Alireza Kashani

Bio: Alireza Kashani is an academic researcher from University of New South Wales. The author has contributed to research in topics: Portland cement & Mortar. 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.

Additive manufacturing (3D printing): A review of materials, methods, applications and challenges

Abstract: Freedom of design, mass customisation, waste minimisation and the ability to manufacture complex structures, as well as fast prototyping, are the main benefits of additive manufacturing (AM) or 3D printing. A comprehensive review of the main 3D printing methods, materials and their development in trending applications was carried out. In particular, the revolutionary applications of AM in biomedical, aerospace, buildings and protective structures were discussed. The current state of materials development, including metal alloys, polymer composites, ceramics and concrete, was presented. In addition, this paper discussed the main processing challenges with void formation, anisotropic behaviour, the limitation of computer design and layer-by-layer appearance. Overall, this paper gives an overview of 3D printing, including a survey on its benefits and drawbacks as a benchmark for future research and development.

Deep neural network with high-order neuron for the prediction of foamed concrete strength

Abstract: The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high-order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross-entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C-ANN) and second-order artificial neural network (SO-ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high-performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C-ANN and SO-ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water-to-cement and sand-to-cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.

Additive manufacturing (3D printing): A review of materials, methods, applications and challenges

Abstract: Freedom of design, mass customisation, waste minimisation and the ability to manufacture complex structures, as well as fast prototyping, are the main benefits of additive manufacturing (AM) or 3D printing. A comprehensive review of the main 3D printing methods, materials and their development in trending applications was carried out. In particular, the revolutionary applications of AM in biomedical, aerospace, buildings and protective structures were discussed. The current state of materials development, including metal alloys, polymer composites, ceramics and concrete, was presented. In addition, this paper discussed the main processing challenges with void formation, anisotropic behaviour, the limitation of computer design and layer-by-layer appearance. Overall, this paper gives an overview of 3D printing, including a survey on its benefits and drawbacks as a benchmark for future research and development.

Fiber/Fabric-Based Piezoelectric and Triboelectric Nanogenerators for Flexible/Stretchable and Wearable Electronics and Artificial Intelligence.

Abstract: Integration of advanced nanogenerator technology with conventional textile processes fosters the emergence of textile-based nanogenerators (NGs), which will inevitably promote the rapid development and widespread applications of next-generation wearable electronics and multifaceted artificial intelligence systems. NGs endow smart textiles with mechanical energy harvesting and multifunctional self-powered sensing capabilities, while textiles provide a versatile flexible design carrier and extensive wearable application platform for their development. However, due to the lack of an effective interactive platform and communication channel between researchers specializing in NGs and those good at textiles, it is rather difficult to achieve fiber/fabric-based NGs with both excellent electrical output properties and outstanding textile-related performances. To this end, a critical review is presented on the current state of the arts of wearable fiber/fabric-based piezoelectric nanogenerators and triboelectric nanogenerators with respect to basic classifications, material selections, fabrication techniques, structural designs, and working principles, as well as potential applications. Furthermore, the potential difficulties and tough challenges that can impede their large-scale commercial applications are summarized and discussed. It is hoped that this review will not only deepen the ties between smart textiles and wearable NGs, but also push forward further research and applications of future wearable fiber/fabric-based NGs.