A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models DOI Creative Commons
Yusuf Tahir ALTUNCI

Buildings, Journal Year: 2024, Volume and Issue: 14(12), P. 3851 - 3851

Published: Nov. 30, 2024

Conducting comprehensive analyses to predict concrete compressive strength is crucial for enhancing safety in field applications and optimizing work processes. There an extensive body of research the literature focusing on predicting mechanical properties concrete, such as strength. Summarizing key contributions these studies will serve a guide future research. To this end, study aims conduct scientometric analysis that utilize machine learning (ML) models strength, assess models, provide insights developing optimal solutions. Additionally, it seeks offer researchers information prominent themes, trends, gaps regarding prediction. For purpose, 2319 articles addressing prediction published between 2000 19 August 2024, were identified through Scopus Database. Scientometric conducted using VOSviewer software. The evaluation relevant demonstrates ML are frequently used advantages limitations examined, with particular emphasis considerations when working complex datasets. A their practical distinguishes from existing This contributes significantly by examining leading institutions, countries, authors, sources field, synthesizing data, identifying areas, gaps, trends It establishes strong foundation design ML-supported, reliable, sustainable, optimized structural systems civil engineering, building materials, industry.

Language: Английский

A review of the repair measures for reinforced concrete affected by chloride ion corrosion DOI
Peng Zhao, Zheng Si,

Lingzhi Huang

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: 102, P. 112028 - 112028

Published: Feb. 6, 2025

Language: Английский

Citations

0
A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models DOI Creative Commons
Yusuf Tahir ALTUNCI

Buildings, Journal Year: 2024, Volume and Issue: 14(12), P. 3851 - 3851

Published: Nov. 30, 2024

Conducting comprehensive analyses to predict concrete compressive strength is crucial for enhancing safety in field applications and optimizing work processes. There an extensive body of research the literature focusing on predicting mechanical properties concrete, such as strength. Summarizing key contributions these studies will serve a guide future research. To this end, study aims conduct scientometric analysis that utilize machine learning (ML) models strength, assess models, provide insights developing optimal solutions. Additionally, it seeks offer researchers information prominent themes, trends, gaps regarding prediction. For purpose, 2319 articles addressing prediction published between 2000 19 August 2024, were identified through Scopus Database. Scientometric conducted using VOSviewer software. The evaluation relevant demonstrates ML are frequently used advantages limitations examined, with particular emphasis considerations when working complex datasets. A their practical distinguishes from existing This contributes significantly by examining leading institutions, countries, authors, sources field, synthesizing data, identifying areas, gaps, trends It establishes strong foundation design ML-supported, reliable, sustainable, optimized structural systems civil engineering, building materials, industry.

Language: Английский

Citations

2