Numerical and artificial neural network analyses of ground surface settlement of tunnel in saturated soil

Ghiasi, Vahed; Koushki, Mehdi

doi:10.1007/s42452-020-2742-z

Cited by 23 publications

(12 citation statements)

References 31 publications

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“…Among the very recent investigations are the work by Chen et al [18], Khalili et al [19], and Moghaddasi & Noorian-Bidgoli [20], who attempted to integrate the shield's operational parameters, the tunnel's geometry, and the geological conditions in the NNW models to improve the prediction results. Gradually some efforts [2,3,8] have been made to improve the accuracy of the prediction models by developing a hybrid approach of numerical modeling and NNWs.…”

Section: Review the Applications Of Artificial Neural Network In Geot...mentioning

confidence: 99%

“…Many tunnel excavation projects are taking place concurrently around the world. Tunnel construction is becoming more common in urban areas, which poses a risk of damage to existing structures due to tunnel-induced ground deformation [1][2][3]. Recent advances in tunneling technology have allowed it to take advantage of the underground space to a greater extent.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, evaluating the settlement hazards induced by tunneling is recommended since they have high significance for the long-and short-term performance of the tunnel. The effects of tunneling on the ground surface have been studied by several researchers [2][3][4]. However, their conclusions have been marred by uncertainty since it is unclear how the various elements affect the tunneling process.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Models to Evaluate the Interaction Effect of Soil-Tunnel Interaction Parameters on Surface and Subsurface Settlement

et al. 2022

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Nowadays, the need for subway tunnels has increased considerably with urbanization and population growth in order to facilitate movements. In urban areas, subway tunnels are excavated in shallow depths under densely populated areas and soft ground. Its associated hazards include poor ground conditions and surface settlement induced by tunneling. Various sophisticated variables influence the settlement of the ground surface caused by tunneling. The shield machine's operational parameters are critical due to the complexity of shield-soil interactions, tunnel geometry, and local geological parameters. Since all elements appear to have some effect on tunneling-induced settlement, none stand out as particularly significant; it might be challenging to identify the most important ones. This paper presents a new model of an artificial neural network (ANN) based on the partial dependency approach (PDA) to optimize the lack of explainability of ANN models and evaluate the sensitivity of the model response to tunneling parameters for the prediction of ground surface and subsurface settlement. For this purpose, 239 and 104 points for monitoring surface and subsurface settlement, respectively, were obtained from line Y, the west bond of Crossrail tunnels in London. The parameters of the ground surface, the trough, and the tunnel boring machine (TBM) were used to categorize the 12 potential input parameters that could impact the maximum settlement induced by tunneling. An ANN model and a standard statistical model of multiple linear regression (MLR) were also used to show the capabilities of the ANN model based on PDA in displaying the parameter's interaction impact. Performance indicators such as the correlation coefficient (R2), root mean square error (RMSE), and t-test were generated to measure the prediction performance of the described models. According to the results, geotechnical engineers in general practice should attend closely to index properties to reduce the geotechnical risks related to tunneling-induced ground settlement. The results revealed that the interaction of two parameters that have different effects on the target parameter could change the overall impact of the entire model. Remarkably, the interaction between tunneling parameters was observed more precisely in the subsurface zone than in the surface zone. The comparison results also indicated that the proposed PDA-ANN model is more reliable than the ANN and MLR models in presenting the parameter interaction impact. It can be further applied to establish multivariate models that consider multiple parameters in a single model, better capturing the correlation among different parameters, leading to more realistic demand and reliable ground settlement assessments. This study will benefit underground excavation projects; the experts could make recommendations on the criteria for settlement control and controlling the tunneling parameters based on predicted results. Doi: 10.28991/CEJ-2022-08-11-05 Full Text: PDF

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Section: Review the Applications Of Artificial Neural Network In Geot...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive Models to Evaluate the Interaction Effect of Soil-Tunnel Interaction Parameters on Surface and Subsurface Settlement

et al. 2022

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“…In addition, there are many methods predicting the ground surface settlement, such as artificial neural network method [10], artificial bee colony algorithm [11], Peck method [12] and stochastic medium theory [13]. Artificial neural network method and artificial bee colony algorithm are more inclined to evaluate the risk of ground surface collapse, while Peck method is put forward based on the concept of soil loss.…”

Section: Introductionmentioning

confidence: 99%

Improved prediction method for ground surface thawing settlement caused by the melting of tunnel horizontal frozen wall

Hong

Cai

2021

SN Appl. Sci.

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With the rapid development of urban subway tunnel, artificial ground freezing technology is becoming more and more mature. With the natural thawing of horizontal frozen wall, thawing settlement will occur on stratum due to the thawing of frozen soil and the consolidation of thawed soil, which will inevitably bring adverse impact on the surrounding environment of subway tunnel. Therefore, the establishment of a reasonable ground surface thawing settlement prediction method will provide a favorable theoretical support for predicting the ground surface deformation in advance and taking active thawing settlement control measures. In the paper, the time functions of ground surface thawing settlement and consolidation settlement of tunnel horizontal frozen wall are established based on the stochastic medium theory during natural thawing period, and the calculation methods of thawing front radius, inner radius of thawing shrinkage region and inner radius of consolidation region are proposed. The results show that the cumulative ground surface thawing settlement is larger than that of Cai et al. after considering the consolidation of the thawed soil, which fully indicates that the ground surface settlement caused by the drainage and consolidation of the thawed soil cannot be ignored. In addition, the thawing displacement rate of frozen soil is greater than the consolidation displacement rate of thawed soil during the natural thawing and the thawed soil will be consolidated at a lower settlement rate for a long time after the natural thawing period.

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“…But it did not introduce the principle of selecting input variables in the model. Ghiasi established a numerical simulation calculation model and a multi-layer perceptron (MLP) prediction model [6]. It predicted the surface settlement during a single circular tunnel excavation process, and believed that the prediction result was reasonable.…”

Section: Introductionmentioning

confidence: 99%

Research on prediction method of section convergence and deformation of shield tunnel in operation period based on deep learning

Sun¹,

Zheng²,

Li³

2021

Vib. proced.

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Presently, the tunnel construction of China is changing from "mainly construction" to "equal emphasis on construction and maintenance". In this context, tunnel structure inspection, monitoring, and maintenance technologies have developed rapidly. However, the effective use and in-depth mining of massive data has always been the most difficult point in this field. Based on deep learning technology, this paper carries out in-depth mining of the multi-source data of the tunnel structure, so that the convergence and deformation of the section during the operation of the shield tunnel is predicted in a short time. Taking the Nanjing Yangtze River shield tunnel project as an example, the indicators are screened based on the fluctuations of the pair wise correlation coefficients of all monitoring indicators. And based on the Keras (a high-level neural network API) framework, a short-term prediction model of the convergent deformation of the shield tube section at this location is established. The results show that the model successfully predicts the convergence of the tunnel section in the next 10 days, and the prediction accuracy reaches 93.6 %. The short-term prediction of key sections and the near warning sections is realized, so as to prevent it in advance.

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Numerical and artificial neural network analyses of ground surface settlement of tunnel in saturated soil

Cited by 23 publications

References 31 publications

Predictive Models to Evaluate the Interaction Effect of Soil-Tunnel Interaction Parameters on Surface and Subsurface Settlement

Predictive Models to Evaluate the Interaction Effect of Soil-Tunnel Interaction Parameters on Surface and Subsurface Settlement

Improved prediction method for ground surface thawing settlement caused by the melting of tunnel horizontal frozen wall

Research on prediction method of section convergence and deformation of shield tunnel in operation period based on deep learning

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