Horizon 2020 Marie Skłodowska-Curie Innovative Training Network

Federico Perrotta

BSc, MSc
Home/Federico Perrotta
Federico Perrotta 2018-08-18T21:45:49+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


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

“Using truck sensors for road pavement performance investigation” is a research project within TRUSS, an innovative training network funded from the EU under the Horizon 2020 programme. The project aims at assessing the impact of the condition of the road pavement unevenness and macrotexture, on the fuel consumption of trucks to reduce uncertainty in the framework of life-cycle assessment of road pavements. In the past, several studies claimed that a road pavement in poor condition can affect the fuel consumption of road vehicles. However, these conclusions are based just on tests performed on a selection of road segments using a few vehicles and this may not be representative of real conditions. That leaves uncertainty in the topic and it does not allow road mangers to review the current road maintenance strategies that could otherwise help in reducing costs and greenhouse gas emissions from the road transport industry. The project investigated an alternative approach that considers large quantities of data from standard sensors installed on trucks combined with information in the database of road agencies that includes measurements of the conditions of the road network. In particular, using advanced regression techniques, a fuel consumption model that can take into consideration these effects has been developed. The paper presents a summary of the findings of the project, it highlights implications for road asset management and the road maintenance strategies and discusses advantages and limitations of the approach used, pointing out possible improvements and future work.

There is a 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
This paper presents the application of three Machine Learning techniques to fuel consumption modelling of articulated trucks for a large dataset. In particular, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) models have been developed for the purpose and their performance compared. Fleet managers use telematic data to monitor the performance of their fleets and take decisions regarding maintenance of the vehicles and training of their drivers. The data, which include fuel consumption, are collected by standard sensors (SAE J1939) for modern vehicles. Data regarding the characteristics of the road come from the Highways Agency Pavement Management System (HAPMS) of Highways England, the manager of the strategic road network in the UK. Together, these data can be used to develop a new fuel consumption model, which may help fleet managers in reviewing the existing vehicle routing decisions, based on road geometry. The model would also be useful for road managers to better understand the fuel consumption of road vehicles and the influence of road geometry. Ten-fold cross-validation has been performed to train the SVM, RF, and ANN models. Results of the study shows the feasibility of using telematic data together with the information in HAPMS for the purpose of modelling fuel consumption. The study also shows that although all the three methods make it possible to develop models with good precision, the RF slightly outperforms SVM and ANN giving higher R-squared, and lower error. -> Link to full text in repository

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