Assessment of construction smoothness specification pay factor limits using artificial neural network.

摘要

Currently, Indiana Department of Transportation (INDOT) is using the California Profilograph as the standard measuring device in its construction smoothness specifications. The output derived from the profilograph is called Profile Index (PI). PI represents the total accumulated deviations of the profilograph output traces beyond a tolerance zone (blanking band). At present, INDOT is using 0.2-inch blanking band to evaluate the profile traces, which has raised some concerns because some small unpleasant surface irregularities are covered by the blanking band. This study developed a rational method for interpreting profilograph traces using the 0.0-inch blanking band (zero tolerance) method and established the corresponding pavement smoothness specifications. The development of the preliminary PI0.0 smoothness specification was performed by converting the existing PI0.2 specification to the PI0.0 specification. Several Profile Index conversion models were used to perform the conversion.; In addition, current incentive/disincentive policies specified in the smoothness specification are based on the subjective engineering judgment. To which extent they can really reflect the long-term benefits of a smoother pavement by providing a longer service life is still unknown. Thus, the roughness progression model was developed using the Artificial Neural Network methodology to determine the effect of various initial specification smoothness limits on the future smoothness progression and the pavement service life. Finally, using the developed model, the preliminary converted specification was modified to account for the long term benefit of the pavement and thus justified the incentive/disincentive policies in the specification.