Genetic Programming for automated Synthesizing 3D Artificial Magnetic Conductor

Nguyen, Khac Son; Bach, Thuan Bui; Bui, Manh Cuong; Thi, Hoa Phuong Tran; Nguyen, Khac Kiem; Ta, Son Xuat

doi:10.1109/atc50776.2020.9255428

Cited by 2 publications

(2 citation statements)

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“…Recently, owing to their smaller particle size than cement grains, nanoclays, such as nano-kaolin, bentonite, mica, and nano-montmorillonite, are utilized as nanofillers in cement-based products which improve chemical, moisture and gas barrier, and mechanical and thermal properties of the material [186]. Nguyen et al [187] found that the combustion rate (mm/min) of epoxy resin/nanoclay/multiwalled carbon nanotube nanocomposites decreased from 28.41 to 20.80, while the LOI (vol. % O2) increased from 20.6 to 24.6.…”

Section: Fire Retardancymentioning

confidence: 99%

Nanoclays as fillers for performance enhancement in building and construction industries: State of the art and future trends

Bantie,

Tezera,

Abera

et al. 2024

Developments in Clay Science and Construction Techniques

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In construction engineering, there is currently a strong emphasis on finding construction materials, mainly the binder which plays a crucial role, that meet multiple criteria, including sustainability, cost-effectiveness, durability, and reduced environmental impact. However, there is a growing interest in exploring alternatives to traditional binders to address the limitations associated with their production and use. One such alternative is the use of naturally occurring materials like clay. Clay deposits are abundant and widely available, making them a sustainable resource for construction applications. Moreover, clay contains significant amounts of silica and alumina, which are key components for inducing pozzolanic reactions that contribute to the strength and durability of concrete. In recent studies, nanoclays (NCs) have emerged as a promising addition to construction materials as supplementary cementitious materials. These nanoparticles possess unique properties that can enhance the performance of concrete. Nanoclays significantly improve the compressive strength, sustainability, and durability of concrete structures. The high surface area and reactivity of nanoclays facilitate better bonding between cement particles, resulting in enhanced mechanical properties. This chapter aims to discuss the state of the art on performance enhancements of building materials that employ different types of nanoclays in place of conventional binders and the future trends.

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Section: Fire Retardancymentioning

confidence: 99%

Nanoclays as fillers for performance enhancement in building and construction industries: State of the art and future trends

Bantie,

Tezera,

Abera

et al. 2024

Developments in Clay Science and Construction Techniques

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show abstract

“…Publication Year Method(s) Application of AI 196 Zhu, Wang, Sun, et al [403] 2020 GA optimization framework 197 Zhu, Wang, Sui, et al [404] 2020 GA optimization framework 198 Tung, Ha-Van, and Seo [405] 2020 GA optimization framework 199 Suraj, Behera, and Badhai [406] 2020 GA optimization framework 200 Jiang, Li, Li, et al [407] 2020 GA optimization framework 201 Whiting, Kang, Campbell, et al [408] 2020 GA optimization framework 202 Cui, Xu, Yu, et al [409] 2020 GA optimization framework 203 Thomes, Mosquera-Sánchez, and De Marqui [410] 2020 GA optimization framework 204 Nguyen, Bui Bach, Bui, et al [411] 2020 genetic programming (GP) optimization framework 205 Li and Yang [412] 2020 NSCGA optimization framework 206 Li and Yang [413] 2020 NSCGA optimization framework 207 Yang, Huang, Song, et al [414] 2020 PSO optimization framework 208 Papathanasopoulos and Rahmat-Samii [415] 2020 PSO optimization framework 209 Diaz, Burckel, Adomanis, et al [416] 2021 GA optimization framework 210 Ahmadi, Vaezi, Harzand, et al [417] 2021 GA optimization framework 211 Gonçalves, Mesquita, and Silva [418] 2021 GA optimization framework 212 Mayer, Bi, Griesse-Nascimento, et al [419] 2022 GA Optimization framework 213 Zong, Zhu, Yu, et al [420] 2022 GA Inverse Design 214 Liu, Wang, Olivier, et al [421] 2022 GA Inverse Design 215 Luo, Lan, Nong, et al [422] 2022 GA Inverse Design 216 Nguyen and Seo [423] 2022 GA Inverse Design 217 Lu, Gao, and Dai [424] 2022 GA Inverse Design 218 Sun, Jiang, and Wang [425] 2023 GA Optimization Framework 219 Feng, Zhang, Cheng, et al [426] 2023 GA Optimization Framework 220 Zhang, Duan, Liu, et al [427] 2023 GA Optimization Framework…”

Section: Continuation Of Tablementioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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Machine intelligence continues to rise in popularity as an aid to the design and discovery of novel metamaterials. The properties of metamaterials are essentially controllable via their architectures and until recently, the design process has relied on a combination of trial-and-error and physics-based methods for optimization. These processes can be time-consuming and challenging, especially if the design space for metamaterial optimization is explored thoroughly. Artificial intelligence (AI) and machine learning (ML) can be used to overcome challenges like these as pre-processed massive metamaterial datasets can be used to very accurately train appropriate models. The models can be broad, describing properties, structure, and function at numerous levels of hierarchy, using relevant inputted knowledge. Here, we present a comprehensive review of the literature where state-of-the-art machine intelligence is used for the design, discovery and development of metamaterials. In this review, individual approaches are categorized based on methodology and application. We further present machine intelligence trends over a wide range of metamaterial design problems including: acoustics, photonics, plasmonics, mechanics, and more. Finally, we identify and discuss recent research directions and highlight current gaps in knowledge. KeywordsMetamaterials • Artificial intelligence • Machine learning • Neural Networks • Evolutionary algorithms • Surrogate modelling • Optimization 42 solely related to the properties of material constituents. 43 Properties can therefore be controlled and manipulated by 44 changing metamaterial 'unit cell' topology and while the 45 bulk properties of the constituent materials have influence, 46 these properties are often not the sole dominating variable 47 used in designing metamaterials. When designing with 48 respect to topology, the properties of metamaterials can 49 be customized within a very large design space, and this 50 is one of the reasons for the exponential growth in meta-51 materials R&D across the breadth of modern engineering.

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Genetic Programming for automated Synthesizing 3D Artificial Magnetic Conductor

Cited by 2 publications

References 5 publications

Nanoclays as fillers for performance enhancement in building and construction industries: State of the art and future trends

Nanoclays as fillers for performance enhancement in building and construction industries: State of the art and future trends

Machine Intelligence in Metamaterials Design

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