首页|New Findings on Support Vector Machines from Shaanxi Railway Institute Summarized (Fresh State and Strength Performance Evaluation of Slag-based Alkali-activated Concrete Using Soft-computing Methods)
New Findings on Support Vector Machines from Shaanxi Railway Institute Summarized (Fresh State and Strength Performance Evaluation of Slag-based Alkali-activated Concrete Using Soft-computing Methods)
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NETL
NSTL
Elsevier
A new study on Machine Learning - Support Vector Machines is now available. According to news reporting originating in Weinan, People's Republic of China, by NewsRx journalists, research stated, "In this study, machine learning prediction models for the slump (SL) and compressive strength (CS) of alkaliactivated concrete (AAC) were developed. Extreme gradient boosting (XGB) as an ensemble and support vector machine (SVM) as individual methods were chosen." Financial supporters for this research include Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia, Deanship of Scientific Research at Najran University, Shaanxi Railway Institute Scientific Research Fund Project: Application Research of Locked Steel Pipe Pile Cofferdam in Underwater Bridge Pier Construction. The news reporters obtained a quote from the research from Shaanxi Railway Institute, "To evaluate the performance of the models, the Taylor diagram, k-fold validation, and statistical tests were performed. Moreover, to determine the significance of features, a SHapley Additive exPlanations (SHAP) study was carried out. XGB outperformed SVM considerably in predicting the SL and CS of AAC. XGB outperformed SVM in terms of R2 (0.94 for SL and 0.97 for CS), which was 0.86 and 0.88, respectively. Precursor content had the greatest effect on the SL of AAC, followed by blast furnace slag ratio, test time, SiO2/Na2O, and quantities of NaOH, aggregate, and water, according to the results of the SHAP study. The SHAP investigation revealed that curing time had the greatest effect on the CS of AAC, followed by SiO2/Na2O, NaOH quantity, precursor content, aggregate quantity, blast furnace slag ratio, and water quantity."
WeinanPeople's Republic of ChinaAsiaMachine LearningSupport Vector MachinesShaanxi Railway Institute
NETL
NSTL
Elsevier
