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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Vydáno v:	Geotechnical and geological engineering Ročník 43; číslo 2; s. 65
Hlavní autoři:	Fattahi, Hadi, Mohtarami, Ehsan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.02.2025 Springer Nature B.V
Témata:	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
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Abstract	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.
AbstractList	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.
ArticleNumber	65
Author	Fattahi, Hadi Mohtarami, Ehsan
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Snippet	Assessing ground conditions with regard to the potential for squeezing and the behavior of geological structures is a crucial aspect of designing underground...
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SubjectTerms	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
Title	Optimal AI Model for Accurate Classification of Squeezing in Underground Structures
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