# Artificial Intelligence in Concrete Materials: A Scientometric View

arXiv: https://arxiv.org/abs/2209.09636 · HTML: https://mlsanity.com/paper/2209.09636/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 110 references that are in the map.
- First posted on arXiv 17 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Explaining NonLinear Classification Decisions with Deep Taylor Decomposition](https://mlsanity.com/paper/1512.02479/) (2015)
- [Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data](https://mlsanity.com/paper/1612.08544/) (2016)
- [Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling](https://mlsanity.com/paper/1710.11431/) (2017)
- [Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic](https://mlsanity.com/paper/2005.10732/) (2020)
- [Constructing bibliometric networks: A comparison between full and fractional counting](https://mlsanity.com/paper/1607.02452/) (2016)
- [Deep Learning-Based Automated Image Segmentation for Concrete Petrographic Analysis](https://mlsanity.com/paper/2005.10434/) (2020)

## Built on by (most cited first, 1 of 1)

- [Large language model-enabled automated data extraction for concrete materials informatics](https://mlsanity.com/paper/2604.22938/) (2026)

## Abstract (arXiv)

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.