Broad Learning System for Nonparametric Modeling of Clay Parameters
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6, Issue 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.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies.
Acknowledgments
This work has been supported by the Science and Technology Development Fund of the Macau SAR Government under Research Grant No. 019/2016/A1 and by the Research Committee of University of Macau under Research Grant No. CPG2019-00023-FST. These generous supports are gratefully acknowledged.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6 • Issue 2 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Apr 24, 2019
Accepted: Jan 21, 2020
Published online: Apr 14, 2020
Published in print: Jun 1, 2020
Discussion open until: Sep 14, 2020
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