摘要
已有的连续压实质量评价指标在评估堆石坝料的压实质量时仍存在评价精度低、表征压实效果复杂以及结果易受压实材料属性影响等缺点。为给堆石坝施工质量的连续控制提供有效指标,本文采用数据延拓式相关的相位差求解方法来间接获取碾压波速(V_(R)),提出了以实时监测的V_(R)作为堆石坝料压实状态的表征指标。从定性分析角度考虑碾压参数对V_(R)的影响,并在此基础上,采用人工神经网络构建了碾压参数与V_(R)之间的定量关系模型。实际工程应用表明,建立的神经网络模型具有较高的预测精度,V_(R)可由碾压参数精确表征,间接验证了V_(R)可作为堆石料压实状态实时表征指标的可行性。本文所提出的坝料压实质量评价指标不仅可为堆石料压实质量的“过程控制”提供新的途径,而且可以为进一步研究利用碾压波速来表征堆石料压实密度提供了基础。
The existing continuous compaction quality evaluation indexes have numerous shortcomings,such as low evaluation accuracy,complex characterization of compaction effect,and being easy to be affected by the properties of compaction materials.To provide an effective index for the continuous control of rockfill dam compaction quality,this paper adopts an improved correlation method for phase difference measure⁃ment based on data extension to obtain the rolling wave velocity(V_(R)),and puts forward the rolling wave ve⁃locity(V_(R))as the real-time monitoring index of compaction state of rockfill materials.Then,the impact of compaction parameters on V_(R) is considered from the perspective of qualitative analysis.On this basis,a quantitative relationship model between compaction parameters and V_(R) is established by using artificial neu⁃ral network.The practical engineering application shows that the established neural network model has supe⁃rior prediction accuracy,indicating that V_(R) can be accurately represented by compaction parameters,which indirectly verifies the feasibility of V_(R) as a real-time index for compaction quality evaluation of rockfill mate⁃rials.The compaction quality evaluation index proposed in this paper can not only provide a new way for the"process control"of the compaction quality of rockfill materials,but also provides a basis for further re⁃search on the use of rolling wave velocity to characterize the compaction density of rockfill materials.
作者
刘彪
赵宇飞
陈祖煜
王文博
刘必旺
朱丙龙
LIU Biao;ZHAO Yufei;CHEN Zuyu;WANG Wenbo;LIU Biwang;ZHU Binglong(Department of Geotechnical Engineering,China Institute of Water Resources and Hydropower Research,Beijing 100048,China;Sinohydro Bureau 6 Co.,Ltd.,Shenyang,110179,China;Sinohydro Bureau 8 Co.,Ltd.,Changsha,410007,China;School of Transportation Science and Engineering,Beihang University,Beijing 100191,China)
出处
《中国水利水电科学研究院学报（中英文）》
北大核心
2022年第1期20-29,共10页
Journal of China Institute of Water Resources and Hydropower Research
基金
中国水科院三型人才专项项目(GE0145B88201)。
关键词
堆石料
碾压参数
碾压波速
实时监测
神经网络
rockfill materials
compaction parameters
rolling wave velocity
real-time monitoring
neural network
