Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

Physics-assisted machine learning methods for predicting the splitting tensile strength of recycled aggregate concrete

Recycled aggregate concrete (RAC) has become a popular building material due to its eco-friendly features, but the difficulty in predicting the crack resistance of RAC is increasingly impeding its application. In this study, splitting tensile strength is adopted to describe the crack resistance ability of RAC, and physics-assisted machine learning (ML) methods are used to construct the predictive models for the splitting tensile strength of RAC. The results show that the AdaBoost model has excellent predictive performance with the help of the Firefly algorithm, and physical assistance plays a remarkable role in selecting features and verifying the ML models. Due to the limit in data size and the generalizability of the model, the dataset should be supplemented with more representative data, and an algorithm for small sample sizes could be studied in the future.

Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

In this research, we present an efficient implementation of machine learning (ML) models that forecast the mechanical properties of basalt fiber-reinforced high-performance concrete (BFHPC). The objective of the present study was to predict compressive, flexural, and tensile strengths of BFHPC through ML techniques and propose some correlations between these properties. Moreover, the modulus of elasticity (ME) values and compressive stress-strain curves were simulated using ML techniques. In this regard, three predictive algorithms, including linear regression (LR), support vector regression (SVR), and polynomial regression (PR), were considered. LR, SVR, and PR were utilized to forecast the compressive, flexural, and tensile strengths of BFHPC, and the PR technique was employed to simulate the compressive stress-strain curves. The performance of the models was also determined by the coefficient of determination (R²), mean absolute errors (MAE), and root mean square errors (RMSE). According to the obtained values of R², MAE, and RMSE, the performance of PR was better than other types of algorithms in estimating the compressive, tensile, and flexural strengths. For example, R² values were 0.99, 0.94, and 0.98 in predicting the compressive, flexural, and tensile strengths using PR, respectively. This shows the higher accuracy and reliability of the PR technique compared with other predictive algorithms. Finally, we concluded that ML techniques can be appropriately applied to assess the mechanical characteristics of BFHPC.