Horizon 2020 Marie Skłodowska-Curie Innovative Training Network

Federico Perrotta

BSc, MSc
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Federico Perrotta 2018-06-09T08:04:41+00:00
Early Stage Researcher
University of Nottingham (United Kingdom)

Project 13: Using truck sensors for road pavement performance investigation

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Research Interests:

Civil engineering; Transportation engineering; Materials; Pavement design; Roads construction; Traffic simulation; Transport planning

Biography:

Federico comes from Italy. He studied Civil Engineering and he got the MSc degree on the 19th March 2015 at the University of Parma in Italy. He wrote his thesis working on “Performance Characterization of Grid Reinforced Flexible Pavement through Full Scale Accelerated Pavement Test”. For this project, he worked in internship as Academic Guest at EMPA – Swiss Federal Laboratories for Materials Science and Technology in Zurich in Switzerland. He has written two conference articles from his MSc thesis.

From October 2015, he works as PhD researcher at the University of Nottingham in the TRUSS ITN project ESR13. The project is about “Using truck sensors for road pavement performance investigation” looking for a mathematical relation between fuel consumptions and roads conditions.

Research Outputs:

  • Arraigada, M., Raab, C., Partl, M.N., Perrotta, F. and Tebaldi, G. (2016). “Influence of SAMI on the Performance of Reinforcement Grids”. In Chabot A., Buttlar W., Dave E., Petit C., Tebaldi G. (eds), 8th RILEM International Conference on Mechanisms of Cracking and Debonding in Pavements. RILEM Bookseries, vol 13. Springer, Dordrecht.
  • Arraigada, M., Perrotta, F., Raab, C., Tebaldi, G. and Partl, M.N. (2016). “Use of APT for Validating the Efficiency of Reinforcement Grids in Asphalt Pavements”. In Aguiar-Moya J., Vargas-Nordcbeck A., Leiva-Villacorta F., Loría-Salazar L. (eds), The Roles of Accelerated Pavement Testing in Pavement Sustainability, Springer, Cham.
Presentation in 1st TRUSS Symposium

Publications in TRUSS

There is multitude of models available to assess structural safety based on a set of input parameters. As the degree of complexity of the models increases, the uncertainty of their output tends to decrease. However, more complex models typically require more input parameters, which may contain a higher degree of uncertainty. Therefore, it becomes necessary to find the balance that, for a particular scenario, will reduce the overall uncertainty (model + parameters) in structural safety. The latter is the objective of the Marie Skłodowska-Curie Innovative Training Network titled TRUSS (Training in Reducing Uncertainty in Structural Safety) funded by the EU Horizon 2020 research and innovation programme (http://trussitn.eu). This paper describes how TRUSS addresses uncertainty in: (a) structural reliability of materials such as basalt fiber reinforced polymer, (b) testing techniques in the assessment of concrete strength in buildings, (c) numerical methods in computing the non-linear response of submerged nuclear components subjected to an earthquake, (d) estimation of life of wind turbines, (e) the optimal inspection times and management strategies for ships, (f) characterization of the dynamic response of ship unloaders and (g) the relationship between vehicles fuel consumption and pavement condition.-> Link to full text in repository

 European infrastructure is extensive and well developed, but much of it is also ageing. Replacing this ageing network is costly, disruptive and time consuming – so it is vital that what is already in place is utilised to its full potential. This scenario is made even more perilous by the reduced spending in maintenance caused by the economic downturn of recent years. As such, a management strategy that guarantees maintenance and structural safety with the best use of the resources available is required. This goal is at the heart of the TRUSS Innovative Training Network (ITN). TRUSS addresses a lack of expertise on the management and modernisation of an ageing infrastructure stock that is critical for society to function and prosper. The cost of making repairs once an infrastructure starts to fail is prohibitive, yet there is no easy way to measure how infrastructure deteriorates over time and to assess structural safety.  -> External link to full paper as published online. [DOI] -> External link to full text as published online

Considering data from 260 articulated trucks, with ∼12900 cc Euro 6 engines driving along a motorway in England (M18), the study first shows how different approaches lead to the conclusion that road pavement surface conditions influence fuel consumption of the considered truck fleet. Then, a multiple linear regression for the prediction of fuel consumption was generated. The model shows that evenness and macrotexture can impact the truck fuel consumption by up to 3% and 5%, respectively. It is a significant impact which confirms that, although the available funding for pavement maintenance is limited, the importance of limiting GHG emissions, together with the economic benefits of reducing fuel consumption are reasons to improve road condition (Zaabar & Chatti, 2010). [DOI] -> Link to full text in repository

In Europe, the road network is the most extensive and valuable infrastructure asset. In England, for example, its value has been estimated at around £344 billion and every year the government spends approximately £4 billion on highway maintenance (House of Commons, 2011).

Fuel efficiency depends on a wide range of factors, including vehicle characteristics, road geometry, driving pattern and pavement condition. The latter has been addressed, in the past, by many studies showing that a smoother pavement improves vehicle fuel efficiency. A recent study estimated that road roughness affects around 5% of fuel consumption (Zaabar & Chatti, 2010). However, previous studies were based on experiments using few instrumented vehicles, tested under controlled conditions (e.g. steady speed, no gradient etc.) on selected test sections. For this reason, the impact of pavement condition on vehicle fleet fuel economy, under real driving conditions, at network level still remains to be verified.

A 2% improvement in fuel efficiency would mean that up to about 720 million liters of fuel (~£1 billion) could be saved every year in the UK. It means that maintaining roads in better condition could lead to cost savings and reduction of greenhouse gas emissions.

Modern trucks use many sensors, installed as standard, to measure data on a wide range of parameters including fuel consumption. This data is mostly used to inform fleet managers about maintenance and driver training requirements. In the present work, a ‘Big Data’ approach is used to estimate the impact of road surface conditions on truck fleet fuel economy for many trucks along a motorway in England. Assessing the impact of pavement conditions on fuel consumption at truck fleet and road network level would be useful for road authorities, helping them prioritize maintenance and design decisions. -> Link to full text in repository

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. [DOI] -> Link to full text in repository