基于人工智能的超高性能混凝土配合比优化设计研究
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Ehsan G, Mojtaba B, Hugo C, et al. Prediction offresh and hardened state properties of UHPC: compar-ative study of statistical mixture design and an artifi-cial neural network model[J]. Journal of Materials inCivil Engineering, 2015, 27(11): No. 4015017.
吴贤国,陈彬,杨赛,等.基于RF-NSGA-Ⅱ算法的高耐久性混凝土配合比优化研究[J].工业建筑, 2021,51(7): 156-161.
肖祁南.基于人工智能的混凝土配合比多目标优化研究[D].广州:广州大学, 2022.
VAPNIK V N, CHERVONENKIS A. A note on one class of perceptrons[J]. Automationand Remote Control, 1964,25(1).
LIAW A, WIENER M. Classification and regression by randomForest. R News 2:18-22[J].2001.
Lundberg S , Lee S I .A Unified Approach to Interpreting Model Predictions[J]. 2017.DOI:10.48550/arXiv.1705.07874.
KENNEDY J. Particle swarm optimization[J]. Proc. of 1995 IEEE Int. Conf. NeuralNetworks, (Perth, Australia), Nov. 27-Dec., 2011,4(8): 1942-1948.表 3.2-1 配合比优化结果试验编号C(kg/m3)SF(kg/m3)S(kg/m3)FA(kg/m3)w/bQP(kg/m3)V-01656.4115.8562.1760.203175.1V-02726.3155.4521.895.60.192196.5V-03754.1182.5492.5124.80.171225.6试验编号Fi(kg/m3)SP(kg/m3)Sa(kg/m3)(MPa)(MPa)()V-01152.529.51150.8122.5715.31687.6V-02184.631.8955.2151.6921.04496.52V-03207.736.6904.5182.0725.71358.17
DOI: http://dx.doi.org/10.12345/gcjsygl.v9i20.33380
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