An Enhanced Pavement Temperature Prediction Model Incorporating Improved Initial and Boundary Conditions

Abstract

Pavement temperature affects pavement performance, distress development, and the urban heat island effect. Accurate pavement temperature prediction models support pavement design, maintenance, and mitigation of the urban heat island effect. However, existing numerical models often oversimplify surface boundary and initial temperature conditions, producing notable errors. This study developed an enhanced prediction model through: (1) refined initial temperature conditions, (2) improved surface heat flux calculations, and (3) a new method for estimating the effective rainfall mass in rainfall-induced heat flux. The model was validated using typical pavement structures and temperature data from the Long-Term Pavement Performance (LTPP) program. The results indicated that initial conditions had a lasting impact on the predictions, especially at deeper layers. Significant relationships were observed between differences in surface heat flux and both the prediction start time and error dissipation. The proposed preconditioned-initial condition improved the accuracy for both hot and cold days. The new rainfall-induced heat flux calculation method effectively supplemented surface heat flux estimation during rainfall events, reducing temperature prediction discrepancies. Overall, the enhanced temperature prediction approach developed in this study demonstrated broad applicability across various regions and seasons and achieved higher accuracy than existing methods, even when the impact of initial errors was not significant.

Department(s)

Civil, Architectural and Environmental Engineering

Keywords and Phrases

Asphalt pavement; concrete pavement; initial temperature condition; pavement temperature prediction; surface heat flux

International Standard Serial Number (ISSN)

1477-268X; 1029-8436

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Taylor and Francis Group; Taylor and Francis, All rights reserved.

Publication Date

01 Jan 2026

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