首页|Data on Machine Learning Discussed by Researchers at Qingdao University of Technology (Quantification of the Concrete Freezethaw Environment Across the Qinghai-tibet Plateau Based On Machine Learning Algorithms)
Data on Machine Learning Discussed by Researchers at Qingdao University of Technology (Quantification of the Concrete Freezethaw Environment Across the Qinghai-tibet Plateau Based On Machine Learning Algorithms)
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Data detailed on Machine Learning have been presented. According to news reporting out of Qingdao, People’s Republic of China, by NewsRx editors, research stated, “The reasonable quantification of the concrete freezing environment on the Qinghai-Tibet Plateau (QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze-thaw cycles (NFTCs) on the QTP.” Funders for this research include Natural Science Foundation of Shandong Province, Key Research and Development Project in Shandong Province, Project for excellent youth foundation of the innovation teacher team, Shandong. Our news journalists obtained a quote from the research from the Qingdao University of Technology, “The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest (RF) model, generalized boosting method (GBM), generalized linear model (GLM), and generalized additive model (GAM), are used to fit the NFTCs. The root mean square error (RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R2 values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R2 values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP.”
QingdaoPeople’s Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningQingdao University of Technology
NETL
NSTL
Science Press
