Weave-UNISONO 2022-2025

Project title: Advanced approaches for determination and understanding of asphalt mix fatigue behavior

(Zaawansowane metody oceny i analizy zagadnienia zmęczenia mieszanek mineralno-asfaltowych)

Project number:

2021/03/Y/ST8/00079

WUT project team:

Warsaw University of Technology, Faculty of Civil Engineering

  • Professor Jan Król, PhD, DSc. – WUT leader team
  • Professor Marcin Gajewski, PhD, DSc.
  • Miftah Farid, MSc. – PhD student
  • Piotr Pokorski, PhD
  • Katarzyna Konieczna, MSc – researcher

Collaboration:

Czech Technical University in Prague

  • Jan Valentin, PhD – Associate Professor – Leader

University of Udine

  • Nicola Baldo, PhD – Associate Professor

Duration:

01-04-2022 – 31-03-2025

Project description:

In the last few decades, pavement engineering, especially in the field of asphalt mixtures, has performed major steps ahead in implementing new materials and technologies or targeting a more sustainable and material effective pavement design. Modern asphalt multilayer systems are much more long-lasting structures reflecting the ongoing increase in either traffic intensity or traffic load.

Since asphalt mixtures are based on bituminous binders, a complex organic viscoelastic and time-temperature dependent material, they naturally tend to change their performance. Their properties and some of the testing methods are well known and studied for decades. Nevertheless, there is very limited knowledge about fatigue relations, mainly if both bitumen and asphalt mix composite are assessed on fatigue in aged conditions.

In terms of determining the fatigue phenomenon both in bitumen and asphalt mixture levels, the most critical aspect is the time severity of the tests and the necessity to perform such advanced testing on a large number of test specimens. If further focusing on understanding the fatigue performance of virgin asphalt mix composite (as required so far in pavement engineering) and aged asphalt mixtures, the required effort to reach adequate results is vast. This problem could be partially reduced by determining bitumen fatigue parameters, evaluating only basic asphalt mix characteristics, and using advanced predictive models based on the Artificial Neural Network method.

To reduce the need for continuous repetition of laboratory tests and even in situ monitoring of the pavement, in the project modelling tools and consequently predictive tools of the asphalt mix performance will be a subject of development. Since the asphalt mix is a complex material and its performance is related to many factors, more advanced modelling techniques are needed to introduce reliable simulations or model-based robust predictive tools.

Artificial Neural Networks (ANN) will be involved in pavement engineering and road materials science to predict asphalt mix performance, avoid costly equipment laboratory tests and save time.

This study aims to define and validate a neural network-based predictive model for fatigue-life of asphalt mix based on bitumen fatigue and empirical characteristics of asphalt mix. Moreover, it is assumed to provide easy to use and quick prediction tools of parameters that could indicate the performance of the mixture before using tests in the laboratory.

NEWS

  1. The last paper published by our research group, titled “Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework”, has been selected to be the cover of the latest issue of CivilEng journal!
    https://www.linkedin.com/posts/fabio-rondinella-809b5b1b1_i-am-thrilled-to-share-that-one-of-the-latest-activity-7144324668227858432-VynA?utm_source=share&utm_medium=member_desktop
    CivilEng | December 2023 – Browse Articles (mdpi.com)
  2. MRP’23. Modern Road Pavements: Recycling in Road Pavement Structures” international conference. Research in the form of a poster on the ageing process analysis of polymer modified binder using modified TFOT test in the aspect of recycling purpose won the 3rd position in the best poster competition.
    https://www.linkedin.com/posts/miftah-farid-644929150_rap-warsawuniversityoftechnology-alwayscurious-activity-7121164407673356288-YSUX?utm_source=share&utm_medium=member_desktop
  3. Team from WUT consisting of Professor Jan Król, Katarzyna Konieczna, and Miftah Farid were attending the TRANSCOM 2023 Conference in Mikulov, Czech Republic. Miftah Farid presenting the research on fatigue parameter based on the bitumen data. The conference held from 29th – 31st of May 2023.
  4. Rozstrzygnięcie konkursu / The competition is over
    W ramach rozstrzygniętego konkursu na stanowisko stypendysty NCN w projekcie Weave-Unisono został wyłoniony Pan Miftah Farid
    As part of the completed competition, Mr. Miftah Farid was selected for the position of the NCN scholarship holder in the Weave-Unison project.
    /25 marca 2022/
  5. KONKURS! Międzynarodowy projekt w ramach działania Weave – UNISONO
    pt.: „Zaawansowane metody oceny i analizy zagadnienia zmęczenia mieszanek mineralno-asfaltowych”
    poszukuje doktoranta – stypendystyCOMPETITION! International project under the Weave – UNISONO competition
    entitled: “Advanced approaches for determination and understanding of asphalt mix fatigue behaviour”
    is looking for a PhD student – scholar holder
    /15 marca 2022/

Open science

We share a data in open science mode under a Creative Commons Attribution-NonCommercial 4.0 International Public License

Zenodo Repository

The date will be available in Zenodo Repository. The data will be assigned a Digital Object Identifier (DOI).

  1. Analysis of Effective Stiffness and Anisotropy of AC 16 Asphalt Mixture within NCN project Weave-UNISONO 2021, project No 2021/03/Y/ST8/00079, Król Jan, Gajewski Marcin
    https://zenodo.org/records/8326540
  2. Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K, Nicola Baldo, Fabio Rondinella, Fabiola Daneluz, Pavla Vacková, Jan Valentin, Marcin Gajewski, Jan Król
    https://zenodo.org/records/10438700
  3. Evaluation of bitumen’s fatigue resistance – a comparative study within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K, Farid Miftah, Król Jan, Gajewski Marcin, Pokorski Piotr, Konieczna Katarzyna, Valentin Jan, Baldo Nicola
    https://zenodo.org/records/10552666
  4. Investigating the ageing process of polymer modified bitumen using a modified Thin-Film Oven Test in the aspect of recycling purpose within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, Miftah Farid, Jan Król.
    https://zenodo.org/records/10866787

Publications

2024 (upcoming publications)

  1. N. Baldo, F. Rondinella, F. Daneluz, J. Valentin, P. Vacková, J.B. Król, M.D. Gajewski, Asphalt mixtures’ stiffness modulus prediction using a machinelearning approach based on temperature and frequency conditions. 8th International Conference Bituminous mixtures and Pavements. 12-14 June 2024. Thessaloniki, Greece

2023

  1. Farid, M., Król, J., Gajewski, M. D., Pokorski, P., Konieczna, K., Valentin, J., & Baldo, N. (2023). Evaluation of bitumen’s fatigue resistance – a comparative study. Transportation Research Procedia, 74, 748–755. https://doi.org/10.1016/j.trpro.2023.11.206
  2. Farid, M., & Król, J. (2023). Investigating the ageing process of polymer modified bitumen using a modified Thin-Film Oven Test in the aspect of recycling purpose. Roads and Bridges – Drogi i Mosty, 22, 379–386. https://doi.org/10.7409/rabdim.023.020
  3. Baldo, N.; Rondinella, F.; Daneluz, F.; Vacková, P.; Valentin, J.; Gajewski, M.D.; Król, J.B. (2023) Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework. CivilEng4, 1083-1097. https://doi.org/10.3390/civileng4040059

2022

  1. Gajewski, M.D.; Król, J.B. (2022) The Influence of Mortar’s Poisson Ratio and Viscous Properties on Effective Stiffness and Anisotropy of Asphalt Mixture. Materials15, 8946.
    https://doi.org/10.3390/ma15248946

Projekt finansowany przez Narodowe Centrum Nauki w ramach konkursu Weave-UNISONO w programie Weave

Project funded by the National Science Centre, Poland under the Weave-UNISONO call in the Weave programme

Narodowe Centrum Nauki