Predicting jet-grout column diameter to mitigate the environmental impact using an artificial intelligence algorithm

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dc.contributor.authorWang, Zhi-Fengen
dc.contributor.authorCheng, Wen-Chiehen
dc.date.accessioned2022-04-26T12:55:32Zen
dc.date.available2022-04-26T12:55:32Zen
dc.date.issued2021-06en
dc.description.abstractThis paper describes an approach for predicting the diameter of a jet-grout column using the support vector regression (SVR) technique, which is regarded as a novel learning machine based upon recent advances in statistical theory, in which the combined effects of the construction (construction methods and jetting parameters) and soil properties (soil type and shearing resistance) are considered. Four different kernel functions, namely, a linear kernel function, polynomial kernel function, radial basis kernel function, and sigmoid kernel function, are integrated into the SVR technique. A large amount of field measured data on the diameter of jet-grout column are retrieved from the published literature for training and testing purposes. The results indicate that the SVR technique with a radial basis kernel function provides predictions closest to the measured results, whereas the prepared design charts enable the ability to significantly widen the application of the proposed approach to the areas of ground improvement and environmental protection.en
dc.description.notesThe research described in this study would not have been possible without financial supports from The Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Grant no. 2019JQ-114), the National Nature Science Foundation of China (NSFC) (Grant nos. 41702287 and 41807245) and the Fundamental Research Funds for the Central Universities (Grant no. 300102218517). These financial supports are gratefully acknowledged.en
dc.description.sponsorshipNatural Science Basic Research Plan in Shaanxi Province of China [2019JQ-114]; National Nature Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC) [41702287, 41807245]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [300102218517]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.undsp.2020.02.004en
dc.identifier.eissn2467-9674en
dc.identifier.issn2096-2754en
dc.identifier.issue3en
dc.identifier.urihttp://hdl.handle.net/10919/109749en
dc.identifier.volume6en
dc.language.isoenen
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectJet groutingen
dc.subjectSupport vector regressionen
dc.subjectMachine learningen
dc.subjectRadial basis functionen
dc.titlePredicting jet-grout column diameter to mitigate the environmental impact using an artificial intelligence algorithmen
dc.title.serialUnderground Spaceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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