Incorporating Environmental Sustainability into Pavement Design and Management
Bibliographic record
Abstract
Increasing interest in pavement sustainability has led to the development of sustainability assessment tools and a plethora of potential indicators. Well-designed indicators are capable of quantitatively tracking and informing environmental performance of pavement infrastructure systems. Here, we review literature on sustainability assessment, focusing on work relevant to transportation and civil infrastructure. This research considers the context of Ontario, its values, conditions and practices for sustainability assessment in pavement management and proposes a framework, set of indicators and associated measures to assess environmental sustainability of pavements throughout their lifecycle. The framework includes seven factors addressing environmental protection, and four reflecting natural resource management. A combination of input-based, output-based, and outcome-based indicators are provided for each factor. The key environmental performance indicators are selected to be comprehensive, consistent, measurable, context-specific, and informed by values, influence and purpose of Ontario pavement managers. Providing measures of these indicators, where possible, required some novel applications and analysis of existing methods and data for Ontario pavements. \nIt was observed that climate change continues to be an important environmental issue. Widespread impacts of climate change are expected to affect Canadian infrastructure systems because of changes in precipitation, temperature, winds, sea level rise, and other extreme events. A major cause of climate change, greenhouse gas (GHG) emissions, are increasing because of human influence. Responding to climate change requires action to mitigate anthropogenic greenhouse gas (GHG) emissions and increase climate-resiliency of assets and operations. This study provided a comprehensive list of GHG mitigation measures and an improved selection framework for implementing such measures across roadway design, construction, and maintenance processes. Four approaches to manage GHG emissions to investigate strategies to manage GHG emissions during all highway management phases: Design, Construction, Rehabilitation and Reconstruction and Maintenance. A review of current and emerging best practices and strategies from other jurisdictions for addressing GHG emissions was done, along with surveys and interviews were prepared to enhance this information and yield insights into factors that influence implementation of mitigation measures. Survey and interviews were designed to capture information related to cost of implementing measures, changes to internal resourcing at the agencies, industry’s response and evidence of social resistance; as well as strategies to manage these influencing factors. Cost was identified as a major factor for implementing innovations to manage GHG emissions. \nTransportation energy consumption and the associated emission from fuel combustion have adverse effects on air quality and healthy living in Canada. Improvement in engine performance and emission control technologies are often considered to improve vehicle fuel economy and reduce the consequent pollution, but past studies have focused on vehicle emissions. This research shows that road surface condition management can be used as a short-term strategy to reduce environmental impact even as the population of Canada continues to grow, and vehicle numbers increase,, especially in densely populated urban areas. A model to predict the impact of road surface condition measured by International Roughness index (IRI) (m/km) on vehicle fuel consumption and associated emissions was developed. Additionally, the relationship between excess damage cost from rougher roads (IRI >1m/km) was estimated for cars and trucks travelling on Ontario Road. When models are implemented into a network-level analysis of Ontario Roads, results show that the vehicles using on Ontario Road network released an excess 2000 tonnes of carbon dioxide (CO2) emission due to the road condition (IRI >1m/km) in 2014. Also, the environmental damage cost of emissions (2010CAD) of five atmospherics pollutants was found to be about $2.6 Million in 2014. A project level sustainability assessment framework was proposed to incorporate economic, environmental and technical aspects of sustainability into pavement management. Future study can expand the framework to network-level and incorporate the road condition models developed in this research program and documented in this thesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".