In this paper, a new mechanics-based top-down fatigue cracking analysis framework is presented for asphalt pavements. A new mixture morphology-based set of material sub-models is presented for characterising key mixture properties and their change over time. Predicting the load induced top-down fatigue crack initiation (CI) time by utilising comprehensive mixture properties creates the possibility of optimising the mixture morphology while taking into account its subsequent effect on long-term pavement performance. The new framework was calibrated and subsequently validated against a number of field pavement sections with varying traffic levels that are representative for current practices and which have a wide range in material properties. The framework accounts the change in key mixture properties due to ageing and mixture-healing effect on damage accumulation while determining the overall effect of design inputs on cracking performance. Model calibration and validation were achieved based on the healing potential of the asphalt mixture. It was found out that the CI predictions for all the sections are in general agreement with the observed performance in the field, thus giving credibility for the framework.

The mechanics-based analysis framework predicts top-down fatigue cracking initiation timein asphalt concrete pavements by utilising fracture mechanics and mixture morphology-basedproperty. To reduce the level of complexity involved, traffic data were characterised and incorporatedinto the framework using the equivalent single axle load (ESAL) approach. There isa concern that this kind of simplistic traffic characterisation might result in erroneous performancepredictions and pavement structural designs. This paper integrates axle load spectraand other traffic characterisation parameters into the mechanics-based analysis framework andstudies the impact these traffic characterisation parameters have on predicted fatigue crackingperformance. The traffic characterisation inputs studied are traffic growth rate, axle load spectra,lateral wheel wander and volume adjustment factors. For this purpose, a traffic integrationapproach which incorporates Monte Carlo simulation and representative traffic characterisationinputs was developed. The significance of these traffic characterisation parameters wasestablished by evaluating a number of field pavement sections. It is evident from the resultsthat all the traffic characterisation parameters except truck wheel wander have been observedto have significant influence on predicted top-down fatigue cracking performance.

This paper presents a new reliability-based code calibration in load resistance factor design (LRFD) format for the mechanics-based fatigue cracking analysis framework. The pavement design procedure incorporates an appropriate failure criterion and design period. Moreover, it provides uniform performance at the desired level of reliability while considering the inherent variabilities and uncertainties involved. A number of field pavement sections with well documented performance histories and high quality field and laboratory data were used for this purpose. Moreover, a reliability computation methodology that incorporates a central composite design (CCD) response surface approach (RS) is proposed. Appropriate statistical characterization of the dominant design parameters was performed considering its key role in reliability analysis. A first order reliability method (FORM) was used to compute pavement performance reliability and to establish the partial safety factors of the design procedure. Illustrative examples based on the developed LRFD procedure have demonstrated clearly its capacity of delivering designs with uniform reliability.

Pavement design is a probabilistic process as it involves many random variables.Through the incorporation of reliability, pavement design methods consider inputparameters variabilities effect on pavement performance. Load and Resistance Factor Design(LRFD) is a typical example of reliability-based design procedure. In LRFD, a set of partialsafety factors are developed by modelling input parameters variabilities with representativeaverage conditions. This paper evaluates the impact input parameters variabilities have onestimated target reliability in the case when actual variabilities are different from assumedconditions. This was achieved by evaluating a field pavement section with various combinationsof input parameters variabilities. The optimized section for a given target reliability wasachieved through the mechanics-based LRFD procedure and the actual reliabilities of thevarious cases were obtained using a methodology that uses response surface approach andfirst order reliability method (FORM). The results have shown that the level of input parametersvariabilities used affect the target reliability considerably.