A Two-Level Machine Learning Prediction Approach for RAC Compressive Strength

Abstract

Through the use of recycled aggregates, the construction industry can mitigate its environmental impact. A key consideration for concrete structural engineers when designing and constructing concrete structures is compressive strength. This study aims to accurately forecast the compressive strength of recycled aggregate concrete (RAC) using machine learning techniques. We propose a simplified approach that incorporates a two-layer stacked ensemble learning model to predict RAC compressive strength. In this framework, the first layer consists of ensemble models acting as base learners, while the second layer utilizes a random forest (RF) model as the meta-learner. A comparative analysis with four other ensemble learning models demonstrates the superior performance of the proposed stacked model in effectively integrating predictions from the base learners, resulting in enhanced model accuracy. The model achieves a low mean absolute error (MAE) of 2.599 MPa, a root mean squared error (RMSE) of 3.645 MPa, and a high R-squared (R2) value of 0.964. Additionally, a Shapley (SHAP) additive explanation analysis reveals the influence and interrelationships of various input factors on the compressive strength of RAC, aiding design and construction professionals in optimizing raw material content during the RAC design and production process.