• 首页
  • 期刊介绍
  • 编委会
  • 征稿启事
  • 期刊订阅
  • 相关下载
  • 联系我们
再生骨料透水混凝土关键性能统计及预测分析
Key Performance Statistics of Recycled Aggregate Pervious Concrete and Prediction Analysis
投稿时间:2018-07-21  修订日期:2018-11-07
DOI:103969/j.issn.1007 9629201902008
中文关键词:  再生骨料透水混凝土  正态分布  BP网络模型  预测分析  强度  渗透性
英文关键词:recycled aggregate pervious concrete(RAPC)  normal distribution  backpropagation(BP) network model  predictive analysis  strength  permeability
基金项目:国家自然科学基金青年科学基金资助项目(51309101);国家自然科学基金面上项目(51679092);河南省重大科技攻关项目(172102210372);河南省产学研合作项目(182107000031)
              
作者单位
陈守开
CHEN Shoukai
华北水利水电大学水利学院,河南郑州450045
School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
陈家林
CHEN Jialin
华北水利水电大学水利学院,河南郑州450045
School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
汪伦焰
WANG Lunyan
华北水利水电大学水利学院,河南郑州450045
School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
李海瑞
LI Hairui
华北水利水电大学管理与经济学院,河南郑州450045
School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
郭磊
GUO Lei
华北水利水电大学水利学院,河南郑州450045
School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
摘要点击次数: 71
全文下载次数: 0
中文摘要:
      对再生骨料透水混凝土(RAPC)4项关键性能指标(抗压强度、劈拉强度、孔隙率及透水系数)进行了统计分析,发现这4项性能指标均基本服从正态分布规律;同时建立了RAPC宏观性能的统计规律与内在联系.在此基础上,基于人工神经网络方法,运用Python软件建立了基于BP神经网络的RAPC性能预测模型,并对上述关键性能指标进行了相互预测分析.结果表明:4项性能指标的模型预测值平均相对误差均在10%以内,预测精度较高,表明RAPC的透水性能与强度性能之间具有内在的反向关联关系,并具备可预测性.
英文摘要:
      The statistical analysis of 4key performance indicators(compressive strength, splitting tensile strength, porosity and water permeability) of recycled aggregate pervious concrete(RAPC) revealed that these indicators basically obey the normal distribution pattern; based on this, the statistical characteristics and the intrinsic relation of its macroscopic properties were established. On this basis, and based on the artificial neural network method, the backpropagation(BP) network model was established by means of Python software. The model was used to conduct interactive prediction analysis on the key performance indicators of the concrete; the relative error of the average predicted values is within 10%. This indicates that the application of the method can achieve higher accuracy in performance prediction; it indicates that there is an inherent inverse relationship between the permeability and strength properties of RAPC and it is predictable.
查看全文  查看/发表评论  下载PDF阅读器
关闭