Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis-Reference-Cited by-同舟云学术

Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis

Published:2022-08 Issue: Volume:345 Page:128296
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

Abdulalim Alabdullah Anas,Iqbal Mudassir,Zahid Muhammad,Khan Kaffayatullah,Nasir Amin Muhammad,Jalal Fazal E.

Publisher

Elsevier BV

Subject

General Materials Science,Building and Construction,Civil and Structural Engineering

Reference74 articles.

1. Using machine learning techniques for predicting autogenous shrinkage of concrete incorporating superabsorbent polymers and supplementary cementitious materials;Hilloulin;J. Build. Eng.,2022

2. Handbook of alkali-activated cements, mortars and concretes;Pacheco-Torgal,2014

3. Machine learning applications for developing sustainable construction materials, Artificial Intelligence and Data Science in Environmental Sensing;Adel;Elsevier,2022

4. New trends in eco-efficient and recycled concrete;de Brito,2018

5. Evaluation of chloride limits for reinforced concrete phase A, national ready mixed concrete association;Obla,2017

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