Optimum statistical characterization of axle load spectra based on load-associated pavement damage

Traffic is indeed one of the most critical inputs for pavement design; traditionally the one that is associated with the highest uncertainty. In the most comprehensive mechanistic–empirical (M–E) design approaches, traffic is accounted for by axle load distribution instead of equivalent single axle load (ESAL) as in the traditional empirical approach. Research has already been conducted concerning the statistical characteristics of axle load distribution, however, with focus on the goodness of fit of the data. Little of the past research directly accounted for the traffic load-associated pavement damage. To address this particular issue, this study develops a comprehensive statistical methodology that includes not only improved fitted axle distribution functions, but also sound statistics representing load-associated pavement damage. Mixed lognormal distributions are employed to fit the observed axle load spectra. Two fundamental advantages of the fitted functions are: (1) both the physical and statistical meanings of the load spectra are properly accounted for and (2) the load spectra data are well captured and the fitted distribution can be statistically evaluated. In particular, the load-associated pavement damage based on axle load distributions is investigated through the concept of moment statistics. The moment order (or power) is generalized to both integer and non-integer conditions, an important advantage of the lognormal distribution. Different power values are examined concerning varying load-associated pavement distresses or responses. In the case study presented, the results indicate that R 2 is not an adequate statistic to evaluate fitted functions from the perspective of using load spectra in pavement design. It is, therefore, recommended that assessment of fitted distribution be based on moment statistics. Of particular note, it is demonstrated that, due to relatively larger fit errors, higher moment orders should be adopted to evaluate load spectra fit functions in the context of pavement design. To address this issue, optimized parameters are estimated by jointly considering axle load distribution characteristics and load-associated pavement damage. Consequently, both efficient and precise traffic load spectra inputs for pavement design are established.