Artificial Intelligence in Concrete Materials: A Scientometric View
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912268789219328 |
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| author | Li, Zhanzhao Radlińska, Aleksandra |
| author_facet | Li, Zhanzhao Radlińska, Aleksandra |
| contents | Artificial intelligence (AI) has emerged as a transformative and versatile tool, breaking new frontiers across scientific domains. Among its most promising applications, AI research is blossoming in concrete science and engineering, where it has offered new insights towards mixture design optimization and service life prediction of cementitious systems. This chapter aims to uncover the main research interests and knowledge structure of the existing literature on AI for concrete materials. To begin with, a total of 389 journal articles published from 1990 to 2020 were retrieved from the Web of Science. Scientometric tools such as keyword co-occurrence analysis and documentation co-citation analysis were adopted to quantify features and characteristics of the research field. The findings bring to light pressing questions in data-driven concrete research and suggest future opportunities for the concrete community to fully utilize the capabilities of AI techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_09636 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence in Concrete Materials: A Scientometric View Li, Zhanzhao Radlińska, Aleksandra Artificial Intelligence Materials Science Artificial intelligence (AI) has emerged as a transformative and versatile tool, breaking new frontiers across scientific domains. Among its most promising applications, AI research is blossoming in concrete science and engineering, where it has offered new insights towards mixture design optimization and service life prediction of cementitious systems. This chapter aims to uncover the main research interests and knowledge structure of the existing literature on AI for concrete materials. To begin with, a total of 389 journal articles published from 1990 to 2020 were retrieved from the Web of Science. Scientometric tools such as keyword co-occurrence analysis and documentation co-citation analysis were adopted to quantify features and characteristics of the research field. The findings bring to light pressing questions in data-driven concrete research and suggest future opportunities for the concrete community to fully utilize the capabilities of AI techniques. |
| title | Artificial Intelligence in Concrete Materials: A Scientometric View |
| topic | Artificial Intelligence Materials Science |
| url | https://arxiv.org/abs/2209.09636 |