Physics-informed deep learning method for predicting tunnelling-induced ground deformations

Zhang, Zilong; Pan, Qiujing; Yang, Zihan; Yang, Xiaoli

doi:10.1007/s11440-023-01874-9

Cited by 24 publications

(2 citation statements)

References 61 publications

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“…However, existing neural network models mainly rely on monitoring data for calculations during the training process and fail to incorporate the underlying physical laws of engi-neering problems, leading to poor model interpretability and a lack of generalization in analysis results. Therefore, there is a need to establish a physics-informed neural network (PINN) model [77] that integrates both a physics model and a data-driven model. By deeply analyzing the relationship between data laws and physical mechanisms in conjunction with known physics mechanisms and the deep learning framework, this approach aims to facilitate applied research in complex engineering scenarios.…”

Section: Prediction Of Settlement Valuementioning

confidence: 99%

A Review of Deep Learning Applications in Tunneling and Underground Engineering in China

Su,

Hu,

Yang

et al. 2024

Applied Sciences

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With the advent of the era of big data and information technology, deep learning (DL) has become a hot trend in the research field of artificial intelligence (AI). The use of deep learning methods for parameter inversion, disease identification, detection, surrounding rock classification, disaster prediction, and other tunnel engineering problems has also become a new trend in recent years, both domestically and internationally. This paper briefly introduces the development process of deep learning. By reviewing a number of published papers on the application of deep learning in tunnel engineering over the past 20 years, this paper discusses the intelligent application of deep learning algorithms in tunnel engineering, including collapse risk assessment, water inrush prediction, crack identification, structural stability evaluation, and seepage erosion in mountain tunnels, urban subway tunnels, and subsea tunnels. Finally, it explores the future challenges and development prospects of deep learning in tunnel engineering.

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Section: Prediction Of Settlement Valuementioning

confidence: 99%

A Review of Deep Learning Applications in Tunneling and Underground Engineering in China

Su,

Hu,

Yang

et al. 2024

Applied Sciences

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“…Expanding research in predicting foundation settlement could focus on the development of hybrid models integrating both physical mechanics and statistical approaches, allowing for a more comprehensive understanding of the multifaceted nature of subway track foundation settlement [7]. Additionally, exploring advanced machine learning techniques capable of handling nonlinear relationships and complex interactions among various influencing factors could offer enhanced predictive capabilities.…”

Section: Introductionmentioning

confidence: 99%

Subway track foundation settlement deformation prediction based on the BiLSTM-AdaBoost model

Dang,

Yin,

Liu

et al. 2024

Eng. Res. Express

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The rapid economic expansion has spurred extensive construction near subway networks, im-pacting the stability of their track foundations. Consequently, it's crucial to monitor and predict settlement in subway track foundations. However, the dynamic deformation patterns often exhibit nonlinearity and non-stationarity, posing challenges for traditional linear regression models. To tackle this, our study integrates the BiLSTM (bi-directional long short-term memory) network with the AdaBoost ensemble learning algorithm. Using settlement data from Shanghai metro monitor-ing points, the model is trained and evaluated employing R2 (coefficient of determination), MAE (mean absolute error), and RMSE (root mean square error). Results show that our proposed model displays superior predictive accuracy compared to the LSTM and the BiLSTM, with an average training set R2 of 0.99, test set R2 of 0.78, average MAE of 0.32mm, and average RMSE of 0.4mm. Consequently, for forecasting subway track foundation deformations, employing our network model ensures highly accurate predictive capabilities.

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Yaghoubi,

Khamees

et al. 2024

Neural Comput & Applic

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Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

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Physics-informed deep learning method for predicting tunnelling-induced ground deformations

Cited by 24 publications

References 61 publications

A Review of Deep Learning Applications in Tunneling and Underground Engineering in China

A Review of Deep Learning Applications in Tunneling and Underground Engineering in China

Subway track foundation settlement deformation prediction based on the BiLSTM-AdaBoost model

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

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