Abstract
Tunnel engineering is one of the typical megaprojects given its long construction period, high construction costs and potential risks. Tunnel boring machines (TBMs) are widely used in tunnel engineering to improve work efficiency and safety. During the tunneling process, large amount of monitoring data has been recorded by TBMs to ensure construction safety. Analysis of the massive real-time monitoring data still lacks sufficiently effective methods and needs to be done manually in many cases, which brings potential dangers to construction safety. This paper proposes a hybrid data mining (DM) approach to process the real-time monitoring data from TBM automatically. Three different DM techniques are combined to improve mining process and support safety management process. In order to provide people with the experience required for on-site abnormal judgement, association rule algorithm is carried out to extract relationships among TBM parameters. To supplement the formation information required for construction decision-making process, a decision tree model is developed to classify formation data. Finally, the rate of penetration (ROP) is evaluated by neural network models to find abnormal data and give early warning. The proposed method was applied to a tunnel project in China and the application results verified that the method provided an accurate and efficient way to analyze real-time TBM monitoring data for safety management during TBM construction.
Highlights
Tunnel boring machines (TBMs) have been widely used as an effective tool for tunnel construction which is characterized by its large scale, high cost and long project lifecycle, and the construction process is always associated with complex technical problems and potential risks [1]
Geological information is a key factor in safety management of tunnel construction, and different methods including artificial neural network (ANN) [31] and support vector classifier (SVC) [32] has been proposed to predict geological formation based on TBM operating data
The contributions of the proposed approach include: (1) Aiming at three objectives of TBM safety management, a hybrid data mining (DM) method is proposed to improve mining process and achieve multi-objective analysis (2) Both formation data and real-time monitoring data were involved, and the formation data were calibrated to improve accuracy; (3) Association rules are adopted in the parameter selection process to improve the accuracy of DM
Summary
Tunnel boring machines (TBMs) have been widely used as an effective tool for tunnel construction which is characterized by its large scale, high cost and long project lifecycle, and the construction process is always associated with complex technical problems and potential risks [1]. Geological information is a key factor in safety management of tunnel construction, and different methods including ANN [31] and support vector classifier (SVC) [32] has been proposed to predict geological formation based on TBM operating data. The contributions of the proposed approach include: (1) Aiming at three objectives of TBM safety management, a hybrid DM method is proposed to improve mining process and achieve multi-objective analysis (2) Both formation data and real-time monitoring data were involved, and the formation data were calibrated to improve accuracy; (3) Association rules are adopted in the parameter selection process to improve the accuracy of DM. Input variables for TBM data analysis are mainly based on people’s experience, such as inherited from previous models or determined by domain experts This method is quite useful with a small number of parameters. The support requirement guarantees the frequency of association rules, and the confidence requirement ensures the reliability of association rules
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