|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4912601||1428748||2018||12 صفحه PDF||سفارش دهید||دانلود رایگان|
- Provides models that use only nominal inputs to make reliable property estimates during design phase.
- Presents generalized regression framework for developing asphalt property prediction models.
- Model is verified through statistical comparisons and comparisons with other predictive models.
- Application of proposed model for pavement performance prediction is demonstrated.
Dynamic modulus (|Eâ|) and phase angle (Î´) are necessary for determining the response of asphalt mixtures to in-service traffic and thermal loadings. While a number of |Eâ| and Î´ predictive models have been developed, many of them require lab measured properties (e.g. binder complex modulus). The majority of previous work has focused only on prediction of |Eâ|, limited models exist for prediction of Î´. This research utilized generalized regression modelling of lab measurements (from 81 asphalt mixtures) to develop and verify prediction models for |Eâ| and Î´ using only nominal asphalt mix properties that are readily available during the initial mixture design and specification process.
Journal: Construction and Building Materials - Volume 158, 15 January 2018, Pages 308-319