A
Alireza Kashani
Researcher at University of New South Wales
Publications - 47
Citations - 5785
Alireza Kashani is an academic researcher from University of New South Wales. The author has contributed to research in topics: Portland cement & Computer science. The author has an hindex of 16, co-authored 33 publications receiving 3160 citations. Previous affiliations of Alireza Kashani include Amirkabir University of Technology & University of Melbourne.
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The effect of basecoat pigmentation on the scratch resistance and weathering performance of an acrylic–melamine basecoat/clearcoat automotive finish
TL;DR: In this paper, scratch resistance of an acrylic/melamine clearcoat (on the black and silver basecoats) was studied by means of laboratory simulator carwash and nano-scratch tests before and after carrying out a weathering test.
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Nozzle criteria for enhancing extrudability, buildability and interlayer bonding in 3D printing concrete
TL;DR: In this paper , the authors conduct a systematic content review of 70 research papers on nozzle design that were published over the past decade (2012 to 2022), focusing on notable gaps in prevailing literature in terms of: current design practices of a nozzle; correlations between nozzle and printability; and current advances in testing methods.
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Carbon sequestration in engineered lightweight foamed mortar – Effect on rheology, mechanical and durability properties
TL;DR: In this article , the influence of biochar, prepared from wood waste, in enhancing carbon sequestration in foamed mortar of three target densities (1150 kg/m3, 1300 kg /m3 and 1450 kg *m3) through accelerated carbonation (CO2 concentration of 2%) technique is investigated.
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Green mix design of rubbercrete using machine learning-based ensemble model and constrained multi-objective optimization
TL;DR: In this paper, a green mix design model is proposed to estimate the constituents of rubbercrete using the machine learning-based ensemble model (as a combination of M5P tree and multi-gene expression programming (MGEP) algorithms) as well as constrained multi-objective grey wolf optimizer.
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Simulation of cellular structures under large deformations using the material point method
TL;DR: In this article, the performance of the material point method (MPM) is evaluated using experimental measurements of the force deformation curve and energy absorption capacity of stacked tubes and the results indicate that MPM is capable of predicting the large deformation response and energy absorbing properties of cellular structures.