Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/87494
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | - |
dc.creator | Zhang, P | en_US |
dc.creator | Yin, ZY | en_US |
dc.creator | Jin, YF | en_US |
dc.creator | Chan, THT | en_US |
dc.creator | Gao, FP | en_US |
dc.date.accessioned | 2020-07-16T03:57:30Z | - |
dc.date.available | 2020-07-16T03:57:30Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/87494 | - |
dc.language.iso | en | en_US |
dc.publisher | Zhongguo Dizhi Daxue,China University of Geosciences | en_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.rights | The 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.2020.02.014 | en_US |
dc.subject | Clays | en_US |
dc.subject | Compressibility | en_US |
dc.subject | Genetic algorithm | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Optimization | en_US |
dc.subject | Random forest | en_US |
dc.title | Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.doi | 10.1016/j.gsf.2020.02.014 | en_US |
dcterms.abstract | Compression 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | Geoscience frontiers, 2020 | en_US |
dcterms.isPartOf | Geoscience frontiers | en_US |
dcterms.issued | 2020 | - |
dc.identifier.scopus | 2-s2.0-85082006692 | - |
dc.identifier.eissn | 1674-9871 | en_US |
dc.description.validate | 202007 bcma | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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1-s2.0-S1674987120300566-main.pdf | 2.94 MB | Adobe PDF | View/Open |
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