Estimation and uncertainty analysis of energy consumption and CO2 emission of asphalt pavement maintenance

Abstract Substantial energy consumption and CO2 emission are generated in asphalt pavement maintenance and their quantifications based on individual case are highly contingent on the input data qualities and model parameters, exhibiting high uncertainties. This study established energy consumption and CO2 emission uncertainty assessment statistical methodology to deal with the issue. Two sources of uncertainty, the data quality one and the model parameter one, were identified. The former was captured by converting the input data to probability density function (PDF) using Beta distribution following the definitions of data quality pedigree matrix; the latter was assessed by defining uncertainty factor (UF) to determine the distribution form and parameter. Environmental data of 18 field asphalt pavement maintenance projects were collected, including hot mix asphalt (both base and SBS modified asphalts), hot in-plant recycling (15% reclaimed asphalt pavement), cold in-place recycling and cold in-plant recycling asphalt maintenance plans, covering pavement material production, transportation, mixture preparation and construction phases. The PDFs and statistical parameters (mean, standard deviation and percentiles etc.) were obtained via Monte Carlo simulation for these maintenance measures. This study further developed environmental burden comparative parameter to construct the 95% confidence intervals for the comparisons of different maintenance measures. The methodology proposed in this study captured the uncertainty of energy consumption and CO2 emission calculation results of maintenance activity and allow researchers to evaluate the results in a statistical view.

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