Automated Measurement of Heavy Equipment Greenhouse Gas Emission: The Case of Road/Bridge Construction and Maintenance

Road construction and maintenance projects are major contributors to greenhouse gas (GHG) emissions such as carbon dioxide (CO2). This is mainly because of the extensive use of heavy duty diesel (HDD) construction equipment (Hajji and Lewis, 2013), as well as large-scale earthworks and earthmoving operations (Kenley and Harfield, 2011). The need for construction equipment idle time estimation for specific purposes (such as cost estimation, fuel use and emission estimation, data-driven modeling and simulation) has been highlighted in a number of recent studies (Vorster and de la Garza 1990; McCahill and Bernold 1993; Frey et al. 2008; Lewis et al. 2011; Akhavian and Behzadan 2013). However, there are very limited studies targeting the prospect of automated idling detection and idle time estimation for sustainability analysis. The goal of this project is to use sensor data and machine learning methods to evaluate the fuel use and greenhouse gas (GHG) emission of road and bridge construction and maintenance equipment.

Principal Investigator: 

Reza Akhavian, Ph.D.

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