Light and normal weight concretes shear strength estimation using tree-based tunned frameworks

Abstract

Concrete component shear strength is a complex phenomenon that depends on a number of governing bodies and regulatory mechanisms. Moreover, the addition of new concrete variations like fibered concrete and light-weight concrete increases the complexity of the topic. Consequently, in order to estimate the shear resistance (Vu) of normal-weight concrete (NWC) and light-weight concrete (LWC) components, three machine learning-based methods were developed. The work gives researchers significant tools to predict concrete structural integrity. This helps design safer, more efficient buildings by accounting for complicated material qualities and concrete variants like fibered or lightweight concrete in real-world construction settings. Extreme gradient boosting (XGB) was taken into consideration for this aim. The Chaos Game Optimization (CGO), Giant Trevally Optimizer (GTO), and Mountain Gazelle Optimizer (MGO) algorithms were used in order to identify the hyperparameters effectively (abbreviated as XGBMGO,XGBGTO,andXGBCGO). The collected big data was divided into three subsets: training (75 %: 1194), validating (15 %: 256), and testing (15 %: 256). The XGBGTO, XGBMGOand XGBCGO algorithms have a considerable capacity to precisely forecast the Vuof NWC and LWC. The R2 values for theXGBCGO net were determined to be 0.9959, 0.9957, and 0.9961, respectively, throughout the training, verification, and testing phases. The reasons put forward generally suggest that the XGBCGO analysis can achieve more than the XGBGTO, and XGBMGOframeworks.