Probabilistic prediction of squeezing in tunneling under high-stress conditions

Jimenez, Rafael E.; Recio, D.

doi:10.1201/b11646-321

Cited by 4 publications

(4 citation statements)

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“…e resultant average accuracy is slightly reduced from 88.1% to 84.0%, indicating that tunnel diameter does not seem to have a significant influence on the predictive capabilities of the proposed model. is observation is coincident with the study by Jimenez and Recio [37].…”

Section: Influence Of Support Stiffness and Tunnel Diametermentioning

confidence: 52%

“…Additionally, the empirical approach and the binary SVM classifier can only make a binary classification; that is, it can only predict whether squeezing occurs but cannot predict the severity of squeezing. Finally, it is important to mention that, as discussed by Jimenez and Recio [6,37], the proposed multiclass SVM model should not be considered as a "final solution" and it should be expected to be further updated and improved as more tunnel squeezing cases are included in the training database.…”

Section: Comparison With Traditional Methodsmentioning

confidence: 99%

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Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Sun

Feng

Yang

2018

Advances in Civil Engineering

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Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses. It could cause shield jamming, budget overruns, and construction delays and could even lead to tunnel instability and casualties. erefore, accurate prediction or identification of tunnel squeezing is extremely important in the design and construction of tunnels. is study presents a modified application of a multiclass support vector machine (SVM) to predict tunnel squeezing based on four parameters, that is, diameter (D), buried depth (H), support stiffness (K), and rock tunneling quality index (Q). We compiled a database from the literature, including 117 case histories obtained from different countries such as India, Nepal, and Bhutan, to train the multiclass SVM model. e proposed model was validated using 8-fold cross validation, and the average error percentage was approximately 11.87%. Compared with existing approaches, the proposed multiclass SVM model yields a better performance in predictive accuracy. More importantly, one could estimate the severity of potential squeezing problems based on the predicted squeezing categories/classes.

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Section: Influence Of Support Stiffness and Tunnel Diametermentioning

confidence: 52%

Section: Comparison With Traditional Methodsmentioning

confidence: 99%

Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Sun

Feng

Yang

2018

Advances in Civil Engineering

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show abstract

“…El análisis de deformaciones y de la interacción terreno-sostenimiento así como su evolución reológica constituyen fenómenos complejos difíciles de modelizar analítica y numéricamente. Dos claros ejemplos son los presentados en los Capítulos IV y V de esta tesis, ya publicados en [99,100,161] y [160], relativos a la predicción de squeezing y al diseño de pilares de carbón respectivamente. En ambos se implementa Aplicación de la regresión logística en la predicción empírica de fenómenos complejos en obras subterráneas: squeezing y rotura de pilares de carbón -11-un clasificador lineal de tipo regresión logística (ver §III.4), encontrándose unas adecuadas capacidades predictivas.…”

Section: I24 Análisis De Estabilidad De Excavacionesunclassified

“…Cabe destacar que los resultados del trabajo desarrollado en estos capítulos se han presentado en publicaciones científicas [99,161] y congresos internacionales [100,160].…”

Section: I4 Contenido Y Metodologíaunclassified