Stochastic decision modeling for sustainable pavement designs
Abbreviated Journal Title
Int. J. Life Cycle Assess.
Economic input-output analysis; Life-cycle sustainability assessment; Multi-criteria decision making; Pavements; Triple bottom line; LIFE-CYCLE ASSESSMENT; WARM MIX ASPHALT; ENVIRONMENTAL IMPACTS; INTERNATIONAL-TRADE; REINFORCED-CONCRETE; RISK-ASSESSMENT; ENERGY USE; CONSTRUCTION; PERFORMANCE; MANAGEMENT; Engineering, Environmental; Environmental Sciences
In the USA, several studies have been conducted to analyze the energy consumption and atmospheric emissions of Warm-mix Asphalt (WMA) pavements. However, the direct and indirect environmental, economic, and social impacts, termed as Triple-Bottom-Line (TBL), were not addressed sufficiently. Hence, the aim of this study is to develop TBL-oriented sustainability assessment model to evaluate the environmental and socio-economic impacts of pavements constructed with different types of WMA mixtures and compare them to a conventional Hot-mix Asphalt (HMA). The types of WMA technologies investigated in this research include AsphaminA (R) WMA, Evotherm (TM) WMA, and SasobitA (R) WMA. To achieve this goal, supply and use tables published by the U.S. Bureau of Economic Analysis were merged with 16 macro-level sustainability metrics. A hybrid TBL-LCA model was built to evaluate the life-cycle sustainability performance of using WMA technologies in construction of asphalt pavements. The impacts on the sustainability were calculated in terms of socio-economic (import, income, gross operating surplus, government tax, work-related injuries, and employment) and environmental (water withdrawal, energy use, carbon footprint, hazardous waste generation, toxic releases into air, and land use). A stochastic compromise programming model was then developed for finding the optimal allocation of different pavement types for the U.S. highways. WMAs did not perform better in terms of environmental impacts compared to HMA. AsphaminA (R) WMA was found to have the highest environmental and socio-economic impacts compared to other pavement types. Material extractions and processing phase had the highest contribution to all environmental impact indicators that shows the importance of cleaner production strategies for pavement materials. Based on stochastic compromised programming results, in a balanced weighting situation, SasobitA (R) WMA had the highest percentage of allocation (61 %); while only socio-economic aspects matter, AsphaminA (R) WMA had the largest share (57 %) among the asphalt pavements. The optimization results also supported the significance of an increased WMA use in the U.S. highways. This research complemented previous LCA studies by evaluating pavements not only from environmental emissions and energy consumption standpoint, but also from socio-economic perspectives. Multi-objective optimization results also provided important insights for decision makers when finding the optimum allocation of pavement alternatives based on different environmental and socio-economic priorities. Consequently, this study aimed to increase awareness of the inherent benefits of economic input-output analysis and multi-criteria decision making through application to emerging sustainable pavement practices.
International Journal of Life Cycle Assessment
"Stochastic decision modeling for sustainable pavement designs" (2014). Faculty Bibliography 2010s. 5602.