Prediction of the Sulfate Attack Resistance of Concrete Based on Machine-Learning Algorithms-Reference-Cited by-同舟云学术

Prediction of the Sulfate Attack Resistance of Concrete Based on Machine-Learning Algorithms

Published:2024-11 Issue:6 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Li Yue¹,Shi Junjie²^ORCID,Shen Jiale³,Jin Kaikai⁴,Fan Mengtian⁴,Liu Xiaolong⁴

Affiliation:

1. Professor, College of Architecture and Civil Engineering, Beijing Univ. of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, PR China.

2. Master’s Student, College of Architecture and Civil Engineering, Beijing Univ. of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, PR China (corresponding author). ORCID: .

3. Ph.D. Student, College of Architecture and Civil Engineering, Beijing Univ. of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, PR China.

4. Master’s Student, College of Architecture and Civil Engineering, Beijing Univ. of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, PR China.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-6064

Reference38 articles.

1. Role of chloride ions on expansion and strength reduction in plain and blended cements in sulfate environments

2. Response of concrete to accelerated physical salt attack exposure

3. Compressive strength assessment of sulfate-attacked concrete by using sulfate ions distributions

4. Clifton, J. R., and J. M. Pommersheim. 1994. Sulfate attack of cementitious materials: Volumetric relations and expansions. Gaithersburg, MD: National Institute of Standards and Technology.

5. Enhancing concrete sulfate resistance by adding NaCl