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Time Series Analysis of Hydraulic Data for Automated Productivity Monitoring of Pilot Tube Microtunneling

机译:先导管微隧道自动化生产率监测的水力数据时间序列分析

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Monitoring and controlling construction productivity of pilot tube microtunneling (PTMT) are important in reducing delays of tunneling projects and in decreasing project costs. Collecting reliable and detailed productivity data in the field for effective PTMT productivity analysis, however, is challenging. Sensors attached to hydraulic devices of PTMT machines can automatically record time series of a boring machine's hydraulic forces during operations. These time series show cyclic patterns corresponding to cyclic PTMT operations in three stages of PTMT: (1) pilot tube installation, (2) casing installation, and (3) product pipe installation. Analyzing these time series manually for detailed productivity analysis is possible, but such manual analysis becomes tedious and error-prone. This paper presents a knowledge-based and adaptive time series analysis approach that can automatically detect cycles of construction activities from time series data and thus achieve real-time PTMT productivity analyses. This approach can tolerate noises in time series data collected in real PTMT projects and thus can adaptively adjust its parameters according to the characteristics of input data. Such adaptive capability enables engineers to apply this method to various time series collected in different PTMT sessions. The testing results in a PTMT project in Wisconsin showed that the proposed approach achieves 95% or better precision and recall on data collected during seven different sessions of PTMT construction. These data covered three phases of PTMT that use three different machines and pipeline sections under different environmental conditions in order to validate the developed algorithms. Productivity analyses results revealed that productivities on some sections almost doubled those on others and that eliminating anomalous cycles could result in up to 40% improvement in overall productivity. (C) 2015 American Society of Civil Engineers.
机译:监视和控制先导管微隧道(PTMT)的施工生产率对于减少隧道工程的延误和降低工程成本非常重要。然而,在现场收集可靠且详细的生产率数据以进行有效的PTMT生产率分析是一项挑战。连接到PTMT机器液压装置的传感器可以自动记录镗孔机在操作过程中液压力的时间序列。这些时间序列显示了与PTMT的三个阶段中的循环PTMT操作相对应的循环模式:(1)先导管安装,(2)套管安装和(3)产品管安装。可以手动分析这些时间序列以进行详细的生产率分析,但是这种手动分析变得乏味且容易出错。本文提出了一种基于知识的自适应时间序列分析方法,该方法可以从时间序列数据中自动检测施工活动的周期,从而实现实时PTMT生产率分析。这种方法可以容忍在实际PTMT项目中收集的时间序列数据中的噪声,因此可以根据输入数据的特性自适应地调整其参数。这种自适应功能使工程师能够将此方法应用于在不同PTMT会话中收集的各种时间序列。威斯康星州PTMT项目的测试结果表明,该提议的方法可以达到95%或更高的精度,并且可以在PTMT建设的七个不同阶段中收集的数据进行回忆。这些数据涵盖了PTMT的三个阶段,这些阶段在不同的环境条件下使用三个不同的机器和管道部分,以验证开发的算法。生产率分析结果表明,某些部门的生产率几乎是其他部门的两倍,消除异常周期可以使整体生产率提高40%。 (C)2015年美国土木工程师学会。

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  • 来源
    《Journal of Pipeline Systems Engineering and Practice》 |2016年第2期|04015022.1-04015022.12|共12页
  • 作者单位

    Arizona State Univ, Sch Sustainable Engn & Built Environm, POB 873005, Tempe, AZ 85287 USA;

    Arizona State Univ, Sch Sustainable Engn & Built Environm, POB 870204, Tempe, AZ 85287 USA;

    Staheli Trenchless Engineers, 1725 220th St SE,Suite C-200, Bothell, WA 98021 USA;

    Arizona State Univ, Sch Sustainable Engn & Built Environm, POB 873005, Tempe, AZ 85287 USA;

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