Application of the hybrid ACP/ANFIS method for the prediction of surface settlement induced by an earth pressure tunnel boring machine with consideration of the encountered geology

Bouayad, Djamila; Emeriault, Fabrice

doi:10.1201/b17017-61

Cited by 4 publications

(2 citation statements)

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“…The aftermath of the comparative analysis indicates the superiority of the MLP-ANN model compared to the SVR model. The results of comparative analysis for both models were in alignment with the results of similar studies in terms of performance (28,29,31).…”

Section: Comparative Analysis For Trained Modelssupporting

confidence: 82%

“…The use of soft computing in tunneling has led to an enhanced understanding of operational conditions and the identification of probable parametric correlations (27). Machine learning (ML) algorithms have been widely used in estimating tunneling-induced ground settlements where the empirical methods fail to precisely anticipate the ground deformations (28)(29)(30)(31). Ahangari et al (30) studied the application of Gene Expression Programming (GEP) and Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting subway settlements for a case study of 53 tunnels excavated with the NATM method.…”

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confidence: 99%

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Performance Evaluation of Artificial Neural Networks and Support Vector Regression in Tunneling-Induced Settlement Prediction Incorporating Umbrella Arch Method Characteristics

Arjmandazar Varjovi,

Rahmanpour,

Khosravi

et al. 2024

IJE

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Accurate settlement forecasting is essential for preventing severe structural and infrastructure damage. This paper investigates predicting tunneling-induced ground settlements using machine learning models. Empirical methods for estimating settlements are often imprecise and site-specific. Developing novel, accurate prediction methods is critical to avoid catastrophic damage. The umbrella arch method constrains deformations for initial stability before installing primary support. This study develops machine learning models to forecast settlements solely from umbrella arch parameters, disregarding soil properties. Multilayer perceptron artificial neural networks (MLP-ANN) and support vector regression (SVR) are applied. Results demonstrate machine learning outperforms empirical methods. The MLP-ANN surpasses SVR, with R 2 of 0.98 and 0.92, respectively. Strong correlation is observed between umbrella arch configuration and settlements. The suggested approach effectively estimates surface displacements lacking mechanical properties. Overall, this study supports machine learning, specifically MLP-ANN, as an efficient, reliable alternative to empirical methods for predicting tunneling-induced ground settlements from umbrella arch design.

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Section: Comparative Analysis For Trained Modelssupporting

confidence: 82%