A Hybrid Data Mining Method for Tunnel Engineering Based on Real-Time Monitoring Data From Tunnel Boring Machines

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 realtime 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 decisionmaking 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. INDEX TERMS Data mining, monitoring data, tunnel boring machine (TBM), tunnel construction, underground structure.

Factors Affecting Road Tunnel Construction Accidents in China Based on Grounded Theory and DEMATEL

“…According to the order from large to small, they are the regional climate environment, engineering hydrogeology, professional quality, safety awareness, and labor management, which belong to the cause factors, indicating that these factors are easy to affect other factors in the accident system. In the accident report, most of the causes of non-safety accidents are geological environment problems that are difficult to investigate [ 51 ], such as “It is inferred that the coupling effect of various unfavorable geological conditions and climatic reasons such as earthquake and sufficient rainfall into April is the main reason for the overall brittle damage and instantaneous and sudden local collapse of the vault.” and “After the accident, the accident investigation team expert group after site investigation, found that the geological conditions are actually striped mixed granite, lamellar development, the existence of kaolin and other unfavorable geological conditions of water softening”. The regional climate environment and engineering hydrogeology are the most significant factors affecting other factors, such as construction, design, and survey.…”

Research on Intelligent Identification and Analysis Algorithm of Tunnel Engineering Geological Information

“…The performance of the proposed model in tunnel engineering based on the geological data is compared with the different conventional methods such as GPR-SVM [15], ANN [16], GCN-LSTM [17], DM [18], and M-KNN [19] in terms of performance metrics. Figure 3 displays the ROC curve, which was used to evaluate the proposed model's predictive capacity compared to traditional techniques.…”

Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies

“…The practical experience and previous studies indicated that the rotation speed (N), advance rate (V), and rotation speed of the screw machine (N s ) are the main operating parameters of the machine operators. Cutterhead thrust (F) and torque (T) indirectly reflect the interaction between the EPB machine and the excavation face, and torque of the screw machine (T s ) reflects the conditions of slag discharge [58,59]. Chamber pressure (C p ) can be used to evaluate the stability of the excavation face, and it is the representative parameter of EPB shield machine.…”