01 - TRUSS ITN, ESR13 - “Using Truck Sensors for Road Pavement Performance Investigation”

Hello, it’s me… (Adele, 2015… Sorry, I was joking)

In this second post, I’m going to introduce my project.

It is one of the 14 TRUSS (Training in Reducing Uncertainty in Structural Safety) projects funded by the European Union for the 2015/16. TRUSS is a Marie Skłodowska-Curie Innovative Training Network (ITN) funded by the European Union under the Horizon 2020 program and it is structured into taught modules combined with original and impactful research. Each project is supported also by secondments into external companies that will give to the candidates significant exposure and insights to research and innovation in both academia and industry.

Therefore, currently I’m working as an Early Stage Researcher and PhD student in Civil Engineering based at the NTEC (Nottingham Transportation Engineering Centre) at the University of Nottingham. My supervisors are Dr Tony Parry and Dr Luis Canhoto Neves and, as I previously mentioned in the first post I wrote, the title of my project is ESR13 “Using Truck Sensors for Road Pavement Performance Investigation”.

It has been known for many years that road surface evenness influences vehicle fuel consumption, with smoother roads significantly improving fuel efficiency (usual estimates of up to 5% compared to ‘rough’ roads). More recently, it has been claimed that pavement stiffness also influences fuel economy (with varying estimates of <1 to 5%). However, this has been established in experiments using a limited number of instrumented test vehicles under carefully controlled conditions (e.g. steady speed, no gradient etc.) and for short test sections. What is less clear is the significance of these impacts on vehicle fleet fuel economy under real driving conditions at network level. This has recently gained more focus in the highway authority and research community as carbon footprinting of road maintenance plans has gained importance. Modern trucks are fitted with many sensors as standard and used to inform decisions on maintenance and driver training requirements in large fleets. However, many of the information produced might be used also in the measurement of how road condition influences performance in terms of vehicle operation. In particular, two questions have not been sufficiently well answered: “what is the influence of road pavement roughness on truck fleet fuel consumption?” and “what is the influence of road pavement stiffness on truck fleet fuel consumption?”. The project will provide answers to these questions to help prioritize pavement maintenance and design decisions with respect to user and environmental impacts.

The project will be conducted in co-operation with Microlise Ltd, who collect and interpret location-referenced sensor data for truck fleet managers and with TRL Ltd, experts in road condition measurement, will provide data, including evenness, stiffness and geometry, for the UK road network.
In Microlise, I will have: (a) training in truck sensor data collection, in particular with reference to fuel consumption and associated factors such as dynamic axle loads and location referencing, and (b) gain exposure to truck manufacturers and truck fleet operators and understand their priorities with respect to fleet fuel consumption and control. In TRL, I will have: (a) training in road condition measurements of various factors including evenness, stiffness and geometry, and (b) exposure to highway authorities and understand their priorities in the area of road maintenance planning.
At the end of these trainings, I will be able to build the database of condition measurements required for the research study. The correlation between truck fleet fuel consumption and road conditions for a large number of vehicles will be analysed across the UK network.

Summarizing, the first goal of the project is to assess the impact of road condition on truck fuel consumption based on truck sensor measurements. Results will be verified by controlled measurements using a sub-set of the most modern trucks (with the most sensitive fuel sensors) on selected sections of the network and include additional measurements, such as wind speed, air temperature, etc. In particular, it is expected to develop tools able to assess impact of road conditions on truck fleet fuel consumption including influence of road pavement roughness and stiffness, extending the system boundary of lifecycle carbon footprint of road maintenance strategies to include truck fleet fuel consumption, and truck fleet fuel consumption inventory for road maintenance life cycle analysis. Results will improve road maintenance strategies leading to a reduction of costs and life cycle carbon footprint impact.

So, that’s it for the moment.
Thank you for your attention and stay tuned for the next post!


Research Gate: Federico Perrotta


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