07/12/2021
Marie Skłodowska-Curie Actions

MSCA-COFUND-CLEAR-Doc - PhD Position #CD21-38 "Surveillance of pavement structural health using RFID traffic/load sensor data"

This job offer has expired


  • ORGANISATION/COMPANY
    Université Gustave Eiffel
  • RESEARCH FIELD
    EngineeringElectronic engineering
    EngineeringSimulation engineering
    EngineeringSurveying
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    21/03/2022 17:00 - Europe/Brussels
  • LOCATION
    France › Marne-La-Vallée
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35
  • OFFER STARTING DATE
    01/10/2022
  • EU RESEARCH FRAMEWORK PROGRAMME
    H2020 / Marie Skłodowska-Curie Actions COFUND
  • MARIE CURIE GRANT AGREEMENT NUMBER
    101034248

OFFER DESCRIPTION

The first part of the thesis is devoted to the development of a battery-less RFID (Radio-frequency Identification) traffic/load sensor to be buried in the road surface (asphalt), capable of supporting heavy loads. This sensor provides information on the traffic load (especially trucks which are the main source of road degradation) using accelerometers or geophones sensitive to vertical deformations of the pavement. Temperature variations must also be measured because temperature affects the amplitude of structural vibrations. The sensor data is then transmitted wireless via a low-power RFID technology to a mobile reader on the road or fixed on the roadside. This information is sent back without battery by collecting the energy of the load's oscillations through a piezoelectric of reduced volume to extend the sensor's life. This wireless transmission of information contrasts with the usual methods in which cables buried in the pavement are susceptible to damage during construction. For this part, the stakes are threefold:

• to minimize the amount of energy required to power the radio, sensor and signal processing parts of the device on the one hand in order to optimize the radio range of the device

• to take into account in rigorous electromagnetic modelling the impact of the asphalt on the propagation losses and the detuning of the antenna integrated in the sensor.

• to produce a device (radio + sensors + energy recovery + electronics) of sufficiently small volume so as not to modify the behaviour of the pavement locally.

This part of the work will be carried out at the ESYCOM Laboratory in UGE/Marne-La-Vallée. In-situ tests will be carried out on real roads using the resources and expertise of the LAMES team (Laboratory for monitoring, modelling and experimentation of transport infrastructures) located at the UGE/Nantes. The idea will be to distribute the sensors with a sufficient local density in an asphalt mix of variable quality according to the zone considered.

These local deformation measurements will be exploited by the Department of Civil and Environmental Engineering of the Politecnico di Milano in order to estimate the structural state of the road and its evolution in the second part of the thesis. Machine learning algorithms are nowadays available to exploit the huge amount of data continuously acquired through sensor networks, and to solve inverse problems. Challenging engineering issues, like structural health monitoring (SHM) or load identification, are currently linked to Big Data, consisting of structural vibration recordings shaped as multivariate time series. The aforementioned algorithms should therefore allow an effective dimensionality reduction, to retain the informative content of data, and infer correlations within and across time series. Within this framework, AutoEncoders may employ inception modules and residual learning, and a latent representation specifically adapted to tackle identification tasks.

Besides issues linked to the architecture of the neural network, the setting of the network hyperparameters and the fine tuning of the network weights, the quality of the dataset upon which the SMH algorithms provides the classification or inference task is a crux. This is basically linked to the fact that deceptive information is linked to load and environmental variability. The possibility offered by hybrid model-based and data-driven approaches or by the so-called physic-informed neural networks can be exploited to bring into the SHM monitoring additional information collected by the sensors developed by Université G. Eiffel used to measure the traffic loads.

The research proposed will be carried out within the International Associated Laboratory (LIA) SensIN-CT which brings together Université G. Eiffel and Politecnico Milano. It will be supervised by a team common to the two universities.

More Information

Benefits

  • High-quality doctoral training rewarded by a PhD degree, delivered by Université Gustave Eiffel
  • Access to cutting-edge infrastructures for research & innovation.
  • Appointment for a period of 36 months based on a salary of 2 700 € (gross salary per month).
  • Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year.
  • International mobility will be mandatory
  • An international environment supported by the adherence to the European Charter & Code.
  • Access to dedicated CLEAR-Doc trainings with a strong interdisciplinary focus, together with a Career development Plan.

Eligibility criteria

  • At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree. At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
  • At the time of the deadline, applicants must be in the first four years (full-time equivalent research experience) of their research career (career breaks excluded) and not yet been awarded a doctoral degree. Career breaks refer to periods of time where the candidate was not active in research, regardless of his/her employment status (sick leave, maternity leave etc). Short stays such as holidays and/or compulsory national service are not taken into account.
  • At the time of the deadline, applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately prior to the call deadline.
  • Applicants must be available to start the programme on schedule (around 1st October 2022).

Selection process

Additional comments

  • The First step before applying is contacting the PhD supervisor. You will not be able to apply without an acceptation letter from the PhD supervisor.
  • Please contact the PhD supervisor for any additional detail on job offer.
  • There are no restrictions concerning the age, gender or nationality of the candidates. Applicants with career breaks or variations in the chronological sequence of their career, with mobility experience or with interdisciplinary background or private sector experience are welcome to apply.
  • Support service is available during every step of the application process by email: clear-doc@univ-eiffel.fr

Web site for additional job details

Offer Requirements

  • REQUIRED LANGUAGES
    ENGLISH: Good

Skills/Qualifications

  • At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree.
  • At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.

Specific Requirements

International mobility : a 6-month secondment at Politecnico di Milano. For more information, contact the PhD thesis supervisor.

Work location(s)
1 position(s) available at
Université Gustave Eiffel
France
Ile de France
Marne-La-Vallée
77454
5, Boulevard Descartes

EURAXESS offer ID: 716466

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