Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data
Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data
复制标题
基于异构现场数据的隧道掘进机（TBM）动载荷预测
DOI：
10.1016/j.autcon.2018.03.030
复制
发表时间：
2018-08-01
影响因子：
10.3
通讯作者：
Song, Xueguan
中科院分区：
文献类型：
--
作者：
Sun, Wei;Shi, Maolin;Song, Xueguan
隧道掘进机的载荷预测对于这些复杂工程系统的设计和安全运行至关重要。然而，迄今为止，研究大多仅使用地质数据，但隧道掘进机的操作也有重要的影响，特别是其动态行为的负载。随着量测技术的发展，隧道开挖过程中需要获得大量的运营数据。挖掘这些异构的现场数据，包括地质数据和运行数据，有望提高预测精度，实现负荷的动态预测。提出了一种基于异构现场数据和数据驱动技术的电力系统动态负荷预测方法。在该方法中，异构原位数据的集成如下进行：i）使用插值方法扩展地质数据以匹配操作数据的规模; ii）通过提出的编码方法融合分类数据和数值数据;以及iii）根据每个操作基准的位置将地质数据与操作数据组合。采用数据驱动的随机森林技术，建立了基于集成异构现场数据的预测模型。将该方法应用于中国某隧道的非均质现场TBM数据采集，结果表明，该方法不仅能准确预测荷载的动态行为，而且能准确估计荷载的统计特性。这项工作还突出了数据驱动技术在设计和分析类似于隧道掘进机的其他复杂工程系统中的适用性和潜力。
Load prediction of tunnel boring machines (TBMs) is crucial for the design and safe operation of these complex engineering systems. However, to date, studies have mostly used only geological data, but the operation of TBMs also has an important effect on the load, especially its dynamic behavior. With the development of measurement techniques, large amounts of operation data are obtained during tunnel excavation. Mining these heterogeneous in-situ data, including geological data and operation data, is expected to improve the prediction accuracy and to realize dynamic predictions of the load. In this paper, a dynamic load prediction approach is proposed based on heterogeneous in-situ data and a data-driven technique. In this approach, the integration of heterogeneous in situ data is conducted as follows: i) the geological data are extended to match the scale of the operation data using an interpolation method; ii) the categorical data and numerical data are fused through a proposed encoding method; and iii) the geological data are combined with the operation data according to the location of each operation datum. A data-driven technique, Random forest, is used to construct the prediction model based on the integrated heterogeneous in-situ data. The approach is applied to a collection of heterogeneous in-situ TBM data from a tunnel in China, and the results indicate that the approach can not only accurately predict the dynamic behaviour of the load but can also precisely estimate the statistical characteristics of the load. This work also highlights the applicability and potential of data-driven techniques in the design and analysis of other complex engineering systems similar to TBMs.