Ground Settlement of High-Permeability Sand Layer Induced by Shield Tunneling: A Case Study under the Guidance of DBN

Zhou, Cong; Gao, Wei; Cui, Shuang; Zhong, Xiaochun; Hu, Chengjie; Chen, Xin

doi:10.1155/2020/6617468

Cited by 8 publications

(2 citation statements)

References 26 publications

(36 reference statements)

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“…Moreover, for its complex network structure, the DBN can treat big data problems. Nowadays, DBNs have already been used to solve the big data problems in geotechnical engineering [30]. Therefore, in this study, the DBN is applied to the performance prediction of a utility tunnel in a soft foundation during the operation period.…”

Section: Methodologies 21 Deep Belief Network (Dbn)mentioning

confidence: 99%

Prediction of Utility Tunnel Performance in a Soft Foundation during an Operation Period Based on Deep Learning

Gao,

Ge,

Gao

et al. 2024

Applied Sciences

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The underground utility tunnel in a soft foundation is generally affected by the serious disturbance of the vehicle load during the operation period. Therefore, in this study, for the typical utility tunnel engineering in Suqian City of Jiangsu Province, China, field tests were conducted to monitor the performance of the utility tunnel structure in a soft foundation affected by the ground traffic loads during the operation period. Based on the test results, the datasets whose number is 15,376, composed of the five main disturbance factors (four vehicle operating load parameters and one operating time parameter), and the corresponding two main structure responses (displacement and stress) have been constructed. Based on the obtained datasets, using the proposed new deep learning model called WO-DBN, in which the seven hyperparameters of a deep belief network (DBN) are determined by the whale optimization algorithm (WOA), the safety responses of the utility tunnel structure have been predicted. The results show that for the prediction results, the average absolute error for the displacement is 0.1604, and for the stress, it is 12.3726, which are not significant and can meet the requirement of the real engineering. Therefore, the deep learning model can accurately predict the performance of the utility tunnel structure under a vehicle load and other disturbances, and the model has good applicability.

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Section: Methodologies 21 Deep Belief Network (Dbn)mentioning

confidence: 99%

Prediction of Utility Tunnel Performance in a Soft Foundation during an Operation Period Based on Deep Learning

Gao,

Ge,

Gao

et al. 2024

Applied Sciences

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“…Although the increases in porosity and permeability coefficient are beneficial to the diffusion of the grout, large porosity and permeability coefficient are not conducive to improving the grouting effect [51]. Large porosity and permeability coefficient result in a faster flow of the grout in the soil, which leads to cavity formation inside the shield tail void.…”

Section: Porosity ϕ and Permeability Coefficient K Wmentioning

confidence: 99%

Pressure Model Study on Synchronous Grouting in Shield Tunnels Considering the Temporal Variation in Grout Viscosity

Ma,

Sun,

Jiang

et al. 2023

Applied Sciences

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The grout pressure in the shield tunnel tail void during synchronous grouting is the key to controlling ground settlement and restraining the segment. However, the circumferential, longitudinal, and radial distribution of grout pressure considering the temporal variation in grout viscosity has not been well explored yet. In this study, a theoretical model of grout pressure distribution and dissipation considering the temporal variation in Bingham grout viscosity was established. The simulation results of the pressure model were verified by field-measured data. The results showed that the radial and longitudinal distributions of grout pressure considering the temporal variation in grout viscosity were closer to the field-measured data. The impacts of the main parameters on the pressure distribution and dissipation were analyzed. Compared with the effect of the shield tail void thickness, tunnel radius and yield shear stress have greater effects on grout pressure during the circumferential filling phase. During the longitudinal and radial diffusion phases, the increase in soil porosity and permeability coefficient was conducive to grout diffusion. The increase in the grout viscosity reduces the pressure loss during the grout flow process. The results of this research can provide a theoretical basis for the grout design process in shield tunnels.

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Influence of double-line large-slope shield tunneling on settlement of ground surface and mechanical properties of surrounding rock and segment

Lou¹,

Li²,

Tang³

et al. 2023

Alexandria Engineering Journal

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Ground Settlement of High-Permeability Sand Layer Induced by Shield Tunneling: A Case Study under the Guidance of DBN

Cited by 8 publications

References 26 publications

Prediction of Utility Tunnel Performance in a Soft Foundation during an Operation Period Based on Deep Learning

Prediction of Utility Tunnel Performance in a Soft Foundation during an Operation Period Based on Deep Learning

Pressure Model Study on Synchronous Grouting in Shield Tunnels Considering the Temporal Variation in Grout Viscosity

Influence of double-line large-slope shield tunneling on settlement of ground surface and mechanical properties of surrounding rock and segment

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