LIU Guoguang,,et al.Compactness prediction of airport soil field based on artificial neural network[J].Journal of Shenzhen University Science and Engineering,2021,38(1):54-60.[doi:10.3724/SP.J.1249.2021.01054]





Compactness prediction of airport soil field based on artificial neural network
1)中国民航大学机场学院,天津 300300
2)中国科学院西北生态环境资源研究院冻土工程国家重点实验室,甘肃兰州 730026
3)中国科学院西北生态环境资源研究院,甘肃兰州 730026
4)成都双流国际机场飞行区管理部,四川成都 610030
LIU Guoguang1 2 3 PEI Leiyang1 YANG Yuemin1 and LI Shinan1 4
1) Airport College, Civil Aviation University of China, Tianjin 300300, P.R.China
2) State Key Laboratory of Frozen Soils Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730026, Gansu Province, P.R.China
3) Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730026, Gansu Province, P.R.China
4) Airfield Management Department, Chengdu Shuangliu International Airport, Chengdu 610030, Sichuan Province, P.R.China
geotechnical engineering mitigation and protection engineering highway and railway engineering compactness prediction model artificial neural network airport soil field
为提高机场土面区安全动态预测及保障能力,结合某机场为期6 a的土面区安全评估数据,建立基于人工神经网络(artificial neural network, ANN)的压实度预测模型.选取天然密度(natural density, ND)、实测含水率(actual water content, AW)、最优含水率(optimal water content, OW)、降水状况(rainfall condition, RC)和压实状况(compaction condition, CC)作为输入向量,以双曲正切S型传输函数作为传递函数,利用400组实测数据完成模型训练后,用随机抽取的100组测试数据对模型进行精确校验,通过纳什效率系数(Nash-Sutcliffe efficiency coefficient, NSE)分析ANN模型的预测能力,并进行工程应用验证.结果表明,充分训练后ANN模型的均方差为0.98,NSE计算值为0.89,可有效预测土面区压实度.机场对比试验结果表明,大部分测区预测误差在±5%之内,仅有1个样本误差为15%,NSE计算值为0.86,达到了工程应用精度.采用影响因素分析法优化ANN模型发现,ND和AW是影响压实度最重要的因素,管理部门可通过严格控制回填土级配和加强排水措施有效改善土面区安全性.
In order to improve the dynamic prediction and management capabilities of soil field safety in airfield, a new artificial neural network (ANN) model was established based on the evaluation data of six years in an airport.By factors analysis of the actual soil field of the airport, natural density (ND), actual water content (AW), optimal water content (OW), rainfall condition (RC) and compaction condition (CC) were chosen as the input data, and hyperbolic tangent sigmoid function was set as the transfer function.The network was trained by 400 sets of data and validated for its accuracy by 100 sets of data selected randomly from the database.The prediction capability of achieved ANN model was analyzed by Nash-Sutcliffe efficiency coefficient (NSE) method.And engineering application had been done in another airport.The results show that soil field compactness can be effectively predicted by well-trained ANN model with R-Squared of 0.98 and NSE of 0.89.The outcomes of validation test in another airport prove that the errors of most sample zones are between -5% and 5%, with only one exception of 15%, with calculated NSE of 0.86, which satisfies the requirements of engineering application.By optimization of ANN model with factor analysis method, it indicates that ND and AW are the controlling factors of model compactness prediction, and the best ways of improving the safety of soil field in airport are soil gradation and drainage control strictly.


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Foundation:Fundamental Scientific Research Project of Central Universities(3122018C013);Science and Technology Commissioner Project of Tianjin Enterprise(19JCTPJC53800);Scientific Research Project of Tianjin Municipal Education Commission(2018KJ243)
Corresponding author:Associate professor LIU Guoguang.Email: ggliu@cauc.edu.cn
Citation:LIU Guoguang,PEI Leiyang,YANG Yuemin,et al.Compactness prediction of airport soil field based on artificial neural network[J]. Journal of Shenzhen University Science and Engineering, 2021, 38(1): 54-60.(in Chinese)
引文:刘国光,裴磊洋,杨跃敏,等.基于人工神经网络的机场土面区压实度预测[J]. 深圳大学学报理工版,2021,38(1):54-60.
更新日期/Last Update: 2021-01-26