Interpretable XGBoost–SHAP machine learning technique to predict the compressive strength of environment-friendly rice husk ash concrete

• Published in: Innovative infrastructure solutions : the official journal of the Soil-Structure Interaction Group in Egypt (SSIGE) 2023-05, Vol.8 (5), Article 147
• Main Authors: Uddin, Md Nasir, Li, Ling-Zhi, Deng, Bo-Yu, Ye, Junhong
• Format: Article
• Language: English
• Subjects: Earth and Environmental Science; Earth Sciences; Environmental Science and Engineering; Foundations; Geoengineering; Geotechnical Engineering & Applied Earth Sciences; Hydraulics; Technical Paper
• ISSN: 2364-4176; 2364-4184
• DOI: 10.1007/s41062-023-01122-9

Global greenhouse gas emissions from the construction concrete industry are 50% higher than those from all other industries combined. Concrete incorporating waste and recycled materials could help lessen the negative effects of environmental problems. Agricultural waste is increasingly being used to substitute cement in environmentally friendly concrete production. Rice husk ash (RHA) is a workable alternative that merits further investigation. Since evaluating the properties of concrete containing RHA requires extensive and time-consuming experimentation, machine learning (ML) can accurately predict its properties. Consequently, this study aims to anticipate and develop an empirical formula for RHA concrete’s compressive strength (CS) using ML algorithms. This study employs several ML methods such as random forest, support vector machine, light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and SHAP. A total of 192 data points are used in this study to assess the CS of RHA-blended concrete. The input parameters are age, amount of cement, rice husk ash, superplasticizer, water, and aggregates. Across all ML models, the XGBoost method is used to build a highly accurate predictive model. Predicting RHA concrete's CS using an existing XGBoost model is consistently accurate. R 2 demonstrates a CS of 0.99 during training and 0.94 during testing. Model characteristics and complex correlations are explained using the SHAP algorithm. The proposed model’s prediction outcomes are compared to prior research, and the best ML algorithm is selected.