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

Ehsan Sadrossadat

doi:10.26182/af7b-nr82

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

149 Downloads (Pure)

Abstract

Cementitious mixtures such as concrete and cemented-paste-backfill are important in construction and mining projects due to their designable properties. In terms of sustainability, they must meet many functional, environmental and economic objectives and constraints. Design optimisation aims to adjust the constituents and their proportions so that the product meets the desired criteria. This research engineers the concept and proposes artificial intelligence based frameworks for the design optimisation of cementitious mixtures using machine learning and metaheuristic techniques. Compared to traditional methods, this research contributes to reducing design costs and associated efforts, optimising the use of resources, and disseminating more sustainable solutions.

Original language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia
Supervisors/Advisors	Karrech, Ali, Supervisor Basarir, Hakan, Supervisor Elchalakani, Mohamed, Supervisor
Thesis sponsors	Australian Government Research Training Program
Award date	28 Nov 2022
DOIs	https://doi.org/10.26182/af7b-nr82
Publication status	Unpublished - 2022

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10.26182/af7b-nr82

THESIS - DOCTOR OF PHILOSOPHY - SADROSSADAT Ehsan - 2022
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4 Article
1 Conference paper

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
39 Citations (Web of Science)
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
43 Citations (Scopus)

Cite this

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

abstract = "Cementitious mixtures such as concrete and cemented-paste-backfill are important in construction and mining projects due to their designable properties. In terms of sustainability, they must meet many functional, environmental and economic objectives and constraints. Design optimisation aims to adjust the constituents and their proportions so that the product meets the desired criteria. This research engineers the concept and proposes artificial intelligence based frameworks for the design optimisation of cementitious mixtures using machine learning and metaheuristic techniques. Compared to traditional methods, this research contributes to reducing design costs and associated efforts, optimising the use of resources, and disseminating more sustainable solutions.",

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school = "The University of Western Australia",

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AB - Cementitious mixtures such as concrete and cemented-paste-backfill are important in construction and mining projects due to their designable properties. In terms of sustainability, they must meet many functional, environmental and economic objectives and constraints. Design optimisation aims to adjust the constituents and their proportions so that the product meets the desired criteria. This research engineers the concept and proposes artificial intelligence based frameworks for the design optimisation of cementitious mixtures using machine learning and metaheuristic techniques. Compared to traditional methods, this research contributes to reducing design costs and associated efforts, optimising the use of resources, and disseminating more sustainable solutions.

KW - cemented paste backfill

KW - concrete

KW - design optimisation

KW - artificial intelligence

KW - sustainability

KW - tailings

KW - metaheuristics

KW - machine learning

U2 - 10.26182/af7b-nr82

DO - 10.26182/af7b-nr82

M3 - Doctoral Thesis

ER -

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

Abstract

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Research output

An engineered ML model for prediction of the compressive strength of Eco-SCC based on type and proportions of materials

Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics

Multi-objective mixture design of cemented paste backfill using particle swarm optimisation algorithm

Cite this