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

Nie, Cong; Zhang, Dong Ming; Ouyang, Linghan; Huang, Xu; Zhang, Bo; Tong, Yue

doi:10.3390/geosciences13070189

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…Increase in face pressure force [15,[17][18][19] Although the influences of these operation parameters are identified, the quantitative measurements of these operation parameters on jacking forces remain absent. Recent research has shown the use of machine learning and deep learning techniques in the pipe jacking process, such as predicting the changes in geological conditions [26][27][28][29], changes in ground settlement [30][31][32][33][34][35] and prediction of various operation parameters [36][37][38][39][40]. Hence, this paper will use deep learning techniques, such as gated recurrent units (GRUs) with an attention mechanism, to predict jacking forces through a region of weathered phyllite based on pipe jacking operation parameters as the input features.…”

Section: Jacking Speedmentioning

confidence: 99%

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Yeo,

Phangkawira,

Kueh

et al. 2024

Geosciences

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Pipe jacking is a trenchless technology used to install buried pipelines, such as sewer lines in wastewater management systems. Existing mechanistic approaches based on geomaterial strength parameters (i.e., friction angle and apparent cohesion) can provide an estimation of the potential jacking forces during construction. However, extracting intact rock cores for strength characterisation is challenging when dealing with highly weathered ‘soft rocks’ which exhibit RQD values of zero. Such was the case for a pipe jacking drive traversing the highly weathered lithology underlying Kuching City, Malaysia. Furthermore, mechanistic approaches face limitations during construction when jacking forces are dependent on operation parameters, such as jacking speed and lubrication. To address these knowledge gaps, the primary objectives of this study are the development of rock strength parameters based on in situ pressuremeter testing for the purpose of estimating jacking forces. Furthermore, this study investigates the influence of various pipe jacking operation parameters, with a particular focus on their impact on jacking forces in weathered ‘soft rocks’. To achieve this, a novel deep learning model with an attention mechanism is introduced. The proposed methods of rock strength parameters derived from pressuremeter testing and the utilisation of deep learning will help to provide insights into the key factors affecting the development of jacking forces. This paper successfully shows the use of in situ pressuremeter testing in developing Mohr–Coulomb (MC) parameters directly from the site. In addition, the developed deep learning model with an attention mechanism successfully highlights the significance of pipe jacking operation parameters with an accuracy of 88% in predicting the jacking forces.

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Section: Jacking Speedmentioning

confidence: 99%

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Yeo,

Phangkawira,

Kueh

et al. 2024

Geosciences

Self Cite

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Prediction and Control of Existing High-Speed Railway Tunnel Deformation Induced by Shield Undercrossing Based on BO-XGboost

Fei,

Wu,

Peng

2024

Sustainability

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The settlement of existing high-speed railway tunnels due to adjacent excavations is a complex phenomenon influenced by multiple factors, making accurate estimation challenging. To address this issue, a prediction model combining extreme gradient boosting (XGBoost) with Bayesian optimization (BO), namely BO-XGBoost, was developed. Its predictive performance was evaluated against conventional models, such as artificial neural networks (ANNs), support vector machines (SVMs), and vanilla XGBoost. The BO-XGBoost model showed superior results, with evaluation metrics of MAE = 0.331, RMSE = 0.595, and R2 = 0.997. In addition, the BO-XGBoost model enhanced interpretability through an accessible analysis of feature importance, identifying volume loss as the most critical factor affecting settlement predictions. Using the prediction model and a particle swarm optimization (PSO) algorithm, a hybrid framework was established to adjust the operational parameters of a shield tunneling machine in the Changsha Metro Line 3 project. This framework facilitates the timely optimization of operational parameters and the implementation of protective measures to mitigate excessive settlement. With this framework’s assistance, the maximum settlements of the existing tunnel in all typical sections were strictly controlled within safety criteria. As a result, the corresponding environmental impact was minimized and resource management was optimized, ensuring construction safety, operational efficiency, and long-term sustainability.

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

Cited by 2 publications

References 28 publications

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Prediction and Control of Existing High-Speed Railway Tunnel Deformation Induced by Shield Undercrossing Based on BO-XGboost

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