New-generation wide-base tire (NG-WBT) is known for improving fuel economy and at the same time for potentially causing a greater damage to pavement. No study has been conducted to evaluate the net environmental saving of the combined system of pavements and NG-WBT. This study adopted a holistic approach (life cycle assessment [LCA] and life cycle costing [LCC]) to quantitatively evaluate the environmental and economic impact of using NG-WBT.
MethodsThe net effect of different levels of market penetration of NG-WBT on energy consumption, global warming potential (GWP), and cost based on the fatigue cracking and rutting performance of two different asphalt concrete (AC) pavement structures was evaluated. The performance of pavements was determined based on pavement design lives; pavement surface characteristics, and pavement critical strain responses obtained from the artificial neural network (ANN) based on finite element (FE) simulations were used to calculate design lives of pavements. Based on the calculated design lives, life cycle inventory (LCI) and cost databases, and rolling resistance (RR) models previously developed by the University of Illinois at Urbana-Champaign (UIUC) were used to calculate the environmental and economic impact of the combined system.
Results and discussionThe fuel economy improvement using NG-WBT is 1.5% per axle. Scenario-based case studies were conducted. Considering 0% NG-WBT market penetration (or 100% standard dual tire assembly [DTA]) as a baseline, scenario 1 assumed the same fatigue and rutting potential between NG-WBT and DTA; therefore, the only difference came from fuel economy improvement of using NG-WBT. In scenario 2, pavement fatigue cracking potential determined the pavement design life; both thick and thin AC overlay sections experienced positive net environmental savings, but mixed net economic savings. In scenario 3, pavement rutting potential determined the pavement design life; the thick AC overlay section experienced positive net environmental savings, but mixed net economic savings. The thin section experienced negative net environmental and economic savings.
ConclusionsThe outcomes of scenario-based case studies indicated that NG-WBT can result in significant savings in life cycle energy consumption and cost, and GWP; however, these benefits were sensitive to the method used to determine the pavement performance; especially, a small change in pavement strain can result in significant change in pavement life. In addition, the effect of fuel price/economy improvement, discount rate, and International Roughness Index (IRI) threshold values was studied in the sensitivity analyses.
相似文献Fuel economy and emissions of heavy-duty trucks greatly vary based on vehicular/environmental conditions. Large-scale infrastructure construction projects require a large amount of material/equipment transportation. Single-parameter generic hauling models may not be the best option for an accurate estimation of hauling contribution in life cycle assessment (LCA) involving construction projects; therefore, more precise data and parameterized models are required to represent this contribution. This paper discusses key environmental/operational variables and their impact on transportation of materials and equipment; a variable-impact transportation (VIT) model accounting for these variables was developed to predict environmental impacts of hauling.
MethodsThe VIT model in the form of multi-nonlinear regression equations was developed based on simulations using the U.S. Environmental Protection Agency (EPA)’s Motor Vehicle Emission Simulator (MOVES) to compute all the impact categories in EPA’s TRACI 2.1 and energy consumption of transportation. Considering actual driving cycles of hauling trucks recorded during a pavement rehabilitation project, the corresponding environmental impacts were calculated, and sensitivity analyses were performed. In addition, an LCA case study based on historical pavement reconstruction projects in Illinois was conducted to analyze the contribution of transportation and variability of its impacts during the pavements’ life cycle.
Results and discussionThe importance of vehicle driving cycles was realized from simulation results. The case study results showed that considering driving cycles using the VIT model could increase the contribution of hauling in total life cycle Global Warming Potential (GWP) and total life cycle GWP itself by 2–4 and 3–5%, respectively. In addition to GWP, ranges of other hauling-related impact categories including Smog, Ozone Depletion, Acidification, and Primary Energy Demand from fuel were presented based on the case study. Ozone Depletion ranged from 9 to 45%, and Smog ranged from 11 to 48% of the total relevant life cycle impacts. The GWP contribution of hauling in pavement LCA ranged between 5 and 32%. The results indicate that the contribution of hauling transportation can be significant in pavement LCA.
ConclusionsFor large-scale roadway infrastructure construction projects that need a massive amount of material transportation, high fidelity models and data should be used especially for comparative LCAs that can be used as part of decision making between alternatives. The VIT model provides a simple analytical platform to include the critical vehicular/operational variables without any dependence on an external software; the model can also be incorporated in those studies where some of the transportation activity data are available.
相似文献Objective uncertainty quantification (UQ) of a product life-cycle assessment (LCA) is a critical step for decision-making. Environmental impacts can be measured directly or by using models. Underlying mathematical functions describe a model that approximate the environmental impacts during various LCA stages. In this study, three possible uncertainty sources of a mathematical model, i.e., input variability, model parameter (differentiate from input in this study), and model-form uncertainties, were investigated. A simple and easy to implement method is proposed to quantify each source.
MethodsVarious data analytics methods were used to conduct a thorough model uncertainty analysis; (1) Interval analysis was used for input uncertainty quantification. A direct sampling using Monte Carlo (MC) simulation was used for interval analysis, and results were compared to that of indirect nonlinear optimization as an alternative approach. A machine learning surrogate model was developed to perform direct MC sampling as well as indirect nonlinear optimization. (2) A Bayesian inference was adopted to quantify parameter uncertainty. (3) A recently introduced model correction method based on orthogonal polynomial basis functions was used to evaluate the model-form uncertainty. The methods are applied to a pavement LCA to propagate uncertainties throughout an energy and global warming potential (GWP) estimation model; a case of a pavement section in Chicago metropolitan area was used.
Results and discussionResults indicate that each uncertainty source contributes to the overall energy and GWP output of the LCA. Input uncertainty was shown to have significant impact on overall GWP output; for the example case study, GWP interval was around 50%. Parameter uncertainty results showed that an assumption of ±?10% uniform variation in the model parameter priors resulted in 28% variation in the GWP output. Model-form uncertainty had the lowest impact (less than 10% variation in the GWP). This is because the original energy model is relatively accurate in estimating the energy. However, sensitivity of the model-form uncertainty showed that even up to 180% variation in the results can be achieved due to lower original model accuracies.
ConclusionsInvestigating each uncertainty source of the model indicated the importance of the accurate characterization, propagation, and quantification of uncertainty. The outcome of this study proposed independent and relatively easy to implement methods that provide robust grounds for objective model uncertainty analysis for LCA applications. Assumptions on inputs, parameter distributions, and model form need to be justified. Input uncertainty plays a key role in overall pavement LCA output. The proposed model correction method as well as interval analysis were relatively easy to implement. Research is still needed to develop a more generic and simplified MCMC simulation procedure that is fast to implement.
相似文献