Building A Pavement Sustainability Index For Highway Infrastructures: Case Study Of Flexible Pavements

Established literature reviews indicate that analytical methods have been developed to design different components of pavement performances due to pavement conditions and sustainability. Some of these models were mostly based on definitions, operations, structural and functional research oriented analysis; and they have contributed substantially to the society. However, the integration or correlations of sustainability indices with pavement condition indices were not practically fully explored; and in practice, the triple bottom-line sustainability dimensions (environmental, economic and social) play a major role in planning for both construction and maintenance of these infrastructures.

This research work aims to quantitatively develop a pavement sustainability index (PSTI), correlate it with pavement performance evaluation indices; and qualitatively integrate the triple bottom-line sustainability dimensions with pavement condition indices. These pavement condition indices are accounted for using existing mathematical models to analyze them. In order for this task to be implemented, an empirical multi-linear regression analysis (MLRA) model that will analyze and correlate these indicators is developed using the following possible four categories of pavement performance evaluation indicators for flexible pavements as independent variables for each section.

These categories include: (1) pavement serviceability, (2) surface distress, (3) skid resistance, and (4) structural capacity and they are further divided into indices: International Roughness Index (IRI); Present Serviceability Index (PSI); Present Serviceability Rating (PSR); Rut Index (RTI); Cracking Index (CKI); Skid Resistance Index (SRI); and Structural Adequacy Index (SAI). The combined regression results for the five case studies indicate that the analysis yielded a strong coefficient of determination R of higher value of 96% which is highly correlated.

This means that 96% of the sum of squares in dependable variables (PSTI) can be associated with the variations in the seven independent variables. According to the t-Stat, the most significant factor for PSTI is IRI (which negatively impact PSTI with a value of -18.2372 and coefficient value of -0.001295253), followed by PSI and PSR (with positive values); while SAI, RTI, CKI, and SRI are marginal significant factors. Finally, the regression results of the combined case studies for each index demonstrate that PSI (0.0320) and PSR (0.0277) have higher positive coefficient values than the rest seven independent variables.

This means that for each unit increase of PSI and PSR, the PSTI will increase in value, thereby improving the PSTI and providing a more sustainable pavement infrastructure; which explains the significance of the research model.