A comprehensive literature mining and machine learning to decipher and predict effects of concrete materials on concrete corrosion of sewer pipe

ABSTRACT

The concrete composition of different pipelines varies greatly, and a comprehensive analysis of the impact of concrete design on pipeline corrosion is required. Developed an integrated model based on Scikit-Optimize, XGBoost and SHAP to analyze concrete’s carbonation and sulfuric acid resistance from five aspects: binder type, supplementary cementitious material (SCM’s), aggregate, admixture and w/b. Choose Scikit-Optimize for parameter optimization. As an explanation model, the SHAP model can perform analysis well and explain the analysis basis of the XGBoost training model. The established model can accurately predict the corrosion effect (R² >0.987) without overfitting. Binder should be maintained at 380 to 400 (kg/m³). Water/binder and coarse aggregate/binder are the two main factors that influence the binder, and they should be maintained at a ratio of around 0.4 to 0.6 and 2.5, respectively. Fly ash (FA) should be SCM’s used as at a range of 300 to 350 (kg/m³) to improve corrosion resistance.

KEYWORDS:

• Sewer corrosion
• concrete
• workability
• machine learning
• acid corrosion

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data presented herein will be provided upon reasonable request.

Author’s contribution statement

Wenhao Wang: Investigation, Methodology, Writing. Jingguo Cao: Supervision, Project administration; Ming Zeng: Supervision, Methodology, Yongxiang Qiao: Concepts.

Supplementary material

Supplemental data for this article can be accessed https://doi.org/10.1080/1573062X.2024.2352599.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.