The design optimisation of cementitious mixtures using artificial intelligence algorithms for sustainable mining and construction projects

Ehsan Sadrossadat

School of Engineering

Research output: Thesis › Doctoral Thesis

158 Downloads (Pure)

4 Article
1 Conference paper

4 results

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2022
An engineered ML model for prediction of the compressive strength of Eco-SCC based on type and proportions of materials
Sadrossadat, E., Basarir, H., Karrech, A. & Elchalakani, M., Jun 2022, In: Cleaner Materials. 4, 100072.
Research output: Contribution to journal › Article › peer-review

Open Access
8 Citations (Scopus)
Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics
Sadrossadat, E., Basarir, H., Karrech, A. & Elchalakani, M., Aug 2022, In: Engineering with Computers. 38, p. 2569-2582 14 p.
Research output: Contribution to journal › Article › peer-review
42 Citations (Scopus)
2020
Multi-objective mixture design of cemented paste backfill using particle swarm optimisation algorithm
Sadrossadat, E., Basarir, H., Luo, G., Karrech, A., Durham, R., Fourie, A. & Elchalakani, M., 1 Jul 2020, In: Minerals Engineering. 153, 106385.
Research output: Contribution to journal › Article › peer-review
45 Citations (Scopus)
2019
An evolutionary-based prediction model of the 28-day compressive strength of high-performance concrete containing cementitious materials
Sadrossadat, E. & Basarir, H., 29 May 2019, In: Advances in Civil Engineering Materials. 8, 3, 14 p.
Research output: Contribution to journal › Article › peer-review
16 Citations (Scopus)