You are being redirected because this document is part of your ASTM Compass® subscription.
    This document is part of your ASTM Compass® subscription.

    Volume 47, Issue 3 (May 2019)

    Special Issue Paper

    Asphalt Pavement Performance Analysis Using “Big Data” Computing Approaches

    (Received 21 December 2017; accepted 29 August 2018)

    Published Online: 2018

    CODEN: JTEVAB

      Format Pages Price  
    PDF (1.27 MB) 13 $25   ADD TO CART

    Cite this document

    X Add email address send
    X
      .RIS For RefWorks, EndNote, ProCite, Reference Manager, Zoteo, and many others.   .DOCX For Microsoft Word



    Abstract

    Rapid deterioration of pavements depends on both objective and subjective factors. This study investigates the relationships among six parameters—climate, traffic volume, distress, friction, longitudinal profile, and transverse profile—related to asphalt pavement performance. For this purpose, LTPP InfoPave, the largest pavement performance database provided by the Long-Term Pavement Performance (LTPP) program, is used. The LTPP InfoPave data exhibit the characteristics of five V’s (volume, velocity, variety, value, veracity) of “big data.” The correlation analyses are conducted on the basis of the LTPP program’s Specific Pavement Study-3 experiments. Analysis results for the data from 1987 to 2005 reveal that the average block cracking area and average raveling area are highly correlated with a correlation coefficient of maximum 0.85. The average mean roughness index (MRI) is correlated with the average block cracking area and the average raveling area with correlation coefficients 0.59 and 0.64, respectively. The results also indicate that smoothness and surface distress have relatively lower correlation; these results are consistent with the previously published results. In addition, for the traffic volume data from 1990 to 2014, the MRI is found to be higher at higher traffic volume, indicating rougher pavement, but this difference is statistically insignificant (correlation coefficient, 0.423). At the beginning and end of the section, traffic volume is highly correlated with friction, having correlation coefficients of 0.711 and 0.646, respectively. This study serves as a reference for the use of big data to gain an in-depth understanding of pavement performance, and engineers can continue to explore the application of the LTPP program using big data computing approaches.

    Author Information:

    Chang, Jia-Ruey
    Graduate Institute of Architecture and Sustainable Planning, National Ilan University, Yilan City, Yilan County


    Stock #: JTE20170772

    ISSN:0090-3973

    DOI: 10.1520/JTE20170772

    Author
    Title Asphalt Pavement Performance Analysis Using “Big Data” Computing Approaches
    Symposium ,
    Committee D04