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MSCA-COFUND-CLEAR-Doc-PhD Position #CD22-22: Machine vision for construction sites

13/10/2022

Job Information

Organisation/Company
Université Gustave Eiffel
Department
LIGM
Research Field
Computer science
Computer science » Informatics
Engineering
Researcher Profile
First Stage Researcher (R1)
Country
France
Application Deadline
Type of Contract
Temporary
Job Status
Full-time
Hours Per Week
35
Is the job funded through the EU Research Framework Programme?
H2020 / Marie Skłodowska-Curie Actions COFUND
Marie Curie Grant Agreement Number
101034248
Is the Job related to staff position within a Research Infrastructure?
No

Offer Description

Context, positioning and objectives :

With productivity not increasing since the 1950's, the construction sector is the last one to start its digital transformation, which includes the use of lean management tools but also inevitably robotization. In this context, 3D concrete printing represents a real opportunity to transform the way we build. Numerous experimental applications are very visible, but developments concerning printing materials, construction strategies and the process itself are still necessary to make the technology truly operational, certifiable and competitive. The optimization of this construction process as well as the control and certification of the performance of the structures manufactured using this technique remains a major challenge and a very current subject of development. The variation of a process parameter can eventually give rise to deviations or defects that can result in the rejection of the printed part, with economic and environmental consequences. Predicting, defining, quantifying or avoiding these defects, guaranteeing acceptable limits and justifying rejection criteria, remains a fundamental task to be carried out to allow a better diffusion of this technology. It requires a detailed understanding of the impact of material parameters (modulus, threshold, viscosity, appearance, etc.) and of the process (flow rate, admixture, nature and speed of flow, etc.) on the phenomena observed (cracking, local and global buckling, settling, creep, drying shrinkage).

Work program :

Some works propose inspection techniques during the printing of mortars, using photogrammetry, 3D scanner or profilometer [1]. Excessive deformations of layers or imperfect interfaces are also inspected using deep neural networks [2], [3] and works of ESIEE in collaboration with Navier are in progress, measuring the curvature and thickness of the printed layer, orientation deviation, textural anomaly detection of cracks [4], [5]. The thesis proposes to continue this work in order to link these data with the properties of the mortar in the fresh and hardened state.

The mechanical properties of the mortar mix, yield strength, viscosity, modulus of elasticity, and the so-called printability and constructability properties, are now well known [6] but the conventional rheological metrology devices (rheometers, Abrams cone, slump tests) are not adapted to in-situ measurement and must be invented. Some works in this direction exist [7] and the Navier laboratory has proposed the slug test [8], which allows to deduce the yield strength of the mortar from the mass of the drops that form at the printing nose. The thesis plans to develop this test and to investigate other solutions (penetrometers, squeeze test...) by comparing various imaging and metrology with modelling or numerical simulation. To reduce the analysis time, it is also planned to investigate the use of physics-informed neural network models [9], [10].

Finally, mechanical tests on printed elements and simulations will allow to relate overall structural performance to measured geometric imperfections, cracking patterns and other undesirable phenomena (e.g. shrinkage).

This will allow to establish recommendations and a framework for the certification of the parts produced, which is a major issue and much awaited by the profession.

References

[1] R. A. Buswell, W. R. Leal de Silva, S. Z. Jones, et J. Dirrenberger, « 3D printing using concrete extrusion: A roadmap for research », Cem. Concr. Res., vol. 112, p. 37‑49, oct. 2018, https://doi.org/10.1016/j.cemconres.2018.05.006.

[2] O. Davtalab, A. Kazemian, X. Yuan, et B. Khoshnevis, « Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection », J. Intell. Manuf., oct. 2020, https://doi.org/10.1007/s10845-020-01684-w.

[3] A. Kazemian, X. Yuan, O. Davtalab, et B. Khoshnevis, « Computer vision for real-time extrusion quality monitoring and control in robotic construction », Autom. Constr., vol. 101, p. 92‑98, may 2019, https://doi.org/10.1016/j.autcon.2019.01.022.

[4] P. Dokládal, « Statistical Threshold Selection for Path Openings to Detect Cracks », in Mathematical Morphology and Its Applications to Signal and Image Processing, Cham, 2017, p. 369‑380. https://doi.org/10.1007/978-3-319-57240-6_30.

[5] R. Rill-García, P. Dokladal and E. Dokladalova, « Pixel-accurate road crack detection in presence of.pdf , 2021. on line: https://hal.archives-ouvertes.fr/hal-03400373

[6] N. Roussel, « Rheological requirements for printable concretes », Cem. Concr. Res., vol. 112, p. 76‑85, oct. 2018, https://doi.org/10.1016/j.cemconres.2018.04.005.

[7] R. J. M. Wolfs, F. P. Bos, E. C. F. van Strien, et T. A. M. Salet, « A Real-Time Height Measurement and Feedback System for 3D Concrete Printing », in High Tech Concrete: Where Technology and Engineering Meet, Cham, 2018, p. 2474‑2483. https://doi.org/10.1007/978-3-319-59471-2_282.

[8] N. Ducoulombier et al., « The “Slugs-test” for extrusion-based additive manufacturing: Protocol, analysis and practical limits », Cem. Concr. Compos., vol. 121, p. 104074, aug. 2021, https://doi.org/10.1016/j.cemconcomp.2021.104074.

[9] G. E. Karniadakis, I. G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, et L. Yang, « Physics-informed machine learning », Nat. Rev. Phys., vol. 3, no 6, p. 422‑440, june 2021, https://doi.org/10.1038/s42254-021-00314-5.

[10] N. Thuerey, P. Holl, M. Mueller, P. Schnell, F. Trost, et K. Um, « Physics-based Deep Learning », ArXiv210905237 Phys., sept. 2021, http://arxiv.org/abs/2109.05237

Requirements

Research Field
Computer science
Education Level
Bachelor Degree or equivalent
Research Field
Engineering
Education Level
Bachelor Degree or equivalent
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.
Languages
FRENCH
Level
Good
Languages
ENGLISH
Level
Good

Additional 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

Applicants must fulfil the following 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 fulfil the transnational mobility rule: incoming applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 previous years
  • One application per call per year is allowe
  • Applicants must be available full-time to start the programme on schedule (November 1st 2023)
  • Application rules are enforced by the French doctoral system which specifies a standard duration of 3 years for a full-time PhD together with the MSCA standards and the OTM-R European rules as follows
  • Citizens of any nationality may apply to the programme
  • There is no age limit
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.
  • International mobility : yes - please contact your PhD supervisor to have further detail.
  • 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
Website for additional job details

Work Location(s)

Number of offers available
1
Company/Institute
Université Gustave Eiffel
Country
France
City
Bouguenais
Postal Code
44340
Street
Allée des Ponts et Chaussées
Geofield

Contact

City
Marne-la-Vallée
Website
Street
5, Boulevard Descartes
Postal Code
77454
E-Mail
eva.dokladalova@univ-eiffel.fr