37th International Symposium on Automation and Robotics in Construction (ISARC 2020) Real-time Aarly Warning of Clogging Risk in Slurry Shield Tunneling: A Self-updating Machine Learning Approach Qiang Wanga, Xiongyao Xie a, and Yu Huang a aDepartment of geotechnical engineering, Tongji University E-mail:
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[email protected] Abstract – pressure balanced (SPB) tunnel boring machine [1,2]. Clogging is one of the main risks when slurry The clogging problem will trigger risks during tunneling shield tunneling in the mixed ground condition construction, such as slower tunneling efficiency[3], containing clayey soils. Severe consequences, such as instability of tunnel face[4], higher wear of cutterhead[5], instability in the excavation face and high cutter wear, etc. As a typical kind of clayey soil, mudstone has a high may occur if the shield machine operators don’t take potential to result in a clogging problem[4]. Several specific measures to eliminate clogging. Therefore, projects in China have been encountered with clogging early warning of clogging during one ring excavation problems in mudstone rich area, for example, Wuhan becomes essential for the safetyf o tunneling. The Sanyang cross-river road tunnel[6], the metro tunnel line currently available methods to judge the clogging 1&2 in Nanning city[7], Nanchang metro line 1[8], risks focus mainly on field engineer experience, which Nanjing Yangze river tunnel[9]. The filed experience seems arbitrary sometimes. In this paper, an indicates that it’s difficult to maintain the normal automatic self-updating machine learning approach tunneling state in mudstone rich area, especially for the is proposed to realize the real-time early warning of mixed ground containing mudstone.