One of the most common types of pavement on the national highway system is composite asphalt concrete (AC) over portland cement concrete (PCC). With a large percentage of PCC pavements either approaching or at the end of their design lives, AC overlay of PCC pavements has become one of the most common methods of rehabilitation. This has resulted in several thousand kilometers of composite AC/PCC pavements. As the level of heavy truck traffic loading continues to increase on a majority of pavements, it is likely that the total length of composite pavements in the nation will continue to increase considerably in the coming years. A common type of distress that occurs on these composite pavements is reflective cracking. This occurs when the joints or cracks in the underlying PCC pavement reflect through the AC overlay. A performance model that can be used to predict accurately the amount of reflective cracks in composite AC/PCC pavements has enormous potential uses. The development of a mechanistic-based performance model for predicting the amount of reflective cracks in composite AC/PCC pavements is described. Data from the Long-Term Pavement Performance database were used to develop the model. Using the principles of fracture mechanics, it is illustrated that a mechanistic-based model can be developed that closely models the real-life behavior of composite pavements and predicts the amount of reflective cracks. Because of the mechanistic nature of the model, it is particularly effective for performance prediction for design checks and pavement management. Also, since the model can take into account the relative damaging effect of the actual axle loads in any traffic distribution, it has great potential for application in cost allocation.
A mechanistic roughness performance model that takes into account vehicle dynamics was developed for use in flexible pavement design and evaluation. The model was developed in the form of a relation between roughness and number of load repetitions, axle load, and asphalt layer thickness. The model is completely mechanistic and uses vehicle dynamics analysis to estimate the dynamic force profile and finite element structural analysis to estimate the change of pavement surface roughness for each load repetition. The model makes use of the fact that pavement roughness changes the magnitude of the vehicle dynamic forces applied on the pavement and that the dynamic forces change the road roughness. The developed mechanistic roughness performance model can be used to estimate the 80-kN (18-kip) equivalent single-axle load for mixed traffic. The model can also be used to design pavement so that it will last for a certain number of load repetitions before reaching a predetermined roughness level. Performance-based specifications can be developed using the methodology presented in this study. The model has been calibrated and verified with field data elsewhere.
The objective of this study was to identify and quantify design and construction features most important to joint faulting of joint plain concrete pavements. With data obtained from the Long-Term Pavement Performance (LTPP) database, an analysis approach that combined pavement engineering expertise and modern data analysis techniques was to develop guidelines for improved design and construction of Portland cement concrete (PCC) pavement. The approach included typical preliminary analyses, but emphasis was placed on using a series of multivariate data analysis techniques. Discriminant analysis was used to develop models that classify individual pavement into performance groups developed by cluster analysis, which was used to partition the pavements into three distinct groups representing good, normal, and poor performance. These models can be used to classify and evaluate additional or new pavements performance throughout the pavement's design life. To quantify the levels of the key design and construction features that contribute to performance, the classification and regression tree procedure was used to develop tree-based models for performance measure. The analysis approach described was used to develop the guideline on the key design and construction features that can be used by designers to decrease joint faulting of jointed plain concrete pavements (JPCPs).Key words: faulting, Long-Term Pavement Performance (LTPP), jointed plain concrete pavement (JPCP), cluster analysis, discriminant analysis, classification and regression tree (CART) analysis.
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