System boundary expansion in road pavement life cycle assessment

The application of Life Cycle Assessment to road pavements has been evolving over the last years, receiving a growing interest from the academic sector and from governmental and non-governmental institutions and organizations. However, the complete introduction of this approach in the asset management decision making process is not possible yet, due to an incomplete understanding of the impact of some relevant phases and components of a road pavement LCA, such as the work zone impact during maintenance events and the rolling resistance in the use phase. The first one refers to the additional congestion and traffic delay in an area of a trafficway interested by construction and maintenance activities. The road pavement rolling resistance is the energy loss due the pavement-vehicle interaction (PVI) and it is affected by the tire properties and by the pavement surface condition. The introduction of the Carbon Footprint/LCA approach in highway asset management, as a decision making tool, requires a deep understanding of all the phases of the life cycle of a road and of the impact of the selected methods and assumed parameters to model them. This thesis provides a review of the main models used to describe the influence on the vehicle fuel consumption - in terms of CO2 emissions - of the work zone during maintenance activities and the rolling resistance during the use phase and investigates the potential impact of these models and of some input parameters on the LCA results. The study was applied on two different UK road sections, characterized by different traffic volume, maintenance activities and design. The impact of the work zone during maintenance activities was explored, comparing the CO2 emissions obtained from two generally applied models in Life Cycle Assessment studies (LCAs) with different level of sophistication: the microsimulation model Aimsun and the macroscopic analytical/deterministic method described in the Highway Capacity Manual (HCM), which is based on the Demand-Capacity (D-C) model and the queue theory. In these models, the traffic volume, the Traffic Management (TM) strategy, the Emission Factor (EF) model and the network boundary are input variables that potentially generate uncertainty in the results and their impact was investigated. The impact of the rolling resistance, due to the pavement surface properties, was assessed with two different models provided in literature and a sensitivity test was performed on some significant input variables, namely the pavement deterioration, the traffic growth and the selected EF. The results obtained in this research have shown that the models adopted to estimate the vehicle emissions for both the work zone impact and the rolling resistance components have a significant influence on the LCA results. Therefore, the selection of the model to assess the impact of these components need to be accurate and appropriate. To assess the work zone impact during maintenance events, the selection of the traffic and emission models should be based on the study objectives and on the available resources. The assessment of the impact of the rolling resistance on the vehicle emissions requires the development of models to estimate the deterioration rate of the pavement surface properties over time and models to link them to the rolling resistance energy loss and to the vehicle emissions. Although currently there are few models available in literature, they are affected by site specific elements and are not suitable for all geographical locations. In the UK, there is currently a lack of general pavement deterioration models able to predict the change of unevenness and texture depth over time and the relationship between them and the rolling resistance and the fuel consumption. This must be corrected before pavement LCA studies can be extended to the use phase. The selected model is not the only source of uncertainty in the assessment of these components. In fact, the analysis of the work zone impact and of the rolling resistance requires several methodological assumptions that, as shown in this study, can have a relevant impact on the results, generating a high level of uncertainty. The results obtained from the work zone impact analysis are sensitive to all the input variables taken into account in this study: the traffic growth, the TM strategy adopted, the EF model and the extent of the road network assumed to be impacted by the work zone. For the rolling resistance, if the deterioration rate of the pavement surface properties is a significantly sensitive parameter, the traffic growth and the EF/fuel efficiency predictions, combined to predict future vehicle emissions, have a relatively small effect because they cancel out to a large extent. However, changes in predicted future traffic levels or EF could change this result and should be kept under review. These research outcomes highlight the importance of incorporating uncertainty into pavement LCA. The reliability and accuracy of an LCA is affected by the reliability of the methodologies and models adopted. LCA results should not be presented as ’single figure’ absolute values, but rather considering a range of values to reflect the uncertainties and variability that lie behind them

System boundary expansion in road pavement life cycle assessment

The application of Life Cycle Assessment to road pavements has been evolving over the last years, receiving a growing interest from the academic sector and from governmental and non-governmental institutions and organizations. However, the complete introduction of this approach in the asset management decision making process is not possible yet, due to an incomplete understanding of the impact of some relevant phases and components of a road pavement LCA, such as the work zone impact during maintenance events and the rolling resistance in the use phase. The first one refers to the additional congestion and traffic delay in an area of a trafficway interested by construction and maintenance activities. The road pavement rolling resistance is the energy loss due the pavement-vehicle interaction (PVI) and it is affected by the tire properties and by the pavement surface condition. The introduction of the Carbon Footprint/LCA approach in highway asset management, as a decision making tool, requires a deep understanding of all the phases of the life cycle of a road and of the impact of the selected methods and assumed parameters to model them. This thesis provides a review of the main models used to describe the influence on the vehicle fuel consumption - in terms of CO2 emissions - of the work zone during maintenance activities and the rolling resistance during the use phase and investigates the potential impact of these models and of some input parameters on the LCA results. The study was applied on two different UK road sections, characterized by different traffic volume, maintenance activities and design. The impact of the work zone during maintenance activities was explored, comparing the CO2 emissions obtained from two generally applied models in Life Cycle Assessment studies (LCAs) with different level of sophistication: the microsimulation model Aimsun and the macroscopic analytical/deterministic method described in the Highway Capacity Manual (HCM), which is based on the Demand-Capacity (D-C) model and the queue theory. In these models, the traffic volume, the Traffic Management (TM) strategy, the Emission Factor (EF) model and the network boundary are input variables that potentially generate uncertainty in the results and their impact was investigated. The impact of the rolling resistance, due to the pavement surface properties, was assessed with two different models provided in literature and a sensitivity test was performed on some significant input variables, namely the pavement deterioration, the traffic growth and the selected EF. The results obtained in this research have shown that the models adopted to estimate the vehicle emissions for both the work zone impact and the rolling resistance components have a significant influence on the LCA results. Therefore, the selection of the model to assess the impact of these components need to be accurate and appropriate. To assess the work zone impact during maintenance events, the selection of the traffic and emission models should be based on the study objectives and on the available resources. The assessment of the impact of the rolling resistance on the vehicle emissions requires the development of models to estimate the deterioration rate of the pavement surface properties over time and models to link them to the rolling resistance energy loss and to the vehicle emissions. Although currently there are few models available in literature, they are affected by site specific elements and are not suitable for all geographical locations. In the UK, there is currently a lack of general pavement deterioration models able to predict the change of unevenness and texture depth over time and the relationship between them and the rolling resistance and the fuel consumption. This must be corrected before pavement LCA studies can be extended to the use phase. The selected model is not the only source of uncertainty in the assessment of these components. In fact, the analysis of the work zone impact and of the rolling resistance requires several methodological assumptions that, as shown in this study, can have a relevant impact on the results, generating a high level of uncertainty. The results obtained from the work zone impact analysis are sensitive to all the input variables taken into account in this study: the traffic growth, the TM strategy adopted, the EF model and the extent of the road network assumed to be impacted by the work zone. For the rolling resistance, if the deterioration rate of the pavement surface properties is a significantly sensitive parameter, the traffic growth and the EF/fuel efficiency predictions, combined to predict future vehicle emissions, have a relatively small effect because they cancel out to a large extent. However, changes in predicted future traffic levels or EF could change this result and should be kept under review. These research outcomes highlight the importance of incorporating uncertainty into pavement LCA. The reliability and accuracy of an LCA is affected by the reliability of the methodologies and models adopted. LCA results should not be presented as ’single figure’ absolute values, but rather considering a range of values to reflect the uncertainties and variability that lie behind them

Route level analysis of road pavement surface condition and truck fleet fuel consumption

Experimental studies have estimated the impact of road surface conditions on vehicle fuel consumption to be up to 5% (Beuving et al., 2004). Similar results have been published by Zaabar and Chatti (2010). However, this was established testing a limited number of vehicles under carefully controlled conditions including, for example, steady speed or coast down and no gradient, amongst others. This paper describes a new “Big Data” approach to validate these estimates at truck fleet and route level, for a motorway in the UK. Modern trucks are fitted with many sensors, used to inform truck fleet managers about vehicle operation including fuel consumption. The same measurements together with data regarding pavement conditions can be used to assess the impact of road surface conditions on fuel economy. They are field data collected for thousands of trucks every day, year on year, across the entire network in the UK. This paper describes the data analysis developed and the initial results on the impact of road surface condition on fuel consumption for journeys of 157 trucks over 42.6km of motorway, over a time period of one year. Validation of the relationship between road pavement surface condition and vehicle fuel consumption will increase confidence in results of LCA analyses including the use phase

Rolling resistance contribution to a road pavement life cycle carbon footprint analysis

Purpose Although the impact of road pavement surface condition on rolling resistance has been included in the life cycle assessment (LCA) framework of several studies in the last years, there is still a high level of uncertainty concerning the methodological assumptions and the parameters that can affect the results. In order to adopt pavement carbon footprint/LCA as a decision-making tool, it is necessary to explore the impact of the chosen methods and assumptions on the LCA results. Methods This paper provides a review of the main models describing the impact of the pavement surface properties on vehicle fuel consumption and analyses the influence of the methodological assumptions related to the rolling resistance on the LCA results. It compares the CO2 emissions, calculated with two different rolling resistance models existing in literature, and performs a sensitivity test on some specific input variables (pavement deterioration rate, traffic growth, and emission factors/fuel efficiency improvement). Results and discussion The model used to calculate the impact of the pavement surface condition on fuel consumption significantly affects the LCA results. The pavement deterioration rate influences the calculation in both models, while traffic growth and fuel efficiency improvement have a limited impact on the vehicle CO2 emissions resulting from the pavement condition contribution to rolling resistance. Conclusions and recommendations Existing models linking pavement condition to rolling resistance and hence vehicle emissions are not broadly applicable to the use phase of road pavement LCA and further research is necessary before a widely-used methodology can be defined. The methods of modelling and the methodological assumptions need to be transparent in the analysis of the impact of the pavement surface condition on fuel consumption, in order to be interpreted by decision makers and implemented in an LCA framework. This will be necessary before product category rules (PCR) for pavement LCA can be extended to include the use phase

Route level analysis of road pavement surface condition and truck fleet fuel consumption

Experimental studies have estimated the impact of road surface conditions on vehicle fuel consumption to be up to 5% (Beuving et al., 2004). Similar results have been published by Zaabar and Chatti (2010). However, this was established testing a limited number of vehicles under carefully controlled conditions including, for example, steady speed or coast down and no gradient, amongst others. This paper describes a new “Big Data” approach to validate these estimates at truck fleet and route level, for a motorway in the UK. Modern trucks are fitted with many sensors, used to inform truck fleet managers about vehicle operation including fuel consumption. The same measurements together with data regarding pavement conditions can be used to assess the impact of road surface conditions on fuel economy. They are field data collected for thousands of trucks every day, year on year, across the entire network in the UK. This paper describes the data analysis developed and the initial results on the impact of road surface condition on fuel consumption for journeys of 157 trucks over 42.6km of motorway, over a time period of one year. Validation of the relationship between road pavement surface condition and vehicle fuel consumption will increase confidence in results of LCA analyses including the use phase

Universidad pública y desarrollo : innovación, inclusión y democratización del conocimiento

La recuperación de la centralidad del Estado en su capacidad regulatoria, distributiva y en la provisión de bienes y servicios públicos ha interpelado al sistema científico y a la Universidad Pública. La Universidad se ha visto obligada a repensar las históricas misiones de docencia, investigación y extensión en el marco de una agenda de reformas políticas y sociales orientadas a la generación de un proceso de desarrollo con equidad. Se trata de un escenario novedoso, en el cual se ha revitalizado el debate sobre la relación entre tres actores disociados durante el auge de las políticas neoliberales: el sistema científico, el Estado y el aparato productivo. En el campo de las ciencias sociales, este escenario obliga a revisar las culturas académicas e institucionales instaladas, y repensar la necesidad de construir conocimiento socialmente relevante y de ampliar su impacto. Visibilizar, multiplicar y promover prácticas de vinculación desde nuestros campos disciplinares ha significado uno de los grandes desafíos de los últimos años. En ese marco, la Facultad de Ciencias Sociales de la Universidad de Buenos Aires se ha propuesto trabajar fuertemente para generar aportes activos y concretos a las transformaciones estructurales que se están produciendo tanto en la sociedad como con el Estado. La vinculación con FEDUBA, sindicato de docentes universitarios de la UBA, para el diseño y organización del Programa en Investigación, Transferencia y Desarrollo en la Universidad Pública constituye una de estas iniciativas