Improved Sensitivity of Base Layer on the Performance of Rigid Pavement
This work addresses the problem of inaccurate pavement performance predictions for civil engineers and infrastructure planners, though it is incremental as it builds on existing models with specific improvements.
The study tackled the low sensitivity of AASHTOWare Pavement ME design to base and subgrade properties in rigid pavements by developing an Artificial Neural Network (ANN) model to predict modified modulus of subgrade reaction (k-value) and adopting improved resilient modulus (MR) models, resulting in higher sensitivity to water content in base layers and the ability to predict critical responses at any partially bonded conditions, unlike the existing model limited to two extremes.
The performance of rigid pavement is greatly affected by the properties of base/subbase as well as subgrade layer. However, the performance predicted by the AASHTOWare Pavement ME design shows low sensitivity to the properties of base and subgrade layers. To improve the sensitivity and better reflect the influence of unbound layers a new set of improved models i.e., resilient modulus (MR) and modulus of subgrade reaction (k-value) are adopted in this study. An Artificial Neural Network (ANN) model is developed to predict the modified k-value based on finite element (FE) analysis. The training and validation datasets in the ANN model consist of 27000 simulation cases with different combinations of pavement layer thickness, layer modulus and slab-base interface bond ratio. To examine the sensitivity of modified MR and k-values on pavement response, eight pavement sections data are collected from the Long-Term Pavement performance (LTPP) database and modeled by using the FE software ISLAB2000. The computational results indicate that the modified MR values have higher sensitivity to water content in base layer on critical stress and deflection response of rigid pavements compared to the results using the Pavement ME design model. It is also observed that the k-values using ANN model has the capability of predicting critical pavement response at any partially bonded conditions whereas the Pavement ME design model can only calculate at two extreme bonding conditions (i.e., fully bonding and no bonding).