Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations-Reference-Cited by-同舟云学术

Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations

Published:2024-07 Issue: Volume:20 Page:e02723
ISSN:2214-5095
Container-title:Case Studies in Construction Materials
language:en
Short-container-title:Case Studies in Construction Materials

Author:

Das Pobithra,Kashem Abul

Publisher

Elsevier BV

Subject

Materials Science (miscellaneous)

Reference71 articles.

1. Application of Ultra-High-Performance Concrete in Bridge Engineering: Current Status, Limitations, Challenges, and Future Prospects;Abdal,2023

2. Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis;Abdulalim Alabdullah;Constr. Build. Mater.,2022

3. Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques;Abuodeh;Appl. Soft Comput. J.,2020

4. Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques;Ahmad;Case Stud. Constr. Mater.,2022

5. Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis;Ahmed;Materials,2023

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