| 1. |
- Rahman, Mohammad Shafiqur, 1978-, et al.
(author)
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A model for the permanent deformation behavior of the unbound layers of pavements
- 2021
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In: Proceedings Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields. - London : CRC Press. - 9781003222880 ; , s. 277-287
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Conference paper (other academic/artistic)abstract
- This article presents a model for the permanent deformation (PD) behavior of unbound granular materials (UGMs) used in the base and subbase layers of pavement struc- tures. The model was developed based on multistage (MS) repeated load triaxial (RLT) test- ing. This is essentially a modified version of a previously developed model to better suit to field conditions in a simple and effective manner. The model was calibrated for eight com- monly used UGMs using MSRLT tests with a range of moisture contents. For validation, the calibrated models were used to predict the PD behavior of three of the UGMs in MSRLT tests with stress levels and moisture contents different from those used during the calibrations. This model showed better quality of fit when compared with another widely used PD model. The model was further tested successfully for field conditions by capturing the PD behavior of an instrumented pavement test section in a controlled environment using a heavy vehicle simu- lator (HVS) based accelerated pavement testing (APT). Inputs for calibrating the model were based on the readings from the instrumentations. The parameters of the model were adjusted to match the measured data with the predictions. Based on these results for various design conditions, some ranges of values of the material parameters of the model were suggested.
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| 2. |
- Zhu, Jiqing, 1987-, et al.
(author)
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Experimental analysis and predictive modelling of linear viscoelastic response of asphalt mixture under dynamic shear loading
- 2022
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In: Construction and Building Materials. - : Elsevier. - 0950-0618 .- 1879-0526. ; 328
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Journal article (peer-reviewed)abstract
- The use of predictive models can facilitate the inclusion of shear parameters in asphalt mixture evaluation and design processes. Unlike more extensively studied tension–compression models, the currently existing shear model, the Hirsch model, has unrealistic constants, particularly for the prediction of phase angle. Aiming at an improved predictive model in shear, this study employs a simple shear apparatus to experimentally analyse the linear viscoelastic properties of asphalt mixtures for road paving. Master curves were constructed and compared between different asphalt mixtures. Additionally, the test results were also analysed in the Black space and the Cole-Cole space. The dynamic shear response of asphalt mixtures was thereafter modelled on the basis of the Hirsch model. As the original model for phase angle prediction was found to be unrealistic, a particular focus in this study was put on identifying realistic empirical relationships for predicting the phase angle of asphalt mixtures in shear. More reliable shear test results of asphalt mixtures were used to calibrate the model, and extra test data were utilized to validate the calibrated model. It is indicated that the predictive model after calibration could deliver results of greatly improved accuracy, especially at the high-frequency and low-frequency ends. The analysis and modelling also leads to realistic empirical relationships for predicting the phase angle of asphalt mixtures in shear. The experimental verification confirms the good prediction accuracy of the calibrated model and proposed empirical relationships.
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| 3. |
- Dinegdae, Yared H., 1981-
(author)
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Pavement inputs variability characterization : state of the art literature review
- 2022
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Reports (other academic/artistic)abstract
- The pavement analysis and design process, even if it is considered in most pavement performance evaluation tools as deterministic, is actually probabilistic. The probabilistic nature arises from uncertainties that originate from different sources. Even if all the sources contribute to the overall variance, each source impact can differ based on the failure mode and design approach under consideration. Moreover, some of these sources can be mitigated at a reasonable cost while those that are associated with model bias and inherent variability of future traffic and environmental inputs are difficult to quantify. This report aims to study and establish the variability associated with pavement inputs. This is achieved through a literature survey that encompasses a large body of literature among others published articles, reports, design guides and specifications. As it will not be economically feasible to model all the design inputs and parameters as random variable, a sensitivity survey is required to identify those inputs with significant influence. It is evident from the literature survey that pavement inputs variability can be described by a variety of distributions with a wide range of coefficient of variation (CV). Variability levels were shown to be dependent to a larger extent on data quality and testing method. Furthermore, factors such as functional class, material type, and design thickness were observed in creating their own variability cluster. For future variability characterization, a methodology that improves data quality while keeping the testing cost at a reasonable level should be pursued. In addition, emphasis should be given to factors that define and govern level of variability such as functional class as this would allow the data to be analysed in a manner that facilitate immediate utilization
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