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EFFECTIVE LAYER TEMPERATURE PREDICTION MODEL AND TEMPERATURE CORRECTION VIA FALLING WEIGHT DEFLECTOMETER DEFLECTIONS (WITH DISCUSSION AND CLOSURE).
| 题名： | EFFECTIVE LAYER TEMPERATURE PREDICTION MODEL AND TEMPERATURE CORRECTION VIA FALLING WEIGHT DEFLECTOMETER DEFLECTIONS (WITH DISCUSSION AND CLOSURE). |
| 作者： | Park-D-Y; Buch-N; Chatti-K; Hermansson-A (Discusser); Knutsson-S (Discusser) |
| 关键词： | Asphalt-layer-temperature; Backcalculation-; Climatic-regions; Deflection-; Falling-weight-deflectometers; Flexible-pavements; Mathematical-models; Michigan-; Modulus-of-resilience; Seasons-; Temperature-; Temperature-gradients; Test-sections; Validation- |
| 摘要： | Surface deflections and backcalculated layer moduli of flexible pavements are significantly affected by the temperature of the asphalt concrete (AC) layer. The correction of these deflections and moduli to a reference temperature requires the determination of an effective temperature of the AC layer. In light of this, a new temperature prediction model for determination of the AC temperature on the basis of a database approach is presented, and temperature correction factors for AC modulus are developed. Temperature datum points (n=317) and deflection profiles (n=656) were collected from the six in-service test sites in Michigan. Temperature datum points (n=197) from three of the test sites were used to develop the temperature prediction model, and data from the remaining sites were used for validation. The temperature prediction model developed has an R-squared value greater than 90% and an F-statistic significantly greater than 1.0. For further validation of the temperature prediction model, temperature datum points (n=18,444) from seven Seasonal Monitoring Program sites (Colorado, Connecticut, Georgia, Nebraska, Minnesota, South Dakota, and Texas) were obtained from DATAPAVE, version 2.0 (Long-Term Pavement Performance program database). The validation results suggested that the model could be adapted to all seasons and other climatic and geographic regions. The major improvements over existing models are that (a) the model does not require temperatures for the previous 5 days, (b) it takes into account temperature gradients due to diurnal heating and cooling cycles, and (c) it needs fewer parameters than other published models. The effect of temperature prediction error on the performance prediction was also investigated. |
| 总页数： | Transportation Research Record. 2001. (1764) pp97-111 (20 Fig., 3 Tab., 12 Ref.) |
| 报告类型： | 科技报告 |
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