Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/87494
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorZhang, Pen_US
dc.creatorYin, ZYen_US
dc.creatorJin, YFen_US
dc.creatorChan, THTen_US
dc.creatorGao, FPen_US
dc.date.accessioned2020-07-16T03:57:30Z-
dc.date.available2020-07-16T03:57:30Z-
dc.identifier.urihttp://hdl.handle.net/10397/87494-
dc.language.isoenen_US
dc.publisherZhongguo Dizhi Daxue,China University of Geosciencesen_US
dc.rights©2020 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)en_US
dc.rightsThe following publication Zhang, P., Yin, Z. Y., Jin, Y. F., Chan, T. H., & Gao, F. P. (2020). Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms. Geoscience Frontiers, is available at https://doi.org/10.1016/j.gsf.2020.02.014en_US
dc.subjectClaysen_US
dc.subjectCompressibilityen_US
dc.subjectGenetic algorithmen_US
dc.subjectMachine learningen_US
dc.subjectOptimizationen_US
dc.subjectRandom foresten_US
dc.titleIntelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithmsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1016/j.gsf.2020.02.014en_US
dcterms.abstractCompression index Cc is an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge. This paper suggests a novel modelling approach using machine learning (ML) technique. The performance of five commonly used machine learning (ML) algorithms, i.e. back-propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), random forest (RF) and evolutionary polynomial regression (EPR) in predicting Cc is comprehensively investigated. A database with a total number of 311 datasets including three input variables, i.e. initial void ratio e0, liquid limit water content wL, plasticity index Ip, and one output variable Cc is first established. Genetic algorithm (GA) is used to optimize the hyper-parameters in five ML algorithms, and the average prediction error for the 10-fold cross-validation (CV) sets is set as the fitness function in the GA for enhancing the robustness of ML models. The results indicate that ML models outperform empirical prediction formulations with lower prediction error. RF yields the lowest error followed by BPNN, ELM, EPR and SVM. If the ranges of input variables in the database are large enough, BPNN and RF models are recommended to predict Cc. Furthermore, if the distribution of input variables is continuous, RF model is the best one. Otherwise, EPR model is recommended if the ranges of input variables are small. The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationGeoscience frontiers, 2020en_US
dcterms.isPartOfGeoscience frontiersen_US
dcterms.issued2020-
dc.identifier.scopus2-s2.0-85082006692-
dc.identifier.eissn1674-9871en_US
dc.description.validate202007 bcma-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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