首页|Hebei University of Technology Researcher Yields New Findings on Machine Learnin g (Elemental Design of Alkali-Activated Materials with Solid Wastes Using Machin e Learning)
Hebei University of Technology Researcher Yields New Findings on Machine Learnin g (Elemental Design of Alkali-Activated Materials with Solid Wastes Using Machin e Learning)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting out of Tianjin, Pe ople's Republic of China, by NewsRx editors, research stated, "Understanding the strength development of alkali-activated materials (AAMs) with fly ash (FA) and granulated blast furnace slag (GBFS) is crucial for designing high-performance AAMs."Financial supporters for this research include Natural Science Foundation of Chi na; S&T Program of Hebei; Education Department of Hebei Province; H ebei University of Technology. Our news journalists obtained a quote from the research from Hebei University of Technology: "This study investigates the strength development mechanism of AAMs using machine learning. A total of 616 uniaxial compressive strength (UCS) data points from FA-GBFS-based AAM mixtures were collected from published literature to train four tree-based machine learning models. Among these models, Gradient Boosting Regression (GBR) demonstrated the highest prediction accuracy, with a c orrelation coefficient (R-value) of 0.970 and a root mean square error (RMSE) of 4.110 MPa on the test dataset. The SHapley Additive exPlanations (SHAP) analysi s revealed that water content is the most influential variable in strength devel opment, followed by curing periods. The study recommends a calcium-to-silicon ra tio of around 1.3, a sodium-to-aluminum ratio slightly below 1, and a silicon-to -aluminum ratio slightly above 3 for optimal AAM performance."
Hebei University of TechnologyTianjinPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learn ing
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