Ważne linki
Search
Seweryn Malazdrewicz
 |
Seweryn Malazdrewicz, MSc Eng seweryn.malazdrewicz@pwr.edu.pl, office hours for students, |
Ph.D. thesis:
Experimental evaluation of the fundamental properties of self-compacting concrete made of recycled coarse aggregate sourced from the demolition of large panel system buildings
Supervisor: Ph.D. Łukasz Sadowski
Co-supervisor: Ph.D. Krzysztof Ostrowski, Cracow University of Technology
Research fields:
artificial intelligence, recycled aggregates in concrete, abrasion resistance of concrete.
Projects:
Title: Experimental evaluation of the fundamental properties of self-compacting concrete made of recycled coarse aggregate sourced from the demolition of large panel system buildings (RECSCCUE)
Grant No. 2020/39/O/ST8/01217, Preludium Bis 2, National Science Center (Poland)
Position: Ph.D., project manager: Ph.D. Łukasz Sadowski
Duration: 2021-2025, grant value: 455 121 PLN - info PWr
Internships:
- Serbia – Univerzitet u Novom Sadu, Универзитет у Новом Саду – 2017 r., duration: 5 months – Erasmus+ with partner countries.
- Rosja – Московский Государственный Строительный Университет – 2019 r., duration: 4 months – Erasmus+ with partner countries.
Articles:
- Malazdrewicz, S., & Sadowski, Ł. (2021). An intelligent model for the prediction of the depth of the wear of cementitious composite modified with high-calcium fly ash. Composite Structures, 259, 113234.
- Malazdrewicz, S., & Sadowski, Ł. (2021). Neural modelling of the depth of wear determined using the rotating-cutter method for concrete with a high volume of high-calcium fly ash. Wear, 203791.
- Farooq, F., Czarnecki, S., Niewiadomski, P., Aslam, F., Alabduljabbar, H., Ostrowski, K. A., ... & Malazdrewicz, S. (2021). A comparative study for the prediction of the compressive strength of self-compacting concrete modified with fly ash. Materials, 14(17), 4934.
- Khan, M. A., Farooq, F., Javed, M. F., Zafar, A., Ostrowski, K. A., Aslam, F., Malazdrewicz S., Maślak, M. (2022). Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches. Materials, 15(1), 58.