H
Hamid Nikraz
Researcher at Curtin University
Publications - 467
Citations - 4165
Hamid Nikraz is an academic researcher from Curtin University. The author has contributed to research in topics: Asphalt & Consolidation (soil). The author has an hindex of 25, co-authored 457 publications receiving 3236 citations. Previous affiliations of Hamid Nikraz include Missouri University of Science and Technology & University of Western Australia.
Papers
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Properties of fly ash geopolymer concrete designed by Taguchi method
Monita Olivia,Hamid Nikraz +1 more
TL;DR: In this paper, the authors presented an optimization of fly ash geopolymer mixtures by Taguchi method, and a study on the mechanical properties and durability of concrete produced from the optimal mixes.
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Effects of nano-Al2O3 on early-age microstructural properties of cement paste
TL;DR: The effects of nano-Al2O3 addition on the early-age microstructural properties of cement paste is reported in this article, which is limited to evaluation of properties of Cement paste hydrated up to an age of 7 days.
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Mix design for fly ash based oil palm shell geopolymer lightweight concrete
TL;DR: In this paper, the experimental results of an on-going research project to produce geopolymer lightweight concrete using two locally available waste materials, low calcium fly ash (FA) and oil palm shell (OPS) as the binder and lightweight coarse aggregate, respectively, were presented.
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Comparison of embodied energies of Ordinary Portland Cement with Bayer-derived geopolymer products
TL;DR: In this paper, the authors evaluated the embodied energy of a new class of geopolymers, namely Bayer-derived geopolymer binders, and showed that they can be produced with embodied energy intensity at levels comparable to manufactured or recycled sand, gravel and stone.
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Predicting axial capacity of driven piles in cohesive soils using intelligent computing
Iyad Alkroosh,Hamid Nikraz +1 more
TL;DR: The results have demonstrated that the GEP model performs well with coefficient of correlation, mean and probability density at 50% equivalent to 0.94, 0.96 and 1.01, indicating that the proposed model predicts pile capacity accurately.