Estimating locations of soil–rock interfaces based on vibration data during shield tunnelling

Shen, Shui‐Long; Yan, Tao; Zhou, Annan

doi:10.1016/j.autcon.2023.104813

Cited by 20 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shen et al [79] also used sensors arranged at the back of the bulkhead inside the shield machine to monitor the vibration signals. The monitoring system included one triaxial accelerometer and three unilateral accelerometers, the type specification of the sensor was MPS-ACC03X/01X-IEPE, and the fixing method was magnetic suction, as shown in Figure 7d.…”

Section: Indirect Monitoring Within the Shieldmentioning

confidence: 99%

“…This work can help engineers better understand the vibration generation mechanism in complex formations, and provide reference for geological identification based on vibration analysis. Shen et al [79] analyzed the time-frequency spectral characteristics of vibration signals in various formations using continuous wavelet transform (CWT) for identifying the vibration peaks and their periods. Based on the process of disc cutter collision with the soil-rock interface (SRI), the key factors affecting the amplitude of the acceleration peak (installation radius of the disc cutter and the position of the SRI) were identified.…”

Section: Predicting Geologic Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Review on Vibration Monitoring and Its Application during Shield Tunnel Construction Period

Yang,

Fang,

Wang

et al. 2024

Buildings

View full text Add to dashboard Cite

With the rapid development of metro construction, shield machines inevitably have to traverse a variety of complex geological conditions, leading to the frequent occurrence of geological disasters, equipment failures, building vibration and other problems. Vibration, as an important feature of the shield tunneling process, has received more and more attention in recent years. This paper summarizes the relevant research progress of vibration monitoring during shield construction from 2015 to 2023. It analyzes the shield vibration generation mechanism, monitoring methods and application areas. Firstly, the shield vibration type is divided into mechanical vibration triggered by internal excitation and forced vibration triggered by external excitation, and the principles of vibration generated by shield main bearing, gearbox and disc cutter are discussed. Then, the commonly used vibration monitoring methods are outlined according to the installation location of the sensors (inside and outside of the shield). Finally, the applications of vibration signals in the diagnosis of shield faults, the identification of geologic conditions, and the evaluation of the current status of the interference with the buildings are summarized. This paper discusses the development trend of vibration monitoring during shield tunneling based on the current research situation and the current technology level, which provides valuable insights to enhance the safety and intelligence of shield construction.

show abstract

Section: Indirect Monitoring Within the Shieldmentioning

confidence: 99%

Section: Predicting Geologic Featuresmentioning

confidence: 99%

Review on Vibration Monitoring and Its Application during Shield Tunnel Construction Period

Yang,

Fang,

Wang

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Their capacity to comprehensively encapsulate intricate and non-linear circumstances might sometimes be insufficiently robust, particularly when confronted with outliers or instances of extraordinary data. On the other hand, numerous researchers 4 , 19 – 27 have used artificial intelligence models to predict TBM performance, penetration rate, torque and thrust, cutter wearing, and lithology identification using geological information and TBM operational parameters. Despite integrating TBM parameters and lithology data into prior intelligent models, most research has focused on traditional methods for predicting how the conditions of the ground affect the operation of TBMs.…”

Section: Introductionmentioning

confidence: 99%

Soft ground micro TBM jack speed and torque prediction using machine learning models through operator data and micro TBM-log data synchronization

Kilic,

Narihiro,

Ikeda

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Tunnel Boring Machines (TBMs) are pivotal in underground projects like subways, highways, and water supply tunnels. Predicting and monitoring jack speed and torque is crucial for optimizing TBM excavation efficiency. Conventionally, skilled operators manually adjust numerous tunnelling parameters to regulate the machine's progress. In contrast, machine learning (ML) algorithms offer a promising avenue where computers learn from operator actions to establish parameter relationships autonomously. This study introduces an innovative approach to enhancing operator monitoring and TBM data comprehension. A robust correlation between TBM operator behaviour and TBM logged data is established by leveraging an Optuna-assisted ML methodology—the research light on the intricate dynamics influencing TBM advance rate parameters. Operational data is collected from micro slurry tunnel boring machine (MSTBM) umbrella support excavations. The proposed framework harnesses Optuna, an advanced hyperparameter optimization platform, to dynamically refine jack speed and torque settings. Through meticulous analysis of the interplay between TBM operator decisions and real-time logged data, the AI model discerns patterns, empowering informed decision-making. Using Optuna, a range of models, including random forest (RF), K-nearest neighbours (kNN), decision tree (DT), XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were automatically compared and tuned. The best model's (RF) performance is evaluated through a correlation coefficient (R2) of 96%, mean squared error (MSE) of 119.7, and mean absolute error (MAE) of 4.42 for jack speed decision making while 83% of R2, MSE of 0.62, and MAE of 0.42 for the torque decision making. This intelligent model can assist the TBM operator in making decisions about TBM control.

show abstract

Novel model for risk assessment of shield tunnelling in soil-rock mixed strata

Zhou,

Shen

2024

Acta Geotech.

View full text Add to dashboard Cite

Estimating locations of soil–rock interfaces based on vibration data during shield tunnelling

Cited by 20 publications

References 27 publications

Review on Vibration Monitoring and Its Application during Shield Tunnel Construction Period

Review on Vibration Monitoring and Its Application during Shield Tunnel Construction Period

Soft ground micro TBM jack speed and torque prediction using machine learning models through operator data and micro TBM-log data synchronization

Novel model for risk assessment of shield tunnelling in soil-rock mixed strata

Contact Info

Product

Resources

About