Data-Based Modeling Approaches for Short-Term Prediction of Embankment Settlement Using Magnetic Extensometer Time-Series Data
Publication: International Journal of Geomechanics
Volume 22, Issue 2
Abstract
Developing data-driven predictive models is highly desirable for monitoring the condition of infrastructure assets but is dependent on the generation of large data sets that are regularly updated. This represents a challenge in modern geotechnical infrastructure projects such as earth embankments, where the size of settlement monitoring data sets is generally small and of low resolution. While long-term settlement predictions for embankment structures are useful for design engineers, short-term predictions are more valuable to site engineers who are required to make operational decisions regarding construction. Their challenge is greater on sites where ground conditions are complex. The purpose of this study is to explore the applicability of parametric data-driven methods (namely polynomial curve fitting and transfer function methods) to forecast the trend of soil settlement in real-time using magnetic extensometer and embankment fill-level data. An industrial data set was sourced for a highway earth embankment, which was founded on a sequence of interbedded glacial soils. Polynomials models were more effective in predicting settlement during earlier stages of embankment construction when information on the influence of loading on settlement is limited. As this information grows, transfer functions are preferable in terms of quality of prediction. The findings from this study highlight the potential for wider use of data-driven approaches to assist in earth embankment construction.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party (soil Settlement data from Pegswood Moor, Morpeth Northern Bypass, UK received by AECOM Environment and Ground Engineering, Newcastle upon Tyne, UK). Direct request for these materials may be made to the provider as indicated in the Acknowledgments.
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request (MATLAB codes for the models).
Acknowledgments
Thanks go to Teesside University and the Doctoral Training Alliance (DTA) for funding this research. In addition, the authors would like to thank Northumberland County Council and AECOM Environment and Ground Engineering (Newcastle upon Tyne, UK) for providing all of the monitoring data from the Morpeth Northern Bypass.
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International Journal of Geomechanics
Volume 22 • Issue 2 • February 2022
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© 2021 American Society of Civil Engineers.
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Received: May 28, 2021
Accepted: Sep 21, 2021
Published online: Nov 22, 2021
Published in print: Feb 1, 2022
Discussion open until: Apr 22, 2022
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