Laboratory study on temperature loss behavior of asphalt mixture during transportation
The temperature loss of asphalt mixtures during transportation is crucial to pavement construction quality. Although previous studies have investigated temperature loss through field measurements and numerical simulations, comprehensive analysis under controlled conditions remains limited. To addres...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525006941 |
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| Summary: | The temperature loss of asphalt mixtures during transportation is crucial to pavement construction quality. Although previous studies have investigated temperature loss through field measurements and numerical simulations, comprehensive analysis under controlled conditions remains limited. To address this gap, an indoor simulation platform was developed to examine the temperature loss behavior of asphalt mixtures during transportation. A four-factor, three-level orthogonal experiment was conducted to assess the effects of environmental temperature, driving speed, manufacturing temperature, and thermal insulation conditions on temperature loss. Furthermore, a backpropagation (BP) neural network model was employed to predict temperature variations during transportation. The results show that temperature loss exhibits a nonlinear trend, with the most rapid heat dissipation occurring within the first 80 min. Notable spatial variations in temperature loss were observed within the carriage box, with the front and rear sections experiencing the greatest heat loss, whereas the middle section retained heat more effectively. Vertically, the top and bottom layers lost heat faster than the middle layer. Additionally, the dominant factor influencing heat dissipation varied by mixture position within the carriage box. Except for the center region, most locations exhibited a strong linear correlation in temperature variations. The BP neural network model achieved a prediction accuracy of R² = 82 %, confirming its effectiveness in forecasting temperature loss behavior in asphalt mixtures during transportation. |
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| ISSN: | 2214-5095 |