CALIBRATION OF REGRESSION-BASED MODELS FOR PREDICTION OF TEMPERATURE PROFILE OF ASPHALT LAYERS USING LTPP DATA

For analysis, design, and rehabilitation purposes of flexible pavements, the temperature profile asphalt layers should be determined. The predictive models as an alternative to in-situ measurements, are rapid easy methods determine layer at various depths. These developed based on limited field data. Hence, there is a need for developing new prediction in climatic regions. In this study, data was retrieved from Long-Term Pavement Performance (LTPP) database. information 33 pavement test sections 16 states United States employed calibrating models. Using prepared data, predicted utilizing four regression-based models, including Ramadhan Wahhab, Hassan et al., Albayati Alani, Park al. Existing were calibrated, predict layer, developed. evaluation validation newly showed excellent correlation between measured values. Results show ability predicting with very good precision (R2 = 0.94) low bias.