Artificial intelligence applied to the stone manufacturing industry: A systematic literature review

Natural stone has long been used in construction, as its properties provide functional and visual value, and the natural stone market currently holds significant importance in the global economy. It is important to consider integrating new technologies in the production chain to aid the industry in moving forward, increasing profit margins and reducing wasted material. This article reviews recent trends in using Artificial Intelligence and Machine Learning techniques in the industry between 2017 and 2024, following a methodology for Systematic Literature Reviews in computer science. It was found that extensive research has been conducted on the subject of tile classification, with solid solutions proposed, achieving results that can be considered robust enough for industrial application. Other subjects comprise tasks regarding stone cutting and defect detection, as well as variable prediction, and quarry activity monitoring. Some authors propose solutions to integrate new technologies into the complete production chain. While more research needs to be done on specific subjects, this review provides a solid first step to future research.

Authors

Institutions

Publication Details

Journal
Computers & Electrical Engineering
Published
2025-09-16
DOI
https://doi.org/10.1016/j.compeleceng.2025.110702
Citations
2
Primary Topic
3D Surveying and Cultural Heritage
Type
article
Field-Weighted Citation Impact
4.69
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial intelligence applied to the stone manufacturing industry: A systematic literature review

C Fidalgo Antunes, José Ribeiro, Rogério Luís de C. Costa, Rolando Miragaia et al.
2 citations
Computers & Electrical Engineering
3D Surveying and Cultural Heritage
4.69
article

Artificial intelligence applied to the stone manufacturing industry: A systematic literature review

C Fidalgo Antunes, José Ribeiro, Rogério Luís de C. Costa, Rolando Miragaia, Fernando Silva, Alexandre Silva
article en
2 citations

Abstract

Natural stone has long been used in construction, as its properties provide functional and visual value, and the natural stone market currently holds significant importance in the global economy. It is important to consider integrating new technologies in the production chain to aid the industry in moving forward, increasing profit margins and reducing wasted material. This article reviews recent trends in using Artificial Intelligence and Machine Learning techniques in the industry between 2017 and 2024, following a methodology for Systematic Literature Reviews in computer science. It was found that extensive research has been conducted on the subject of tile classification, with solid solutions proposed, achieving results that can be considered robust enough for industrial application. Other subjects comprise tasks regarding stone cutting and defect detection, as well as variable prediction, and quarry activity monitoring. Some authors propose solutions to integrate new technologies into the complete production chain. While more research needs to be done on specific subjects, this review provides a solid first step to future research.

Computers & Electrical EngineeringVol. 128
Instituto Politécnico de Leiria (PT)
Openalex Percentile: Top 5%
3D Surveying and Cultural Heritage
4.69
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Artificial intelligence applied to the stone manufacturing industry: A systematic literature review — C Fidalgo Antunes, José Ribeiro, et al. · Computers & Electrical Engineering (2025) | TGRS Research Map | TGRS