Modeling Fuel Use, Emissions and Mass of On-Road Construction Equipment through Monitoring Field Operations

Construction industry is considered as one of the largest contributors to fuel consumption and greenhouse gases (GHGs) emissions globally. Fuel use and emissions of construction equipment are normally estimated through simulation or conducting dynamometer tests in the laboratory which may not represent the real-world situations. In such models, fuel use and emissions rate are mainly estimated at macro scale, while the effect of operational conditions cannot be measured. There is also a lack of quantitative operational level fuel use and emissions reduction schemes in the construction industry despite the potential of significant cost saving by applying such strategies. This thesis presents an integrated data monitoring framework including instrumentation and experimentation procedures to monitor operations of construction equipment. It develops operational level models to estimate fuel use and emissions rate of on-road construction equipment through investigating the effect of operational and environmental variables. Using the automated data sensing system, this study also develops a comprehensive model to predict the weight of on-road construction vehicles and their carried payload as crucial parameter affecting fuel use and emissions rate. Three types of devices, portable emission measurement system (PEMS), GPS-aided inertial navigation system (GPS-INS) and engine data logger were employed to collect emissions rates, operational parameters and engine data of on-road construction vehicles. Models are developed through performing statistical regression and artificial neural network (ANN) analysis on the filtered data. The proposed models consider the engine specifications, operational factors and environmental parameters for estimating fuel use, emissions rate and weight of the vehicles. Based on the developed models, this study designs different schemes to improve fuel efficiency of construction equipment. As the main operational level strategy, optimal driving speed is proposed over other operational and environmental variables. Other factors, such as traffic conditions, effect of idling and equipment stop on fuel use and emissions production of equipment are also investigated. At equipment level, this thesis evaluates the impact of different engine tiers on fuel use and emissions rate through applying the developed models. It is found that adoption of high-tier engines leads to considerable savings on the operation costs of equipment