P1D models were compared with the conventional Sigmoidal model. Additionally, the Black diagram was recommended for examining the quality of the complex modulus test data before constructing the master curves.