Detecting soil mixing, grain size distribution, and clogging potential of tunnel excavation face by classification-regression algorithms using EPBM operational data

IF 8.2 1区 工程技术 Q1 ENGINEERING, CIVIL
Sharmin Sarna, Marte Gutierrez
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引用次数: 0

Abstract

Earth pressure balance machine (EPBM) operation is sensitive to the properties of the excavated soil due to the requirements of proper soil conditioning and maintenance of necessary chamber pressure. However, soil properties are invariably only available at a limited number of borehole explorations and soil samplings conducted during the subsoil investigation. Thus, it is crucial to identify properties of the tunnel excavation face, such as clay-sand mixed conditions, grain size distributions, and clogging potential along the whole alignment beside the few borehole locations to attain optimally efficient EPBM operation. Therefore, this paper presents the development of machine learning prediction models (i.e., classifiers and regressors) to estimate the properties of the tunnel excavation face using EPBM operational data collected during the tunneling operation as input features. Geotechnical data collected from boreholes and soil samples can be used to validate prediction models and calibrate them. To develop such models, the Northgate Link Extension (N125) tunneling project, constructed in Seattle, Washington, the USA, is used to capture and identify the true ground conditions. The results indicate successful prediction performances by the models, providing indication that EPBM parameters are crucial pointers of the tunnel excavation face properties to help attain optimally efficient EPBM operation.
利用 EPBM 运行数据,通过分类-回归算法检测隧道开挖工作面的土壤混合情况、粒度分布和堵塞潜力
土压平衡机(EPBM)的运行对挖掘土壤的性质非常敏感,因为需要对土壤进行适当的调节并保持必要的腔室压力。然而,在底土勘察过程中,土壤特性往往只能通过有限的钻孔勘探和土壤取样获得。因此,除了少数几个钻孔位置外,确定隧道开挖面的特性(如粘沙混合状况、粒度分布和整个线路的堵塞潜力)对于实现 EPBM 的最佳高效运行至关重要。因此,本文介绍了机器学习预测模型(即分类器和回归器)的开发情况,利用在隧道开挖过程中收集到的 EPBM 运行数据作为输入特征来估计隧道开挖面的属性。从钻孔和土壤样本中收集的岩土工程数据可用于验证预测模型并对其进行校准。为了开发此类模型,我们使用了在美国华盛顿州西雅图市建造的北门连接线延伸段(N125)隧道项目来捕捉和识别真实的地层条件。结果表明,模型的预测性能非常成功,表明 EPBM 参数是隧道开挖面特性的关键指针,有助于实现 EPBM 的最佳高效运行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Underground Space
Underground Space ENGINEERING, CIVIL-
CiteScore
10.20
自引率
14.10%
发文量
71
审稿时长
63 days
期刊介绍: Underground Space is an open access international journal without article processing charges (APC) committed to serving as a scientific forum for researchers and practitioners in the field of underground engineering. The journal welcomes manuscripts that deal with original theories, methods, technologies, and important applications throughout the life-cycle of underground projects, including planning, design, operation and maintenance, disaster prevention, and demolition. The journal is particularly interested in manuscripts related to the latest development of smart underground engineering from the perspectives of resilience, resources saving, environmental friendliness, humanity, and artificial intelligence. The manuscripts are expected to have significant innovation and potential impact in the field of underground engineering, and should have clear association with or application in underground projects.
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