scholarly journals Evaluating the effect of EPBM operational parameters on surface settlement in soft ground

2020 ◽  
Author(s):  
Ali Naghi Dehghan ◽  
Ehsan Bagheri ◽  
Meysam Khodaei ◽  
Rouzbeh Imani Kalehsar
Keyword(s):  
Ground Surface ◽  
Coupled Model ◽  
Earth Pressure ◽  
Surface Settlement ◽  
Ground Settlement ◽  
Operational Parameters ◽  
Pressure Balance ◽  
Tunnel Face ◽  
Earth Pressure Balance ◽  
Parametric Analyses

Abstract The present study investigates the effects of some essential earth pressure balance machine (EPBM) operational parameters, including face, annulus and grout pressures, on ground surface settlement during tunnel excavation. A numerical soil-tunnel-fluid coupled model was developed using the finite difference method (FDM) in FLAC3D for the basis of this research. Hence, the effects of geostatic stress in the tunnel face and annulus and the grout pressure behind the segment on ground settlement were investigated by parametric analyses. The results indicated that a reduction in the boundary pressure (from 508 to 335 kPa) on the face and annulus space did not change the settlement significantly, altering the settlement from 1.18 to 1.48 mm. The results of parametric grout pressure analysis showed that applying a grout pressure equal to the vertical geostatic stress (i.e. 335 kPa) had a larger effect than applying horizontal geostatic stress (i.e. 357 kPa) to the tunnel face on controlling the ground settlement—i.e. 1.45 and 1.20 mm, respectively. A lateral earth pressure of below 1 (K0 < 1) led to the estimation of horizontal geostatic stress value that was lower than the vertical geostatic stress value, thereby applying a lower effect to controlling the ground settlement. Grout pressure increment relative to the vertical geostatic stress considerably reduced the ground settlement. However, an excessive rise in the grout pressure could lead to deformation and upward displacements in the form of ground heaving at a transverse distance of above 20 m from the tunnel axis.

Surface settlement during shield tunnelling at CH218 in Taipei

1998 ◽  
Vol 35 (1) ◽  
pp. 159-168 ◽  
Author(s):  
Chang-Yu Ou ◽  
Richard N Hwang ◽  
Wei-Jung Lai
Keyword(s):  
Ground Surface ◽  
Earth Pressure ◽  
Surface Settlement ◽  
Pressure Balance ◽  
Transit System ◽  
Ground Surface Settlement ◽  
Earth Pressure Balance Shield ◽  
Shield Tunnelling ◽  
Earth Pressure Balance ◽  
Twin Tunnels

This paper presents the surface settlement performance induced by the foamed type of earth pressure balance shield in contract CH218 of the Hsintien Line of the Taipei Rapid Transit System. The surface settlement characteristics caused by the single tunnel and by twin tunnels with reference to two sections spaced at 87 m are studied. Field observations indicate that the surface settlement trough due to the single tunnel can be represented by the normal distribution. The distance of the inflection point to the tunnel center and maximum surface settlement value are consistent with those found in the literature. The characteristics of the surface settlement trough are related to the type of the soil, particularly where the crown of the tunnel is located in a layered soil deposit. The ground surface settlement induced by twin tunnels was found to be larger than estimated using the principle of superposition.Key words: shield tunnelling, surface settlement, field observation.

As-Encountered Prediction of Tunnel Boring Machine Performance Parameters using Recurrent Neural Networks

2020 ◽  
Vol 2674 (10) ◽  
pp. 241-249
Author(s):  
Kabir Nagrecha ◽  
Luis Fisher ◽  
Michael Mooney ◽  
Tonatiuh Rodriguez-Nikl ◽  
Mehran Mazari ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Precast Concrete ◽  
Earth Pressure ◽  
Tunnel Boring Machine ◽  
Operational Parameters ◽  
Pressure Balance ◽  
Tunnel Boring ◽  
Machine Performance ◽  
Earth Pressure Balance

The earth pressure balance tunnel boring machine (TBM) is advanced excavation machinery used to efficiently drill through subsurface ground layers while placing precast concrete tunnel segments. They have become prevalent in tunneling projects because of their adaptability, speed, and safety. Optimal usage of these machines requires information and data about the soil of the worksite that the TBM is drilling through. This paper proposes the utilization of artificial intelligence and machine learning, particularly recurrent neural networks, to predict the operational parameters of the TBM. The proposed model utilizes only performance data from excavation segments before the location of the machine as well as its current operating parameters to predict the as-encountered parameters. The proposed method is evaluated on a dataset collected during a tunneling project in North America. The results demonstrate that the model is effective in predicting operation parameters. To address the potential issue of gathering sufficient data to retrain the model, the possibility of transferring the trained model from one tunnel to another is tested. The results suggest that the model is capable of performing accurately with minimal or even no re-training.

scholarly journals Optimized Adaptive Neuro-Fuzzy Inference System Using Metaheuristic Algorithms: Application of Shield Tunnelling Ground Surface Settlement Prediction

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xinni Liu ◽  
Sadaam Hadee Hussein ◽  
Kamarul Hawari Ghazali ◽  
Tran Minh Tung ◽  
Zaher Mundher Yaseen
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Earth Pressure ◽  
Ground Settlement ◽  
Pressure Balance ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Shield Tunnelling ◽  
Earth Pressure Balance ◽  
Epb Shield

Deformation of ground during tunnelling projects is one of the complex issues that is required to be monitored carefully to avoid the unexpected damages and human losses. Accurate prediction of ground settlement (GS) is a crucial concern for tunnelling problems, and the adequate predictive model can be a vital tool for tunnel designers to simulate the ground settlement accurately. This study proposes relatively new hybrid artificial intelligence (AI) models to predict the ground settlement of earth pressure balance (EPB) shield tunnelling in the Bangkok MRTA project. The predictive models were various nature-inspired frameworks, such as differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimizer (ACO) to tune the adaptive neuro-fuzzy inference system (ANFIS). To obtain the accurate and reliable results, the modeling procedure is established based on four different dataset scenarios including (i) preprocessed and normalized (PPN), (ii) preprocessed and nonnormalized (PPNN), (iii) non-preprocessed and normalized (NPN), and (iv) non-preprocessed and nonnormalized (NPNN) datasets. Results indicated that PPN dataset scenario significantly affected the prediction models in terms of their perdition accuracy. Among all the developed hybrid models, ANOFS-PSO model achieved the best predictability performance. In quantitative terms, PPN-ANFIS-PSO model attained the least root mean square error value (RMSE) of 7.98 and a correlation coefficient value (CC) of 0.83. Overall, the attained results confirmed the superiority of the explored hybrid AI models as robust predictive model for ground settlement of earth pressure balance (EPB) shield tunnelling.

Analysis on Ground Settlement Induced by Shallow Buried Cross Channel of CRD Method under Artificial Filling

2013 ◽  
Vol 353-356 ◽  
pp. 1519-1524
Author(s):  
Jin Kui Li ◽  
Jing Jing Li ◽  
Liu Jie Du
Keyword(s):  
Surface Deformation ◽  
Settling Tank ◽  
Ground Surface ◽  
Surrounding Rock ◽  
Surface Settlement ◽  
Ground Settlement ◽  
Tunnel Face ◽  
Underground Tunnel ◽  
The Cross ◽  
The Stability

The shallow underground tunnel is near to the ground; its many construction procedures are complicated, supporting and excavation are intertwined. The ground surface deformation is complex during construction. Through the analysis of the cross passage surface settlement data of Dalian metro Line 1High-tech zone Street station, we found that the ground surface caused by artificial filling integrally sinks during excavation, the shape of its sinking is like a flat funnel, the characteristics of settling tank are obvious. The influence of faces constructing is obvious on surface settlement, and the transverse influence range is about 30m; the longitudinal influence range is about 15m. The results of the paper show that the place of monitoring points should be held at 15m ahead from the tunnel face, effectivemonitoring period is 70d. The monitoring results are enough and safe for the stability requirement of the surrounding rock.

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