首页|Usage of machine learning methods for forecasting the strength of environmentally friendly geopolymer concrete
Usage of machine learning methods for forecasting the strength of environmentally friendly geopolymer concrete
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NETL
NSTL
Abstract The building industry, which is a major contributor to greenhouse gas emissions, is under increasing pressure due to growing worries over the impact of climate change on communities. Geopolymer concrete (GPC) has emerged as a feasible alternative for construction materials owing to the environmental concerns linked to cement manufacture. The findings of this study contribute to the advancement of machine learning methods for determining the properties of environmentally friendly concrete. This kind of concrete has the potential to replace traditional concrete and reduce carbon dioxide emissions in the building industry. In the current study, when ground granulated blast-furnace slag (GGBS) is substituted with natural zeolite (NZ), silica fume (SF), and varying NaOH concentrations, the compressive strength (fc) of GPC is estimated using integrated analysis. A complete compilation of experimental testing conducted on GPC specimens was gathered from various sources, resulting in a total of 254 data sets. For this aim, support vector regression (SVR) analysis was considered, integrated with Arithmetic optimization algorithm (AOA) (ASVR), Bald eagle search optimization algorithm (BESO) (BSVR) and Henry Gas Solubility Optimization (HGSO) (HSVR). Along with this, the Multivariate adaptive regression splines (MARS) method was developed for determining an equation between inputs and output. The integration of these methods led to significant enhancements in predictive accuracy, surpassing existing models. Notably, the BSVR approach demonstrated remarkable improvements in precision and consistency, outperforming other frameworks across statistical metrics, error distribution, and Taylor diagram analysis. This study marks a substantial advancement in machine learning-driven optimization for sustainable concrete, with BSVR proving to be the most reliable and effective model for predicting the compressive strength of GPC. These improvements offer valuable insights for further reducing environmental impacts in the construction industry.
NETL
NSTL
