基于主成分分析和神经网络的马歇尔试验模型
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摘要
为解决沥青混合料马歇尔试验存在的试验周期长、材料浪费等问题,文中基于主成分分析(PAC)、BP和RBF神经网络算法提出了一种沥青混合料马歇尔试验模型。首先,使用PCA对沥青混合料的合成级配参数和油石比进行变量降维,得到5个主成分;然后,将5个主成分作为神经网络模型的输入变量,稳定度和流值作为模型的输出变量,对模型进行训练;最后,将模型输出结果与实验室试验结果进行对比,验证模型的有效性。结果表明,BP神经网络对稳定度和流值输出的平均相对误差为5.19%和2.61%,RBF神经网络为4.95%和6.67%;BP和RBF神经网络运行时间分别为0.557s和0.962s。
In order to solve the problems of long test period and material waste in asphalt mixture Marshall test,in the paper,a Marshall test model of asphalt mixture is proposed based on principal component analysis(PAC),BP and RBF neural network algorithm.First,the PCA is used to reduce the dimensionality of the synthetic gradation parameters and the oil-stone ratio of the asphalt mixture to obtain five principal components.Then,the five principal components are used as the input variables of the neural network model,and the stability and flow values are used as the output variables of the model to train the model.Finally,the model output is compared with the laboratory test results to verify the validity of the model.The results show that the average relative error of BP neural network for stability and flow value output is 5.19% and 2.61%,RBF neural network is 4.95% and 6.67%;BP and RBF neural network running time are 0.557 sand 0.962 srespectively.
In order to solve the problems of long test period and material waste in asphalt mixture Marshall test,in the paper,a Marshall test model of asphalt mixture is proposed based on principal component analysis(PAC),BP and RBF neural network algorithm.First,the PCA is used to reduce the dimensionality of the synthetic gradation parameters and the oil-stone ratio of the asphalt mixture to obtain five principal components.Then,the five principal components are used as the input variables of the neural network model,and the stability and flow values are used as the output variables of the model to train the model.Finally,the model output is compared with the laboratory test results to verify the validity of the model.The results show that the average relative error of BP neural network for stability and flow value output is 5.19% and 2.61%,RBF neural network is 4.95% and 6.67%;BP and RBF neural network running time are 0.557 sand 0.962 srespectively.
引文
[1]颜舜,邓泰,李闯民.马歇尔试件制作过程中几个问题的探讨[J].湖南交通科技,2011,37(4):36-39.
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[11]刘洋.基于PLS和SVR的水质预测模型研究[D].昆明:昆明理工大学,2017.
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[2]赵宇.神经网络在沥青混合料马歇尔稳定度和流值预测中的应用[J].黑龙江科技信息,2003,(4):88.
[3]李清富,梁博,刘晨辉.人工神经网络在SMA配合比设计中的应用[J].河南科学,2008,(2):208-211.
[4]孙益民,钟明,徐玲,等.基于神经网络的沥青混合料配合比设计[J].公路交通科技,2012,29(2):40-45.
[5]薛薇.SPSS统计分析方法及应用:第二版[M].北京:电子工业出版社,2009.
[6]彭福来.基于光电容积脉搏波描记法的无创血红蛋白浓度检测技术的研究[D].北京:北京理工大学,2016.
[7]罗凡.基于原油实时性质的常减压蒸馏装置数据处理和优化研究[D].南京:东南大学,2017.
[8]郁磊,史峰,王辉,等.MATLAB智能算法30个案例分析:第二版[M].北京:北京航空航天大学出版社,2015:8.
[9]秦富童,岳丽华,万寿红.应用BP神经网络的目标识别效果评估[J].计算机工程与应用,2010,46(5):148-150+156.
[10]石岩.排气消声器消声特性仿真分析与排气噪声声音品质设计[D].天津:天津大学,2010.
[11]刘洋.基于PLS和SVR的水质预测模型研究[D].昆明:昆明理工大学,2017.
[12]李大龙.基于RBF神经网络的政府公共建设项目投资估算方法研究[D].赣州:江西理工大学,2011.