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69 CHAPTER 4 SUMMARY AND RECOMMENDATIONS FOR FUTURE RESEARCH 4.1 SUMMARY The interaction between the concrete slab and the base layer is a complex phenomenon affecting long-term performance of concrete pavements. In this study, modeling of the slab-base interaction in the AASHTO M-E procedure for rigid pavement design was evaluated. It was found that the AASHTO M-E accounts for slab-base interaction through adjustments to the subgrade support (modulus of subgrade reaction), flexural stiffness of the pavement, load transfer efficiency of jointed plain pavements, and CRCP crack spacing distributions. Sensitivity studies of the AASHTO M-E performance models and comparisons with LTPP pavement performance data were conducted. Many complications in the performance models were identified, and thus modification of the AASHTO M-E models extended beyond changes to the bond-no bond condition for the slab-base interface. For example, it was found that modeling of JPCP projects with stiff, fully bonded base layers resulted in unrealistically low damage and predicted cracking levels. The study led to the proposed modification of the AASHTO M-E procedure to account for slab-base interaction. ï· The modified JPCP transverse cracking model now accounts for a partial bond condition between the slab and base on a monthly basis. In addition, the slab curling support condition has been improved to describe built-in curl using a modified parameter that accounts for top-down and bottom-up cracking. It is proposed that other parameters, including base thickness and modulus, can influence the built-in curling as well. ï· The modification of the JPCP joint faulting model resulted in new internal default values for the joint load transfer efficiency and the default built-in curl parameter for certain base materials. ï· For CRCP punchout modeling, revised recommendations for the default base friction coefficients were proposed. The alternative performance prediction models developed in this study have been calibrated using LTPP performance observations from the AASHTO M-E calibration database. A companion tool was developed so that the alternative models can be used in conjunction with the AASHTO M-E software. The alternative models are compatible with the AASHTO M-E framework and thus can be incorporated into the AASHTO M-E software in order for the project research results to be fully implemented. 4.2 RECOMMENDATIONS FOR FUTURE RESEARCH The alternative cracking model utilizes a non-dimensional parameter to control the composite action of slab-base interaction. While this non-dimensional parameter was assumed to represent inferred friction between the slab and base (and a simple model was adopted for the deterioration of this inferred friction/bond), there is nothing in the cracking model that restricts this parameter to any physical property. Should future research better quantify the interaction of slab-base interaction, that work can adopt a new parameter into the modified cracking model without issue through the non-dimensional parameter.
70 In addition, the modified cracking model has adopted a simplified model of deterioration at the slab-base interface, which depends only on monthly fluctuations in the temperature at the slab-base interface. Future work may investigate a mechanistic-empirical model quantifying the deterioration and how to better represent this behavior through the dimensionless inferred friction ï*. Traditionally, bond deterioration is assumed to be due only to environmental effects, however this should be re-examined, and if necessary, additional factors (e.g., the effect of axle loading) on deterioration could be included. The developed companion tool permits the evaluation of varied bond deterioration models through user-provided monthly non-dimensional friction information. As noted, the modified model for JPCP transverse cracking adopts new parameters for built-in curl. However, a rational procedure is needed to predict built-in curl based on concrete materials, construction techniques, site conditions at time of construction, etc. Meeting these research needs will not only improve pavement distress prediction, but potentially will lead to recommendations on controlling built-in curl parameters through construction techniques, construction timing, and materials.