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AI-Driven Prediction of Tunneling Squeezing: Comparing Rock Classification Systems
Understanding tunneling-induced ground deformation, particularly squeezing behavior, is crucial for safe and efficient underground construction. This study employs Artificial Neural Networks (ANN) and Multivariate Adaptive Regression Splines (MARS) models to predict tunneling squeezing behavior usin...
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| Published in: | Geotechnical and geological engineering 2024-05, Vol.42 (3), p.2127-2149 |
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| cites | cdi_FETCH-LOGICAL-a342t-487d93b61915ab0292208ed675f2df0ccb2fce5b105aac802e07ba987f5df4fe3 |
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| container_title | Geotechnical and geological engineering |
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| creator | Al-Sadoon, Zaid A. Alotaibi, Emran Omar, Maher Arab, Mohamed G. Tahmaz, Ali |
| description | Understanding tunneling-induced ground deformation, particularly squeezing behavior, is crucial for safe and efficient underground construction. This study employs Artificial Neural Networks (ANN) and Multivariate Adaptive Regression Splines (MARS) models to predict tunneling squeezing behavior using various rock classification systems, namely rock quality index (Q), rock mass rating (RMR), and geological strength index (GSI). The objective is to assess model performance, evaluate the influence of classification systems, and conduct sensitivity analyses on key parameters. The investigation reveals that both ANN and MARS models exhibit enhanced accuracy as model complexity increases, up to a critical point where overfitting occurs. Comparing model performance, ANN outperforms MARS, and the most accurate ANN model is identified as ANN50-RMR with an R
2
of 0.978. This confirms the ANN’s capability to capture non-linear relationships inherent in tunneling-induced ground deformation. Choosing a rock classification system as an input parameter significantly impacts model accuracy. RMR and GSI classification systems exhibit improved performance over the conventional Q-system. In particular, GSI-based models offer more consistent and accurate predictions, emphasizing GSI’s suitability for modeling tunneling squeezing behavior. Variables’ importance analysis elucidates the dependence of parameter relevance on the chosen classification system. Sensitivity analyses on tunnel depth, diameter, and rock mass deformation modulus reveal logical correlations between these parameters and tunnel squeezing behavior, further validating model predictions. By enhancing our understanding of tunneling-induced ground deformation, these models contribute to safer and more efficient underground construction practices. |
| doi_str_mv | 10.1007/s10706-023-02665-5 |
| format | article |
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2
of 0.978. This confirms the ANN’s capability to capture non-linear relationships inherent in tunneling-induced ground deformation. Choosing a rock classification system as an input parameter significantly impacts model accuracy. RMR and GSI classification systems exhibit improved performance over the conventional Q-system. In particular, GSI-based models offer more consistent and accurate predictions, emphasizing GSI’s suitability for modeling tunneling squeezing behavior. Variables’ importance analysis elucidates the dependence of parameter relevance on the chosen classification system. Sensitivity analyses on tunnel depth, diameter, and rock mass deformation modulus reveal logical correlations between these parameters and tunnel squeezing behavior, further validating model predictions. By enhancing our understanding of tunneling-induced ground deformation, these models contribute to safer and more efficient underground construction practices.</description><identifier>ISSN: 0960-3182</identifier><identifier>EISSN: 1573-1529</identifier><identifier>DOI: 10.1007/s10706-023-02665-5</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Accuracy ; Artificial neural networks ; Civil Engineering ; Classification ; Classification systems ; Compressing ; Construction ; Critical point ; Deformation ; Earth and Environmental Science ; Earth Sciences ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Mathematical models ; Model accuracy ; Modulus of deformation ; Neural networks ; Original Paper ; Parameter sensitivity ; Parameters ; Performance evaluation ; Predictions ; Regression analysis ; Rock ; Rock mass rating ; Rocks ; Sensitivity analysis ; Splines ; Terrestrial Pollution ; Tunneling ; Tunnels ; Underground construction ; Waste Management/Waste Technology</subject><ispartof>Geotechnical and geological engineering, 2024-05, Vol.42 (3), p.2127-2149</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a342t-487d93b61915ab0292208ed675f2df0ccb2fce5b105aac802e07ba987f5df4fe3</citedby><cites>FETCH-LOGICAL-a342t-487d93b61915ab0292208ed675f2df0ccb2fce5b105aac802e07ba987f5df4fe3</cites><orcidid>0000-0002-4765-0639</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,786,790,27957,27958</link.rule.ids></links><search><creatorcontrib>Al-Sadoon, Zaid A.</creatorcontrib><creatorcontrib>Alotaibi, Emran</creatorcontrib><creatorcontrib>Omar, Maher</creatorcontrib><creatorcontrib>Arab, Mohamed G.</creatorcontrib><creatorcontrib>Tahmaz, Ali</creatorcontrib><title>AI-Driven Prediction of Tunneling Squeezing: Comparing Rock Classification Systems</title><title>Geotechnical and geological engineering</title><addtitle>Geotech Geol Eng</addtitle><description>Understanding tunneling-induced ground deformation, particularly squeezing behavior, is crucial for safe and efficient underground construction. This study employs Artificial Neural Networks (ANN) and Multivariate Adaptive Regression Splines (MARS) models to predict tunneling squeezing behavior using various rock classification systems, namely rock quality index (Q), rock mass rating (RMR), and geological strength index (GSI). The objective is to assess model performance, evaluate the influence of classification systems, and conduct sensitivity analyses on key parameters. The investigation reveals that both ANN and MARS models exhibit enhanced accuracy as model complexity increases, up to a critical point where overfitting occurs. Comparing model performance, ANN outperforms MARS, and the most accurate ANN model is identified as ANN50-RMR with an R
2
of 0.978. This confirms the ANN’s capability to capture non-linear relationships inherent in tunneling-induced ground deformation. Choosing a rock classification system as an input parameter significantly impacts model accuracy. RMR and GSI classification systems exhibit improved performance over the conventional Q-system. In particular, GSI-based models offer more consistent and accurate predictions, emphasizing GSI’s suitability for modeling tunneling squeezing behavior. Variables’ importance analysis elucidates the dependence of parameter relevance on the chosen classification system. Sensitivity analyses on tunnel depth, diameter, and rock mass deformation modulus reveal logical correlations between these parameters and tunnel squeezing behavior, further validating model predictions. By enhancing our understanding of tunneling-induced ground deformation, these models contribute to safer and more efficient underground construction practices.</description><subject>Accuracy</subject><subject>Artificial neural networks</subject><subject>Civil Engineering</subject><subject>Classification</subject><subject>Classification systems</subject><subject>Compressing</subject><subject>Construction</subject><subject>Critical point</subject><subject>Deformation</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydrogeology</subject><subject>Mathematical models</subject><subject>Model accuracy</subject><subject>Modulus of deformation</subject><subject>Neural networks</subject><subject>Original Paper</subject><subject>Parameter sensitivity</subject><subject>Parameters</subject><subject>Performance evaluation</subject><subject>Predictions</subject><subject>Regression analysis</subject><subject>Rock</subject><subject>Rock mass rating</subject><subject>Rocks</subject><subject>Sensitivity analysis</subject><subject>Splines</subject><subject>Terrestrial Pollution</subject><subject>Tunneling</subject><subject>Tunnels</subject><subject>Underground construction</subject><subject>Waste Management/Waste Technology</subject><issn>0960-3182</issn><issn>1573-1529</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKt_wNOC5-gkaTYbb2X9KghKW88hm01ka7tZk61Qf71pV_DmYZhheN_5eBC6JHBNAMRNJCAgx0BZijznmB-hEeGCYcKpPEYjkDlgRgp6is5iXAEkGZARmk9n-C40X7bNXoOtG9M3vs28y5bbtrXrpn3PFp9ba79TdZuVftPpsG_OvfnIyrWOsXGN0QfXYhd7u4nn6MTpdbQXv3mM3h7ul-UTfn55nJXTZ6zZhPZ4UohasionknBdAZWUQmHrXHBHawfGVNQZyysCXGtTALUgKi0L4XjtJs6yMboa5nbBpxNjr1Z-G9q0UjGgkHNaSJZUdFCZ4GMM1qkuNBsddoqA2rNTAzuV2KkDO8WTiQ2m2O2_teFv9D-uH_MBceM</recordid><startdate>20240501</startdate><enddate>20240501</enddate><creator>Al-Sadoon, Zaid A.</creator><creator>Alotaibi, Emran</creator><creator>Omar, Maher</creator><creator>Arab, Mohamed G.</creator><creator>Tahmaz, Ali</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TN</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><orcidid>https://orcid.org/0000-0002-4765-0639</orcidid></search><sort><creationdate>20240501</creationdate><title>AI-Driven Prediction of Tunneling Squeezing: Comparing Rock Classification Systems</title><author>Al-Sadoon, Zaid A. ; Alotaibi, Emran ; Omar, Maher ; Arab, Mohamed G. ; Tahmaz, Ali</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a342t-487d93b61915ab0292208ed675f2df0ccb2fce5b105aac802e07ba987f5df4fe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Artificial neural networks</topic><topic>Civil Engineering</topic><topic>Classification</topic><topic>Classification systems</topic><topic>Compressing</topic><topic>Construction</topic><topic>Critical point</topic><topic>Deformation</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Hydrogeology</topic><topic>Mathematical models</topic><topic>Model accuracy</topic><topic>Modulus of deformation</topic><topic>Neural networks</topic><topic>Original Paper</topic><topic>Parameter sensitivity</topic><topic>Parameters</topic><topic>Performance evaluation</topic><topic>Predictions</topic><topic>Regression analysis</topic><topic>Rock</topic><topic>Rock mass rating</topic><topic>Rocks</topic><topic>Sensitivity analysis</topic><topic>Splines</topic><topic>Terrestrial Pollution</topic><topic>Tunneling</topic><topic>Tunnels</topic><topic>Underground construction</topic><topic>Waste Management/Waste Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Al-Sadoon, Zaid A.</creatorcontrib><creatorcontrib>Alotaibi, Emran</creatorcontrib><creatorcontrib>Omar, Maher</creatorcontrib><creatorcontrib>Arab, Mohamed G.</creatorcontrib><creatorcontrib>Tahmaz, Ali</creatorcontrib><collection>CrossRef</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Geotechnical and geological engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Al-Sadoon, Zaid A.</au><au>Alotaibi, Emran</au><au>Omar, Maher</au><au>Arab, Mohamed G.</au><au>Tahmaz, Ali</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AI-Driven Prediction of Tunneling Squeezing: Comparing Rock Classification Systems</atitle><jtitle>Geotechnical and geological engineering</jtitle><stitle>Geotech Geol Eng</stitle><date>2024-05-01</date><risdate>2024</risdate><volume>42</volume><issue>3</issue><spage>2127</spage><epage>2149</epage><pages>2127-2149</pages><issn>0960-3182</issn><eissn>1573-1529</eissn><abstract>Understanding tunneling-induced ground deformation, particularly squeezing behavior, is crucial for safe and efficient underground construction. 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2
of 0.978. This confirms the ANN’s capability to capture non-linear relationships inherent in tunneling-induced ground deformation. Choosing a rock classification system as an input parameter significantly impacts model accuracy. RMR and GSI classification systems exhibit improved performance over the conventional Q-system. In particular, GSI-based models offer more consistent and accurate predictions, emphasizing GSI’s suitability for modeling tunneling squeezing behavior. Variables’ importance analysis elucidates the dependence of parameter relevance on the chosen classification system. Sensitivity analyses on tunnel depth, diameter, and rock mass deformation modulus reveal logical correlations between these parameters and tunnel squeezing behavior, further validating model predictions. By enhancing our understanding of tunneling-induced ground deformation, these models contribute to safer and more efficient underground construction practices.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s10706-023-02665-5</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-4765-0639</orcidid></addata></record> |
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| subjects | Accuracy Artificial neural networks Civil Engineering Classification Classification systems Compressing Construction Critical point Deformation Earth and Environmental Science Earth Sciences Geotechnical Engineering & Applied Earth Sciences Hydrogeology Mathematical models Model accuracy Modulus of deformation Neural networks Original Paper Parameter sensitivity Parameters Performance evaluation Predictions Regression analysis Rock Rock mass rating Rocks Sensitivity analysis Splines Terrestrial Pollution Tunneling Tunnels Underground construction Waste Management/Waste Technology |
| title | AI-Driven Prediction of Tunneling Squeezing: Comparing Rock Classification Systems |
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