Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete.

Concrete constructed using recycled aggregates in place of natural aggregates is an efficient approach to increase the construction sector's sustainability. To improve recycled aggregate concrete (RAC) technologies in permafrost, it is essential to certify the stability in frost‐induced conditions. The main goal of this study was to use support vector regression (SVR) methods to forecast the frost durability (DF) of RAC on the basis of durability agent value in cold climates. Herein, three optimization methods called Ant lion optimization (ALO), Grey wolf optimization (GWO), and Henry Gas Solubility Optimization (HGSO) were employed for indicating optimal values of SVR key parameters. The results depicted that all developed models have capability in predicting the DF of RAC in cold regions. The values of OBJ as a comprehensive index depicted that the GWO−SVR model has the higher value at 0.0571 as the weakest model, then ALO−SVR at 0.0312 recognized as the second‐highest model, and finally the HGSO−SVR system at 0.0224 mentioned as outperformed model. ALO−SVR and GWO−SVR approaches were likewise capable of accurately forecasting the DF of RAC in cold regions, but the created HGSO−SVR method outperformed them all when taking into account the explanations and justifications. [ABSTRACT FROM AUTHOR]