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    题名 作者 年代 出处 被引量
1Predicting the International Roughness Index of JPCP and CRCP Rigid Pavement:A Random Forest(RF)Model Hybridized with Modified Beetle Antennae Search(MBAS)for Higher Accuracy显示文摘To improve the prediction accuracy of the International Roughness Index(IRI)of Jointed PlainConcrete Pavements(JPCP)and Continuously Reinforced Concrete Pavements(CRCP),a machine learning approach is developed in this study for the modelling,combining an improved Beetle Antennae Search(MBAS)algorithm and Random Forest(RF)model.The 10-fold cross-validation was applied to verify the reliability and accuracy of the model proposed in this study.The importance scores of all input variables on the IRI of JPCP and CRCP were analysed as well.The results by the comparative analysis showed the prediction accuracy of the IRI of the newly developed MBAS and RF hybrid machine learning model(RF-MBAS)in this study is higher,indicated by the RMSE and R values of 0.2732 and 0.9476 for the JPCP as well as the RMSE and R values of 0.1863 and 0.9182 for the CRCP.The accuracy of this obtained result far exceeds that of the IRI prediction model used in the traditional Mechanistic-Empirical Pavement Design Guide(MEPDG),indicating the great potential of this developed model.The importance analysis showed that the IRI of JPCP and CRCP was proportional to the corresponding input variables in this study,including the total joint faulting cumulated per KM(TFAULT),percent subgrade material passing the 0.075-mm Sieve(P_(200))and pavement surface area with flexible and rigid patching(all Severities)(PATCH)which scored higher.Zhou Ji Mengmeng Zhou Qiang Wang Jiandong Huang 2024Computer Modeling in Engineering & Sciences2024,139,5:0
2GA-BP神经网络模型在岩爆烈度分类预测研究及应用显示文摘通过引入遗传算法优化BP神经网络,构建GA-BP神经网络模型。选取围岩最大切向力与岩石单轴抗压强度比(应力集中系数)、岩石单轴抗压强度与单轴抗拉强度比(脆性系数)和弹性能量指数作为输入指标,构建岩爆烈度分类预测体系。选取104组工程岩爆实例,其中84组作为训练集,20组作为测试集进行验证,结果表明,GA-BP神经网络模型的分类预测准确率能够达到95%,优于BP神经网络模型的80%。在工程试验中,GA-BP神经网络模型分类预测效果较好(准确率90%),可为岩爆烈度分类预测研究作为参考。滕涛 王国军 周伟胜 倪智伟 景杨凡 2023现代矿业2023,39,9:0
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