Exploring directional energy conversion behavior of electromagnetic-based multifunctional asphalt pavement

Fu, Chaoliang; Li, Kai; Liu, Quantao; Xu, Pengcheng; Dai, Dongling; Tong, Jianhang

doi:10.1016/j.energy.2022.126573

Cited by 23 publications

(6 citation statements)

References 24 publications

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“…As composite materials, the meso-structures of asphalt mixtures and cement concrete should affect the macro thermal properties of the materials. This is an important reason why many studies [15][16][17][18][19][20][21][22], including this one, used models that closely resemble the structure of real asphalt mixtures and cement concrete for analysis. However, it is also reported in the literature that exact microstructure representation of these materials is unnecessary for thermal conductivity prediction [34,35].…”

Section: Discussion On Materials Meso-structurementioning

confidence: 99%

“…However, since the material structures of construction materials are usually more complex than the above idealized ones, simply applying these models on construction materials may cause large errors. To solve this problem, one approach is to develop numerical models based on heterogeneous structures [15][16][17][18][19][20][21][22][23], and the other is to derive more complex but applicable analytical or semi-analytical solutions [24][25][26][27][28][29]. In the numerical models, the effective thermal conductivity was calculated by analyzing the heat flux in the representative heterogeneous structure in the steady state.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Aided Homogenization Approach for Predicting Effective Thermal Conductivity of Composite Construction Materials

Zhu

Peng²,

Chen

et al. 2023

Materials

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Thermal conductivity is a fundamental material parameter involved in various infrastructure design guides around the world. This paper developed an innovative neural network (NN) aided homogenization approach for predicting the effective thermal conductivity of various composite construction materials. The 2-D meso-structures of dense graded asphalt mixture, porous asphalt mixture, and cement concrete were generated and divided into 2n × 2n square elements with specific thermal conductivity values. A two-layer feed-forward neural network with sigmoid hidden neurons and linear output neurons was built to predict the effective thermal conductivity of the 2 × 2 block. The Levenberg-Marquardt backpropagation algorithm was used to train the network. By repeatedly using the neural network, the effective thermal conductivities of 2-D meso-structures were calculated. The accuracy of the above NN aided homogenization approach was validated with experiment, and various factors affecting the effective thermal conductivity were analyzed. The analysis results show that the accuracy of the NN aided approach is acceptable with relative errors of 1.92~4.34% for the dense graded asphalt mixture, 1.10~6.85% for the porous asphalt mixture, and 1.13~3.14% for the cement concrete. The relative errors for all the materials are lower than 5% when the heterogeneous structures are divided into 512 × 512 elements. Ignoring the actual material meso-structures may lead to significant errors (134.01%) in predicting the effective thermal conductivity of materials with high heterogeneity such as porous asphalt mixture. While proper simplification is acceptable for dense construction composite materials. The effective thermal conductivity of composite cement-asphalt mixtures increases with higher saturation of grouted material. However, the improvement effect of the high-conductive cement paste on the composite cement-asphalt mixtures could be significantly reduced when the cement paste concentrates at the bottom of the mixture. Cracked aggregates and segregation of material components tend to decrease the effective thermal conductivity of construction materials. The NN aided homogenization approach presented in this paper is useful for selecting the effective thermal conductivity of construction materials.

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Section: Discussion On Materials Meso-structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network Aided Homogenization Approach for Predicting Effective Thermal Conductivity of Composite Construction Materials

Zhu

Peng²,

Chen

et al. 2023

Materials

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“…HD-32VNAWKN waveguide components were used in the laboratory due to the limitations of experimental instruments. As the dominant and foundational mode for the system's operation [35], a TE 10 waves mode with characteristics of a simple field structure, stability, a wide frequency band and low electromagnetic loss was adopted in this study. Therefore, the complex permittivity ranging from 2.6 to 3.9 GHz was gauged and simulated in this study.…”

Section: Materials and Specimens In The Experimentsmentioning

confidence: 99%

Three-Dimensional Meso-Structure-Based Model for Evaluating the Complex Permittivity of Asphalt Concrete

Xie,

Chen,

Wang

et al. 2024

Materials

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Microwave heating is an emerging alternative pretreatment method for road maintenance in cold seasons. The thermal behavior of asphalt pavement under microwave heating is mainly determined by the complex permittivity of the asphalt mixture. In this study, an innovative approach for calculating the complex permittivity of an asphalt mixture based on a three-dimensional meso-scale heterogeneous structure was proposed. A series of experiments was conducted to verify the accuracy of this approach. The effect of porosity, void size, moisture content and aggregate gradation on the complex permittivity for an asphalt mixture were computationally analyzed based on the validated approach. Moreover, the applicability of commonly used classical dielectric models was analyzed. The classical Lichtenecker–Rother (LR) dielectric model was modified on the basis of simulation data for various conditions. The results showed that the real part of the complex permittivity decreased with the increase in porosity. Some sudden change in the imaginary part of the complex permittivity was observed within the frequency range from 2.6 GHz to 3.9 GHz. A larger air void size would lead to a larger frequency at which sudden change occurs. The real part and imaginary part of the complex permittivity tend to be smaller when more coarse aggregates are replaced with fine aggregates. Both the real part and the imaginary part of the complex permittivity increase with higher moisture content due to the stronger dielectric property of water. Each 1% increase in moisture content would lead to about a 3~4% increase in the real part of the complex permittivity. The determination coefficients R2 for the real part and the imaginary part of the complex permittivity fitted by the modified Brown model were the maximum values, which were 0.922 and 0.980, respectively. The method presented in this study is useful for transportation agencies to optimize microwave heating during winter maintenance.

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“…At the same time, the asphalt mixture is one of the most common types of pavement surface materials used in the world. However, the climate environment and continuous traffic loads can significantly impact the asphalt mixture's ability to resist pavement diseases (Shen et al 2024, Fu et al 2023). To enhance the service life of asphalt mixtures in challenging conditions, bitumen, acting as the binder that binds individual aggregates in the asphalt mixture, becomes the primary component targeted for modification.…”

Section: Introductionmentioning

confidence: 99%

Feasibility investigation of using waste laminated packaging as bitumen performance enhancer

Fu,

Liu,

Bornmann

et al. 2024

Mater. Res. Express

Self Cite

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Million tons of laminated packaging are extensively used in aseptic food packages due to its merits in transporting and storing liquid foods, and thus a huge amount of waste laminated packaging (WLP) needs to be handled urgently. The main component of the WLP is the cellulose fibers, followed by the low-density polyethylene (LDPE) and aluminum. Previous studies have reported the individual enhancement effect of the LDPE and cellulose fibers as bitumen modifiers. However, the research on the feasibility and effectiveness of using WLP as a bitumen modifier is limited. In this study, WLP manually collected from milk and fruit juice packages was processed into particles. The performance of bitumen modified by WLP was characterized and compared with that of base bitumen by conventional and rheological tests. The testing results showed that the addition of WLP increases the softening point and peak force, and decreases the penetration and ductility. Meanwhile, a higher WLP content led to a greater modification index, higher failure temperature, lower non-recoverable creep compliance and lower stress sensitivity. Furthermore, the stabilizing effects of LDPE and the sufficient crosslinking of the cellulose fibers are beneficial to the fatigue life of bitumen.

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Exploring directional energy conversion behavior of electromagnetic-based multifunctional asphalt pavement

Cited by 23 publications

References 24 publications

Neural Network Aided Homogenization Approach for Predicting Effective Thermal Conductivity of Composite Construction Materials

Neural Network Aided Homogenization Approach for Predicting Effective Thermal Conductivity of Composite Construction Materials

Three-Dimensional Meso-Structure-Based Model for Evaluating the Complex Permittivity of Asphalt Concrete

Feasibility investigation of using waste laminated packaging as bitumen performance enhancer

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