This paper proposes an approach to calculate the head difference at two sides of suspended waterproof curtains during multi-grade dewatering.The seepage during the dewatering process can be subdivided into three regio...This paper proposes an approach to calculate the head difference at two sides of suspended waterproof curtains during multi-grade dewatering.The seepage during the dewatering process can be subdivided into three regions:(i)seepage in pit,(ii)seepage between cur-tains,and(iii)seepage outside the pit.The flow rate of the first region is equal to the pumping rate,and the flow rate of the second and third regions can be obtained by numerical analysis.A numerical model is established to simulate the seepage in the second and third regions and its performance is validated by using the measured data of a series of field tests.The flow rate of each region is then used to derive formulae for the head difference in conventional dewatering,which can be used to determine the head difference at two sides of each waterproof curtain during multi-grade dewatering.The proposed formula expresses the head difference as a function of the relative depth of the curtain inserted into the confined aquifer,the thickness of the aquifer,the distance between two curtains,and the anisotropy of the hydraulic conductivity of the aquifer.The proposed numerical approach is further validated by using data derived from numerical analysis.The validation results demonstrated that the predictions of the proposed approach are acceptable and convenient.展开更多
传统离线数据分析方法对于处理即时性高和流量大的数据存在缺陷,而在线检测模型可以满足数据流分析的实时性要求。文中提出了一种基于多阈值模板的在线检测方法。该方法结合多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Sm...传统离线数据分析方法对于处理即时性高和流量大的数据存在缺陷,而在线检测模型可以满足数据流分析的实时性要求。文中提出了一种基于多阈值模板的在线检测方法。该方法结合多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Smirnov,TSTKS)算法进行在线检测,基于突变点密度更新窗口长度从而提高了突变点检测精度。采用等量分级策略实现对时序数据的自学习、匹配和分类,进而对大规模病变数据进行状态检测和预测。仿真实验和病变数据的实验结果表明,所提方法具有效果高、分类准确等优点,为大规模时序数据进行快速分类研究提供了新方法。展开更多
基金the National Natural Science Foundation for Surface Project of China(Grant Nos.51878157,41572273)the Jiangsu Natural Science Foundation,China(Grant No.BK20181282).
文摘This paper proposes an approach to calculate the head difference at two sides of suspended waterproof curtains during multi-grade dewatering.The seepage during the dewatering process can be subdivided into three regions:(i)seepage in pit,(ii)seepage between cur-tains,and(iii)seepage outside the pit.The flow rate of the first region is equal to the pumping rate,and the flow rate of the second and third regions can be obtained by numerical analysis.A numerical model is established to simulate the seepage in the second and third regions and its performance is validated by using the measured data of a series of field tests.The flow rate of each region is then used to derive formulae for the head difference in conventional dewatering,which can be used to determine the head difference at two sides of each waterproof curtain during multi-grade dewatering.The proposed formula expresses the head difference as a function of the relative depth of the curtain inserted into the confined aquifer,the thickness of the aquifer,the distance between two curtains,and the anisotropy of the hydraulic conductivity of the aquifer.The proposed numerical approach is further validated by using data derived from numerical analysis.The validation results demonstrated that the predictions of the proposed approach are acceptable and convenient.
文摘传统离线数据分析方法对于处理即时性高和流量大的数据存在缺陷,而在线检测模型可以满足数据流分析的实时性要求。文中提出了一种基于多阈值模板的在线检测方法。该方法结合多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Smirnov,TSTKS)算法进行在线检测,基于突变点密度更新窗口长度从而提高了突变点检测精度。采用等量分级策略实现对时序数据的自学习、匹配和分类,进而对大规模病变数据进行状态检测和预测。仿真实验和病变数据的实验结果表明,所提方法具有效果高、分类准确等优点,为大规模时序数据进行快速分类研究提供了新方法。
