Equation for Maximum Ground Surface Settlement due to Bored Tunnelling in Cohesive and Cohesionless Soils Obtained by Numerical Simulations

Saeed, Hamza; Uygar, Eriş

doi:10.1007/s13369-021-06436-3

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…In practical situations, it is often impossible to anticipate structural deformations due to a multitude of complex factors. As a result, numerical simulations that are conducted often only occur during the design stage or post-accident review and analysis, leading to an absence of accurate models and techniques for predicting tunnel lining deformation during operation [1][2][3][4][5]. Achievement of accurate prediction of deformation in tunnel lining structure is therefore an urgent challenge that must be tackled to ensure continued safe and reliable operation of subway systems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Prediction of Longitudinal Settlement of Existing Tunnel Using ConvRes-DLinear Model with Integration of Undercrossing Construction Process Information

et al. 2023

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Undercrossing construction can cause severe structural deformation of the above existing tunnel in operation. The induced longitudinal differential settlement between the segments can pose a huge risk to running subways, hence it is of great importance to monitor and predict the settlement. Within this study, a Wireless Sensor Network (WSN) system was implemented to obtain hourly monitoring data of settlement from the very beginning of undercrossing to post construction period. An improved direct multi-step (DMS) forecasting model called ConvRes-DLinear is proposed, which fuses monitoring data with time and process encoding bias to deeply extract and learn temporal correlation of time series. A residual LSTM model is also constructed to compare the accuracy of the improved DLinear model. The training and testing experiment on the monitoring data of longitudinal settlement obtained by WSN system shows that the ConvRes-DLinear model with time and process encoding bias performs surprisingly well with a minimum prediction error. The features of the proposed model are discussed to make the results explainable. The monitoring system and time series forecasting model proposed in this study have a guiding significance for the monitoring and prediction of longitudinal differential settlement of tunnels under environmental disturbance.

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Section: Introductionmentioning

confidence: 99%

Dynamic Prediction of Longitudinal Settlement of Existing Tunnel Using ConvRes-DLinear Model with Integration of Undercrossing Construction Process Information

et al. 2023

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“…At present, surface settlement prediction methods include three main categories: empirical formulas, numerical simulations, and machine learning. For single-hole tunnels, empirical formulas such as the peck formula are often improved to predict surface settlement 1 – 3 . But the existing empirical or semi-empirical methods can hardly take into account the external factors affecting soil deformation comprehensively, making it difficult to predict surface settlement accurately.…”

Section: Introductionmentioning

confidence: 99%

A novel combined intelligent algorithm prediction model for the tunnel surface settlement

Wang,

Dai,

Jia

et al. 2023

Sci Rep

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To ensure the safety and stability of the shield tunnel construction process, the ground settlement induced by the shield construction needs to be effectively predicted. In this paper, a prediction method combining empirical mode decomposition (EMD), chaotic adaptive sparrow search algorithm (CASSA), and extreme learning machine (ELM) is proposed. First, the EMD is used to decompose the settlement sequence into trend vectors and fluctuation vectors to fully extract the effective information of the sequence; Second, the sparrow search algorithm is improved by introducing Cubic chaotic mapping to initialize the population and adaptive factor to optimize the searcher’s position formula, and the chaotic adaptive sparrow search algorithm is proposed; Finally, the CASSA-ELM prediction model is constructed by using CASSA to find the optimal values of weights and thresholds in the extreme learning machine. The fluctuation components and trend components decomposed by EMD are predicted one by one, and the prediction results are superimposed and reconstructed to obtain the predicted final settlement. Taking a shield interval in Jiangsu, China as an example, the meta-heuristic algorithm-optimized ELM model improves the prediction accuracy by 10.70% compared with the traditional ELM model. The combined EMD-CASSA-ELM prediction model can greatly improve the accuracy and speed of surface settlement prediction, and provide a new means for safety monitoring in shield tunnel construction. Intelligent prediction methods can predict surface subsidence more automatically and quickly, becoming a new development trend.

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“…In this paper, based on Gaoliang Tunnel in Guangxi province, constructing the CD, CRD, and full-section methods were analyzed using the finite software. The tunnel vault displacement, deformation around the tunnel, the stress of the initial support and secondary lining, and the law of surface settlement during excavation of deep-buried separate large-section tunnels were studied for different construction methods [22][23][24][25]. However, the tunnel influence law of large section split tunnel excavation was studied by comparing the simulation results with the surveying data.…”

Section: Introductionmentioning

confidence: 99%

Ground Settlement of Deeply Buried Two-Lane Tunnels with Large Cross-Sections Using Different Construction Methods

et al. 2022

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To ensure the safety of excavations such as large section tunnels of the Guangxi Liubin Highway Tunnel Project, we implemented a simulation analysis of 3D tunnel models based on finite software for different construction methods. Different from the traditional simulation, this paper uses various construction methods to compare, study, and analyze the advantages and disadvantages of different excavation methods by combining them with the excavation. The feasibility of different construction methods was analyzed by studying the mechanical characteristics and settlement changes using the Cross Diaphragm (CRD), Center Diaphragm (CD), and full-section methods while building a large section tunnel. The arch perimeter deformation and surface settlement during the initial support and secondary lining proved that the CRD excavation method is the most favorable and causes the least damage to the stability of the surrounding rock. For the analysis of surface subsidence, the “V”-shaped surface subsidence curve excavated by the CRD method is the closest to the subsidence curve. However, we used the adjusted peak formula to fit the prediction formula for the surface settlement curve of the deep-buried two-lane tunnel with a large cross-section applicable to this project, which will provide an important reference for similar projects.

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Equation for Maximum Ground Surface Settlement due to Bored Tunnelling in Cohesive and Cohesionless Soils Obtained by Numerical Simulations

Cited by 7 publications

References 35 publications

Dynamic Prediction of Longitudinal Settlement of Existing Tunnel Using ConvRes-DLinear Model with Integration of Undercrossing Construction Process Information

Dynamic Prediction of Longitudinal Settlement of Existing Tunnel Using ConvRes-DLinear Model with Integration of Undercrossing Construction Process Information

A novel combined intelligent algorithm prediction model for the tunnel surface settlement

Ground Settlement of Deeply Buried Two-Lane Tunnels with Large Cross-Sections Using Different Construction Methods

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