J
Jiang Zhu
Researcher at Guangdong University of Technology
Publications - 12
Citations - 410
Jiang Zhu is an academic researcher from Guangdong University of Technology. The author has contributed to research in topics: Mortar & Natural rubber. The author has an hindex of 8, co-authored 12 publications receiving 259 citations.
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Synergistic effects of micro-silica and nano-silica on strength and microstructure of mortar
TL;DR: In this article, the combined effects of nano-silica (NS) and micro-cement (MS) on the strength and microstructure of mortar or the mortar portion of HPC were investigated.
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Roles of water film thickness and fibre factor in workability of polypropylene fibre reinforced mortar
TL;DR: In this article, a new model is developed whereby the workability attributes are each expressed as a single variable function of the product of a linear function of WFT and an exponential function of FF.
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Reutilizing ceramic polishing waste as powder filler in mortar to reduce cement content by 33% and increase strength by 85%
TL;DR: In this paper, the feasibility of powder filler technology for reducing the amount of ceramic waste to be dumped, and the cement consumption and carbon footprint of the concrete production was investigated, and two design charts for adding ceramic polishing waste (CPW) as paste replacement were developed.
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Packing density of mortar containing polypropylene, carbon or basalt fibres under dry and wet conditions
TL;DR: In this article, the packing density of fiber reinforced concrete made with flexible fibres was measured under dry and wet conditions and with and without super-plasticizer added conditions and compared to evaluate the effects of the flexible fibers on the packing densities.
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Prediction of carbonation depth for recycled aggregate concrete using ANN hybridized with swarm intelligence algorithms
TL;DR: In this paper, the authors investigated the prediction of carbonation depth for recycled aggregate concrete (RAC) with machine learning models and found that the Random Forest model showed superior performance than the Gaussian progress regression model and standalone artificial neural network (ANN) model.