Optimal AI Model for Accurate Classification of Squeezing in Underground Structures

Assessing ground conditions with regard to the potential for squeezing and the behavior of geological structures is a crucial aspect of designing underground structures. Tunnel construction in conditions where squeezing occurs can be a challenging and time-consuming process. Recognizing and evaluati...

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Bibliographic Details
Published in:Geotechnical and geological engineering Vol. 43; no. 2; p. 65
Main Authors: Fattahi, Hadi, Mohtarami, Ehsan
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.02.2025
Springer Nature B.V
Subjects:
Artificial intelligence
Civil Engineering
Classification
Compressing
Construction
Decision trees
Earth and Environmental Science
Earth Sciences
Evaluation
Excavation
Geological structures
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Neural networks
Original Paper
Risk reduction
Support systems
Support vector machines
Terrestrial Pollution
Tunnel construction
Tunnels
Underground construction
Underground structures
Waste Management/Waste Technology
Weak rock
Underground structures
Squeezing phenomenon
Classification models
Artificial intelligence
ISSN:0960-3182, 1573-1529
Online Access:Get full text
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Summary:Assessing ground conditions with regard to the potential for squeezing and the behavior of geological structures is a crucial aspect of designing underground structures. Tunnel construction in conditions where squeezing occurs can be a challenging and time-consuming process. Recognizing and evaluating the potential for squeezing is essential in determining the appropriate excavation method and support system, particularly in weak rock formations. This study introduces an innovative application of multiple artificial intelligence (AI) methods, comparing their effectiveness in accurately classifying tunnel squeezing based on real-world data. Unlike previous works that focused on empirical or single-model approaches, this research systematically evaluates and optimizes six AI-based classifiers (Decision Trees (DT), K-nearest Neighbors (KNN), Neural Networks (NN), Naive Bayes (NB), Support Vector Machines (SVM), and Ensemble Classification (EC)) using a dataset of 114 tunnel cases. The results reveal that the DT model outperforms other classifiers with an accuracy rate of approximately 0.98. This finding is significant because it presents a reliable AI-driven method for classifying tunnel squeezing, offering engineers and designers a robust tool for mitigating risks in tunnel construction.
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ISSN:0960-3182
1573-1529
DOI:10.1007/s10706-024-03007-9