Publication:
Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment

dc.citedby22
dc.contributor.authorAljanabi Q.A.en_US
dc.contributor.authorChik Z.en_US
dc.contributor.authorAllawi M.F.en_US
dc.contributor.authorEl-Shafie A.H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid55786638200en_US
dc.contributor.authorid55522804700en_US
dc.contributor.authorid57057678400en_US
dc.contributor.authorid57207789882en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T06:51:02Z
dc.date.available2023-05-29T06:51:02Z
dc.date.issued2018
dc.descriptionClay; Embankments; Forecasting; Highway engineering; Mean square error; Settlement of structures; Vectors; Mathematical procedures; Root mean square errors; Settlement behaviors; Settlement prediction; Settlement prediction models; Soft clays; Stone column; Support vector regression (SVR); Support vector machinesen_US
dc.description.abstractIn order to have a proper design and analysis for the column of stone in the soft clay soil, it is essential to develop an accurate prediction model for the settlement behavior of the stone column. In the current research, to predict the behavior in the settlement of stone column a support vector machine (SVM) method is developed and examined. In addition, the proposed model has been compared with the existing reference settlement prediction model that using the monitored field data. As SVM mathematical procedure has resilient and robust generalization aptitude and ensures searching for global minima for particular training data as well. Therefore, the potential that support vector regression might perform efficiently to predict the ground soft clay settlement is relatively valuable. As a result, in this study, comparison of two different developed types of SVM method is carried out. Generally, significant reduction in the relative error (RE%) and root mean square error has been achieved. Utilizing nu-SVM-type model through tenfold cross-validation procedure could achieve outstanding performance accuracy level with RE% less than 2% and CR�=�0.9987. The study demonstrates high potential for applying SVM in detecting the settlement behavior of SC prediction and ascertains that SVM could be effectively used for settlement stone columns analysis. � 2017, The Natural Computing Applications Forum.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s00521-016-2807-5
dc.identifier.epage2469
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85008620710
dc.identifier.spage2459
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85008620710&doi=10.1007%2fs00521-016-2807-5&partnerID=40&md5=9d1d57a50ab2daedac3c8d7248e26bd1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23688
dc.identifier.volume30
dc.publisherSpringer Londonen_US
dc.sourceScopus
dc.sourcetitleNeural Computing and Applications
dc.titleSupport vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankmenten_US
dc.typeArticleen_US
dspace.entity.typePublication
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