PhD: Alternative materials based low-carbon formulation with artificial intelligence

17 Feb 2024
Job Information

Organisation/Company

Université Gustave Eiffel

Department

ESIEE

Research Field

Engineering » Civil engineering
Computer science » Other
Physics » Applied physics

Researcher Profile

First Stage Researcher (R1)

Country

France

Application Deadline

1 Apr 2024 - 00:00 (Europe/Paris)

Type of Contract

Temporary

Job Status

Full-time

Hours Per Week

39

Offer Starting Date

1 Sep 2024

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

The building sector is a key lever for reducing greenhouse gas (GHG) emissions and also for achieving France's climate objectives: achieving carbon neutrality by 2050 and reducing GHG emissions by at least 55% compared to 1990 (all sectors combined) by 2030 [1-2]. Buildings are indeed responsible for 36% of GHG in the European Union (EU) and 28% in France related to their energy consumption estimated at 40% of EU energy [3]. Moreover, construction is a sector that consumes enormous quantities of materials and generates large quantities of waste (concrete, wood, glass, excavated earth, etc.). The waste of this sector is in volume, the most widely produced waste in the EU representing 30% of the waste generated. The recycling and reuse of this waste would reduce both the costs of construction and its environmental impact (raw materials and production of materials [4]). In addition, other sectors such as agriculture can be sources of alternative materials for construction (plant aggregates, biomass ashes, plant fibers). In addition to the storage of biogenic carbon, due to their porosity and permeability, these materials are considered as passive regulators of humidity inside homes and/or thermal and acoustic insulators [5-8]. Their increased use is thus among the orientations of the National Low-Carbon Strategy (SNBC) - France's roadmap to fight against climate change [9].

Combined with new environmental regulations such as (i) the RE2020 environmental regulation requiring, during the design of a building, to assess its carbon footprint in life cycle analysis, including indirect emissions related to the materials used in construction and (ii) the law Anti-Waste for a Circular Economy (AGEC), the use of alternative biobased materials and / or recycling in the construction industry is of growing interest [10-13]. Several types of alternative materials have been researched for incorporation into mortars and concretes as partial or total substitution for aggregates, cement, or as fillers or fibers [14-17]. This work has shown their potential for use in sustainable construction by reducing (i) the extraction of non-renewable natural resources, (ii) landfill, burial and incineration, (iii) GHG emissions, etc.

In the literature, the formulation and optimization of mortars and concretes based on alternative materials for structure and insulation is based on experimental approaches and/or numerical models (linear and non-linear regressions, or based on physico-chemical and/or multi-scale arguments). However, the development by an experimental approach of mortars and concretes answering different specifications is time consuming and costly economically and environmentally. The transposition of the results obtained by this approach to other alternative materials is also limited due, among other things, to the variability of the influencing factors [18-19]. Moreover, the optimization of formulations often implies finding the dosage of constituents that satisfies a compromise between antagonistic performances such as workability, mechanical performance, thermal insulation, durability, cost and environmental impact [19]. At last, existing models may be insufficient [20].

Artificial intelligence models have been successfully used to predict the properties of conventional and alternative material-based concretes (foundry sand, dredged sand and sediments, fibers...) [20-22]. These models are receiving increasing attention in civil engineering due to their predictive capability as well as their adaptability to considerable data without requiring large computational resources [18, 20]. However, the properties predicted simultaneously by these models are limited to a few properties in the fresh state (workability) and in the hardened state (most often mechanical properties) [19]. The development of prediction models allowing a multi-criteria optimization is therefore necessary. This multi-objective formulation approach is adapted to multifunctional materials such as bio-based materials and will facilitate the development of new materials for sustainable construction.

This thesis project thus aims to create a digital tool allowing the design by artificial intelligence of new more ecological, economical building materials for the sustainable city. The objectives will be to :
- to have available database of the performances of conventional mortars and concretes and those based on alternative materials according to the formulation;
- to develop mix-design modelling and artificial intelligence methods (multivariate regression methods, deep learning...) and allowing to predict (1) the performance of the material from a formulation (direct problem) and (2) the formulation according to the specifications (inverse problem).

This collaborative thesis project between teacher-researchers from the Gaspard Monge computer science laboratory (UMR 8049) and researchers from the UMR MCD will reinforce the new synergy between artificial intelligence and materials for sustainable construction at the Gustave Eiffel University. It will be part of the continuation of a M2 internship which will start in February 2024 and will allow (i) to collect and analyze experimental data available in the literature on fresh and hardened performances of conventional mortars and concretes, and based on alternative materials previously selected according to their availability in France and (ii) the prediction of the sought performances according to the mix design by artificial intelligence.

 

Application: Please contact 

SALEM Thouraya  [email protected]

FEN-CHONG Teddy  -  [email protected] BERCHER Jean-François - [email protected]

before applying on the website; with CV, academic transcripts and covering letter.  

References :
[1] Haut conseil pour le climat (2020). RÉNOVER MIEUX : LEÇONS D’EUROPE.
[2] Zangheri, P. et Al., Progress of the Member States in implementing the Energy Performance of Building Directive, Publications Office of the European Union, Luxembourg, 2021.
[3] European Commission (2021). European Construction Sector Observatory Renovating the building envelope – Quo vadis?, Trend Paper Series.
[4] European Commission (2019). European Construction Sector Observatory, EU construction sector: in transition towards a circular economy, Trend Paper Series.
[5] State-of-the-Art Report of the RILEM Technical Committee 236-BBM (2017). Sofiane Amziane, Florence Collet (Eds); Springer Dordrecht.
[6] Fabbri A., McGregor F. (2017). Construction and Building Materials 157.
[7] Alexandra Bourdot, Camille Magniont, Méryl Lagouin, César Niyigena, Philippe Evon, et al. Journal of Advanced Concrete Technology, Japan Concrete Institute, 2019, 17 (9).
[8] Salem T., Fois M., Omikrine-Metalssi O., Manuel R., Fen-Chong T. (2020). Construction and Building Materials 264.
[9] Ministère de la Transition Écologique (mars 2020). Stratégie Nationale Bas-Carbone : La transition écologique et solidaire vers la neutralité carbone – Synthèse.
[10] G. M. Cappucci, V. Ruffini, V. Barbieri, C. Siligardi, A. M. Ferrari. Journal of Cleaner Production 349 (2022) 131437.
[11] Joe Tannous, Thouraya Salem, Othman Omikrine-Metalssi, Sandrine Marceau, Teddy Fen- Chong (2022). Construction and Building Materials, 346.
[12] M.P. Sáez-Pérez, Professor, M. Brümmer, J.A. Durán-Suárez. Journal of Building Engineering 31 (2020).
[13] Abbas M. S., McGregor F., Fabbri A., Ferroukhi M. Y. (2020). Construction and Building Materials 259, 120573.
[14] D. Shehadeh, T. Salem, O. Bouchenafa, C. Florence. 40èmes Rencontre Universitaire de Génie Civil, Academic Journal of Civil Engineering 40(1).
[15] H. Beddaa, I. Ouazi, A. Ben Fraj, F. Lavergne, J.M. Torrenti. Journal of Cleaner Production 265 (2020).
[16] Herinjaka Haga Ratsimbazafy, Aurélie Laborel-Préneron, Camille Magniont, Philippe Evon. Recent Progress in Materials 2021; 3(2).
[17] Kim Hung Mo, U. Johnson Alengaram, Mohd Zamin Jumaat, Soon Poh Yap, Siew Cheng Lee. Journal of Cleaner Production 117 (2016).
[18] Huaguo Chen, Jianjun Yang, Xinhong Chen. Construction and Building Materials 313 (2021).
[19] M.A. DeRousseau, J.R. Kasprzyk, W.V. Srubar III. Cement and Concrete Research 109 (2018).
[20] Junfei Zhang, Dong Li, Yuhang Wang. Journal of Building Engineering 30 (2020).
[21] S. Abdelfeteh. Thèse de doctorat, Université de Lille, 2016.
[22] L. Bal. Thèse de doctorat, Université de Lille, 2009.

Requirements

Research Field

Engineering » Civil engineering

Education Level

Master Degree or equivalent

Research Field

Computer science » Other

Education Level

Master Degree or equivalent

Skills/Qualifications

MD in Civil Engineering OR in Computer science

Specific Requirements

Please contact 

SALEM Thouraya  [email protected]

FEN-CHONG Teddy  -  [email protected] BERCHER Jean-François - [email protected]

before applying on the website, with CV, academic transcripts and covering letter.  

 

 

Languages

ENGLISH

Level

Good

Languages

FRENCH

Level

Basic

Additional Information
Work Location(s)

Number of offers available

1

Company/Institute

Un

Country

France

State/Province

Ile-de-France

City

Champs-sur-Marne

Postal Code

93162

Street

Cité Descartes

Geofield

Where to apply

Website

https://www.ifsttar.fr/offres-theses/sujet.php?num=3904&num_session=1&ver=an

Contact

City

Champs-sur-Marne

Website

https://univ-eiffel.fr/

Street

5 Boulevard Descartes

Postal Code

77420

STATUS: EXPIRED