Data-driven shear strength predictions of recycled aggregate concrete beams with/without shear reinforcement by applying machine learning approaches

Recycled aggregate concrete (RAC) has gradually received attention in recent years for sustainable development. However, the adaptation of RAC for structural elements is becoming less popular due to the lack of proper design guidelines. Thus, to encourage the structural application of RAC, it is essential to develop a framework for predicting the shear capacity of RAC beams. The data-driven prediction has demonstrated excellent accuracy compared to traditional empirical equations. This study proposed a data-driven framework using machine learning. In this paper, 401 samples of different RAC beams are analysed, including 264 slender beams and 137 deep beams. Eight machine learning (ML) algorithms namely Linear Regression, K-nearest neighbour, Random Forest, Adaptive boosting, Gradient Boosting, Extreme gradient boosting (XGBoost), Category Boosting, and Light Gradient Boosting Machine were employed to develop the best ML-based framework. Having the best prediction output (R2 score: 0.95 in slender beam, 0.78 in deep beam), XGBoost is selected to further analyse the feature importance and parametric study with SHapley Additive exPlanations (SHAP). Simultaneously, the experiment values were compared with calculated values according to the codes in different regions, such as ACI381-19, AS3600-2018, Eurocode 2, and GB50010-2010. The results of SHAP from XGBoost indicate that the replacement ratio of RCA has limited influence on normalized shear strength value, Vu/bd, and it is applicable to adopt RAC without shear strength reduction if the replacement ratio of RA is lower than 40%. Nevertheless, the comparison of experiment value from the database and calculated value according to classical analytical methods demonstrates the necessity for adjustment to existing design codes against RAC replacement ratio. Some suggestions which could further improve the accuracy of results are provided at the end of this paper as well.

Automated summary

Recycled aggregate concrete (RAC) has gained attention for sustainable development, but its use in structural elements is limited due to the lack of design guidelines. This study proposes a data-driven framework using machine learning to predict the shear capacity of RAC beams. Analysis of 401 RAC beam samples and the implementation of eight machine learning algorithms identified XGBoost as the best ML-based framework with high prediction accuracy.