Usage of Optimized Least Square SVR to Volume Expansion Estimation of Cement Paste Including Fly Ash and Mgo Expansive Additive.

The limited hydration capacity and challenges related to delayed expansion prevent fly ash (FA) and MgO expansive additive (MEA) from being used significantly. Nonetheless, utilizing these two procedures in hydraulic mass concrete applications is a frequently used approach that vields favorable outcomes. To construct and assess machine learning-based algorithms to assess the volume expansion (V_e) of cement paste, which consists of FA and MEA, 170 experimental findings from published studies are employed. A novel approach called least square support vector regression (LSSVR) has been developed. The efficacy of LSSVR is significantly impacted by its hyperparameters (c and g), which were fine-tuned using the Dwarf Mongoose Optimization Algorithm (DMOA) and the Equilibrium Optimization Algorithm (EOA). Based on the results obtained, it can be inferred that there exists a significant potential for both LSSVR_E and LSSVR_D models to accurately predict the V_e of cement paste that incorporates fly ash and MgO expansive addition. In the training and testing phases, the Theil inequality coefficient (TIC) values for LSSVR_E are observed to be 0.0906 and 0.01043, which are comparatively higher than the TIC values for LSSVR_D, which are 0.0382 and 0.0044, respectively. By predicting the volume expansion accurately, engineers can adjust the proportions of FA and MEA to achieve desired expansion properties, improving the durability and stability of concrete structures. Accurate prediction models allow for better control of thermal stresses, reducing the risk of thermal cracking and extending the structure's lifespan. [ABSTRACT FROM AUTHOR]