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Broad Learning System for Nonparametric Modeling of Clay Parameters
Sin-Chi Kuok1,2; Ka-Veng Yuen1
2020-04-14
Source PublicationASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
ISSN2376-7642
Volume6Issue:2
Abstract

Due to the complex and uncertain nature of geomaterial properties, establishing representative parametric models between clay parameters is a challenging task. Nonparametric machine learning offers an accessible approach to develop empirical transformation models of clay parameters based on the available measurement. In this study, nonparametric modeling of clay parameter relationships via broad learning system (BLS) is introduced. Broad learning architecture provides an effective tool for nonparametric modeling based on noise-corrupted data. The architecture of deep learning is configured with stacks of hierarchical layers, which consume expensive computational cost for network training. In contrast, the network of BLS is established in a flat architecture and it can be modified incrementally. As a result, the broad learning flat network can be reconfigured efficiently to accommodate additional training data. To demonstrate the performance of the learning algorithm for clay parameters, the comprehensive global database CLAY/10/7490 with 7,490 data points from over 250 studies worldwide is utilized and analyzed.

DOI10.1061/AJRUA6.0001066
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Civil
WOS IDWOS:000527936400022
PublisherASCE-AMER SOC CIVIL ENGINEERS, 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400
Scopus ID2-s2.0-85083456680
Fulltext Access
FWCI0.4303612
Citation statistics
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorKa-Veng Yuen
Affiliation1.State Key Laboratory of Internet of Things for Smart City, Dept. of Civil and Environmental Engineering, Univ. of Macau, Macao 999078, China
2.Academic Visitor, Dept. of Engineering Science, Univ. of Oxford, Oxford OX1 2JD, UK
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Sin-Chi Kuok,Ka-Veng Yuen. Broad Learning System for Nonparametric Modeling of Clay Parameters[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering,2020,6(2).
APA Sin-Chi Kuok,&Ka-Veng Yuen.(2020).Broad Learning System for Nonparametric Modeling of Clay Parameters.ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering,6(2).
MLA Sin-Chi Kuok,et al."Broad Learning System for Nonparametric Modeling of Clay Parameters".ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 6.2(2020).
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