AN AGGREGATE DAMAGE APPROACH FOR HIGHWAY PAVEMENT PERFORMANCE ANALYSIS (MAINTENANCE, EVALUATION, COST ALLOCATION)

Highway pavement maintenance and its management are areas of serious concern and study today. A topic that deserves a great deal of research effort is the development of the relationship between pavement routine maintenance and pavement performance. This study was undertaken to investigate the feasibility of employing an aggregate performance/damage approach in the analysis of highway pavement performance. A methodology was developed based upon the serviceability performance concept first conceived at the AASHO Road Test completed in the early 1960s. Several new concepts were introduced in the process of developing the aggregate performance approach methodology. A new parameter, PSI-ESAL loss, was defined as an aggregate representation of pavement damage. A concept of zero-maintenance performance curve was adopted to account for the effects of routine maintenance work. This concept permitted an estimation of the actual total damage of a pavement. A quantitative measure for levels of pavement routine maintenance was proposed. This quantitative measure, coupled with the PSI-ESAL loss representation of pavement damage, facilitated the establishment of a relationship between pavement performance and level of routine maintenance. On the basis of the above theoretical framework, a procedure for allocating pavement maintenance and rehabilitation costs in a highway cost allocation analysis was developed. This procedure was successfully implemented in a full-scale highway cost allocation study at state level. A further development of the performance analysis concept involved a quantitative assessment of the effects of routine maintenance on pavement. A maintenance effectiveness index was defined which provided a means for quantitative evaluation of pavement maintenance work. The aggregate performance approach proposed is simple in concept and easy to understand. The amount of data required is much less than that required by a disaggregate distress function approach. The amount of analysis effort involved is also reduced considerably

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