Development of artificial neural networks and multiple regression models for the NATM tunnelling-induced settlement in Niayesh subway tunnel, Tehran

Mohammadi, Seyed Davoud; Naseri, F.; Alipoor, S.

doi:10.1007/s10064-014-0660-2

Cited by 42 publications

(10 citation statements)

References 32 publications

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“…Nowadays with the boom of deep learning, the settlement has been studied through advanced algorithms [7]. Neural network method is more and more popular in settlement prediction [8][9][10]. To protect the surface building, a new formulation was put forward to calculate its subsidence [11].…”

Section: Introductionmentioning

confidence: 99%

Tunnelling‐Induced Settlement and Treatment Techniques for a Loess Metro in Xi’an

Lai

et al. 2020

Advances in Civil Engineering

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Techniques including pre-grouting, long pipe roof, and parameter optimization were employed to ensure the safety of loess metro tunnelling under an existing glass building. Their effects were proved through monitoring the settlement of building and surface during tunnelling. Besides, division of settlement monitoring according to processes, a new method, was conducted to control settlement in time. The highest surface settlement after construction was 16 mm only, meeting the requirement. The result indicates that it is practicable to control the tunnelling settlement strictly in extremely difficult geological areas. The settlement regularities were also studied through numerical simulation; their deformation is larger compared with in situ results while their change trends coincide during most processes. Soil excavations cause settlement primarily, accounting for more than 60%. It is suggested that dual slurry pre-grouting and process-based measurement should be employed before each excavation in water-rich loess areas.

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Section: Introductionmentioning

confidence: 99%

Tunnelling‐Induced Settlement and Treatment Techniques for a Loess Metro in Xi’an

Lai

et al. 2020

Advances in Civil Engineering

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“…Moreover, O'Reilly and New [6] refined and improved Peck's model by adopting Gaussian curves to describe short-term ground settlement over a tunnel. Mohammadi et al [19] used statistical software to perform multiple regression analysis, aiming at obtaining linear or nonlinear models to predict the maximum ground settlement. Although these formulae can generally present the maximum ground settlements under different soil types, the prediction accuracies of these formulae are uncertain due to uncertainties of ground conditions and the execution process since those aspects cannot be fully understood at the design stage [4,9].…”

Section: Empirical Methods For the Prediction Of Ground Settlementsmentioning

confidence: 99%

ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors

Zhang

et al. 2022

Applied Sciences

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Accurately and dynamically predicting ground settlements during the construction of foundation pits is pivotal to the understanding of the potential risk of foundation pits and, therefore, enables constructors to take timely and effective actions to ensure the construction safety of foundation pits. Existing settlement prediction methods mainly focus on the prediction of the maximum ground settlements based on static influence factors, such as soil properties and the geometry of foundation pits. However, these methods are unable to be applied to the prediction of daily ground settlements in a direct way because daily ground settlements can be affected by many time-dependent influence factors, and an accurate prediction of daily ground settlements should take into consideration such factors. To address this problem, this paper proposes an artificial neural network-based daily ground settlement prediction method, where both static and time-dependent influence factors, as well as previous settlement monitoring data, are considered in the optimum artificial neural network. The proposed method is validated using data collected from a real cut-and-cover highway tunnel project in western Hangzhou, China. The results demonstrate that time-dependent influence factors and previous settlement monitoring data play vital roles in establishing an optimum artificial neural network for the accurate prediction of daily ground settlement.

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“…Their simulation results indicated that the proposed ANNW models could accurately predict surface settlement; however, the input parameters also ignored the geological parameters. As a result, considering the soil properties in predicting the impact of tunneling on ground deformation received later significant attention from many researchers [10,17,18]. 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.…”

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

confidence: 99%

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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Development of artificial neural networks and multiple regression models for the NATM tunnelling-induced settlement in Niayesh subway tunnel, Tehran

Cited by 42 publications

References 32 publications

Tunnelling‐Induced Settlement and Treatment Techniques for a Loess Metro in Xi’an

Tunnelling‐Induced Settlement and Treatment Techniques for a Loess Metro in Xi’an

ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors

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

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