• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.1
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• pp.13-24
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• 2014

This paper presents development of artificial neural network(ANN) based prediction method for section forces of TBM tunnel segment lining in an effort to develop an automatized design technique. A series of design cases were first developed and subsequently analyzed using the two-ring beam finite element model. The results were then used to form a database for use as training and validation data sets for ANN development. Using the database, optimized ANNs were developed that can readily be used to predict maximum sectional forces and their distributions. It is shown that the compute maximum section forces and their distributions by the developed ANNs are almost identical to the computed by the two-ring beam finite element model, implying that the developed ANNs can be used as design tools which expedite routine design calculation process. The results of this study indicate that the neural network model can be effectively used as a reliable and simple predictive tool for the prediction of segment sectional forces for design.

• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.1
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• pp.49-63
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• 2015

In this paper, the results of evaluation on the member forces in the virtual subsea tunnel lining segments and optimal thickness of the segment with changes in depth were presented. To evaluate member forces on the hypothetical subsea tunnelling cases were developed and the segmental lining member forces were calculated by performing structural analysis using the 2-Ring Beam model. Through a preliminary reinforcement design review of the cross-section using calculated member force, optimal reinforcement design was selected. Based on the results, the variations of member forces with construction conditions such as the cover depth and the hydraulic pressure are presented. In addition, optimum segment lining designs were developed for various tunnelling conditions.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.4
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• pp.305-330
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• 2023

Unlike NATM tunnels, Shield TBM tunnels have split linings. Therefore, the stress distribution of the lining is different even if the lining is under the same load. Representative methods for analyzing the stress generated in lining in Shield TBM tunnels include Non-joint Mode that does not consider connections and a 2-ring beam-spring model that considers ring-to-ring joints and segment connections. This study is an analysis method by Break-joint Mode. However, we do not consider the structural role of segment lining connections. The effectiveness of the modeling is verified by analyzing behavioral characteristics against vibration loads by modeling with segment connection interfaces to which vertical stiffness and shear stiffness, which are friction components, are applied. Unlike the Non-joint mode, where the greatest stress occurs on the crown for static loads such as earth pressure, the stress distribution caused by contact between segment lining and friction stiffness produced the smallest stress in the crown key segment where segment connections were concentrated. The stress distribution was clearly distinguished based on segment connections. The results of static analysis by earth pressure, etc., produced up to seven times the stress generated in Non-joint mode compared to the stress generated by Break-joint Mode. This result is consistent with the stress distribution pattern of the 2-ring beam-spring model. However, as for the stress value for the train vibration load, the stress of Break-joint Mode was greater than that of Non-joint mode. This is a different result from the static mechanics concept that a segment ring consisting of a combination of short members is integrated in the circumferential direction, resulting in a smaller stress than Non-joint mode with a relatively longer member length.

• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.3
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• pp.363-382
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• 2015

This paper presents a numerical study on the effect of segment assembly characteristics on the TBM segmental lining member section forces. Analyses have been carried out through the two-ring beam finite element model by Midas civil 2012+. TBM segment lining member forces are determined by various joint characteristics. In this study, the segmental member forces were investigated with various joint number and orientation at fixed values of joint stiffness, ground spring parameters. The numerical results were used to identify trends of the member forces in the tunnel lining with the segment assembly characteristics.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.1
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• pp.13-30
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• 2016

This paper concerns the development of an optimized TBM segmental lining design system for a subsea tunnel. The subsea tunnel is normally laid down under the sea water and submarine ground which consists of soil or rock. The design system is the series of process which can predict segmental lining member forces by ANN (artificial neural network system), analyze suitable section for the designated ground, construction and tunnel conditions. Finally, this lining design system aims to be connected with a BIM system for designing the subsea tunnel automatically. The lining member forces are predicted based on the ANN which was calculated by a FEM (finite element analysis) and it helps designers determine its segmental lining dimension easily without any further FE calculations.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.3
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• pp.321-339
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• 2014

This paper concerns the development of a knowledge-based tunnel design system within the framework of artifical neural networks(ANNs). The system is aimed at expediting a routine tunnel design works such as computation of segment lining body forces and stability analysis of selected cross section. A number of sub-modules for computation of segment lining body forces and stability analysis were developed and implemented to the system. It is shown that the ANNs trained with the results of 3D numerical analyses can be generalized with a reasonable accuracy, and that the ANN based tunnel design concept is a robust tool for tunnel design optimization. The details of the system architecture and the ANNs development are discussed in this paper.