摘要
碾压混凝土施工压实质量对大坝成型质量至关重要。现有碾压热层压实质量一般通过核子密度仪法检测,存在检测繁琐、可靠度低、存在安全风险、需定期标定、代表性差等缺点,无法满足快速精准的检测要求。通过理论及试验研究,选取现场碾压料层的物性参数,即拌合料含湿率、骨料级配和碾压层应力波传播速度作为评价参数,并研发了相应的实时仓面含湿率测定仪与碾压热层波速测试仪对以上参数实时采集传输;利用BP神经网络建立基于碾压层拌合料含湿率、骨料级配和应力波传播速度的碾压混凝土压实度预测评价模型,通过远程可视化反馈系统将模型预测结果反馈输出,形成了一整套碾压混凝土施工热层实时压实度馈控技术。该技术在乌弄龙碾压混凝土大坝施工现场进行了实践,实时监控碾压热层的施工效果,验证了其可行性和可靠性。
The compaction quality of RCC construction is very important to dam forming quality.The existing compaction quality detection method of RCC layer is nuclear density instrument method,which has the disadvantages of complex operation,low reliability,safety risk,regular calibration and the lack of representativeness,and cannot meet the requirements of rapid and accurate detection.Through the theoretical and experimental research,the material parameters of the field compaction layers,that is,the moisture content of the mixing material,the gradation of aggregate and the propagation speed of the stress wave of the compaction layer were selected as the evaluation parameters in this paper,and the corresponding real-time warehouse surface moisture content tester and the roller thermal layer wave velocity tester were developed to collect and transmit the above parameters in real-time.And then a prediction and evaluation model for compaction of RCC based on moisture content,gradation of aggregate and propagation velocity of stress wave was established by using BP neural network.Through the remote visual feedback system,a whole set of real-time compaction feedback and control technology of thermal layer in RCC construction was formed.This technique was applied in the construction site of the Wunonglong RCC dam,and the construction effect of the RCC thermal layer was monitored in real time.
作者
郑祥
马元山
付勇
田正宏
ZHENG Xiang;MA Yuanshan;FU Yong;TIAN Zhenghong(Branch NO.1 of SINOHYDRO 7 Co.,Ltd,Meishan 620010,China;College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China)
出处
《人民长江》
北大核心
2020年第1期160-165,179,共7页
Yangtze River
关键词
碾压混凝土
压实质量
BP神经网络模型
实时馈控
含湿率
应力波波速
骨料级配
RCC
compaction quality
BP neural network
real-time feedback and control
moisture content
propagation velocity of stress wave
aggregate gradation
作者简介
郑祥,男,工程师,主要从事工程项目施工管理工作。E-mail:591997905@qq.com;通讯作者:马元山,男,博士研究生,研究方向为土木与水利工程新材料。E-mail:180402020006@hhu.edu.cn
