Interpretable ensemble learning framework for shear strength assessment of corroded reinforced concrete beams
Guotao Shao, Shengquan Zou, Guangcai Zhang, Tao Huang · 2026
Read Interpretable ensemble learning framework for shear strength assessment of corroded reinforced concrete beams by Guotao Shao, Shengquan Zou, Guangcai Zhang, Tao Huang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13350925.txt.
Description
Guotao Shao, Shengquan Zou, Guangcai Zhang, and Tao Huang predict the strength of rusted concrete beams with explainable AI, in a paper dated 2026. Corrosion eats the steel inside beams and saps their resistance to sliding failure. Old formulas assume too much, and black-box models cannot say why they worry.
The team assembled test data on 180 corroded beams, covering materials, shape, reinforcement, and rust measures. Several learning models competed, and a stacked ensemble won, landing most test predictions within twenty percent of measured strength. Interpretation tools then ranked which inputs drive the verdicts. Owners of aging bridges and buildings gain a forecast they can both trust and explain when deciding on repairs.
Catalog record
| CREATOR | Guotao Shao, Shengquan Zou, Guangcai Zhang, Tao Huang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |