The use of neural networks for the prediction of cone penetration resistance of silty sands
作者: Yusuf ErzinNurhan Ecemis
作者单位: 1Celal Bayar University
2Izmir Institute of Technology
刊名: Neural Computing and Applications, 2017, Vol.28 (1), pp.727-736
来源数据库: Springer Journal
DOI: 10.1007/s00521-016-2371-z
关键词: Artificial neural networksCone penetration resistanceHorizontal coefficient of consolidationSilty sand
英文摘要: In this study, an artificial neural network (ANN) model was developed to predict the cone penetration resistance of silty sands. To achieve this, the data sets reported by Ecemis and Karaman, including the results of three high-quality field tests, namely piezocone penetration test, pore pressure dissipation tests, and direct push permeability tests performed at 20 different locations on the northern coast of the Izmir Gulf in Turkey, have been used in the development of the ANN model. The ANN model consisted of three input parameters (relative density, fines content, and horizontal coefficient of consolidation) and a single output parameter (normalized cone penetration resistance). The results obtained from the ANN model were compared with those obtained from the field tests. It is found...
原始语种摘要: In this study, an artificial neural network (ANN) model was developed to predict the cone penetration resistance of silty sands. To achieve this, the data sets reported by Ecemis and Karaman, including the results of three high-quality field tests, namely piezocone penetration test, pore pressure dissipation tests, and direct push permeability tests performed at 20 different locations on the northern coast of the Izmir Gulf in Turkey, have been used in the development of the ANN model. The ANN model consisted of three input parameters (relative density, fines content, and horizontal coefficient of consolidation) and a single output parameter (normalized cone penetration resistance). The results obtained from the ANN model were compared with those obtained from the field tests. It is found...
全文获取路径: Springer  (合作)
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来源刊物:
影响因子:1.168 (2012)

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关键词翻译
关键词翻译
  • penetration 穿透
  • resistance 抵抗
  • silty 淤泥的
  • neural 神经系统的
  • sands 沙滩
  • coefficient 系数
  • ANN All Numeral Numbering
  • parameters 参数
  • error 误差
  • employed 就业