首页|Research from School of Architecture Broadens Understanding of Support Vector Ma chines (Prediction Model for Strength of Fly Ash Concrete in Resourceful Utiliza tion)
Research from School of Architecture Broadens Understanding of Support Vector Ma chines (Prediction Model for Strength of Fly Ash Concrete in Resourceful Utiliza tion)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on . According to news originating from Henan, People’s Republic of China, by NewsRx correspondents, research stated, “This is an article in the field of ceramics an d composites. To achieve the resource utilization of fly ash and accurately asse ss the compressive strength of fly ash concrete, three predictive models for com pressive strength were constructed using machine learning modeling techniques, i ncluding traditional linear regression, decision tree, and support vector machin e models.” Our news reporters obtained a quote from the research from School of Architectur e: “These models were utilized to model and analyze the compressive performance of the concrete. Firstly, a corresponding experimental database was established, with seven input parameters including cement, fly ash, water reducer, coarse ag gregate, fine aggregate, water, and curing age, and the compressive strength as the output parameter. Based on 10-fold cross-validation, the performance of the three models on the training set was evaluated using root mean square error (RMS E), mean absolute error, and correlation coefficient, and their performance on t he test set was compared. The results showed that curing age had a high correlat ion with compressive strength (0.60), and the correlation of fly ash with compre ssive strength was higher than that of cement. The traditional linear regression model exhibited an RMSE of 7.27 and 5.91 on the training and test sets, respect ively. The decision tree model showcased an RMSE of 2.72 and 9.23 on the respect ive sets, while the support vector machine model yielded an RMSE of 5.34 and 4.0 9.”
School of ArchitectureHenanPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningSupport Vector MachinesVector Machines
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