RESEARCH ARTICLE
ARMA Models to Measure the Scale of Fluctuation from CPT Data
Brigid Cami1, Sina Javankhoshdel2, *
Article Information
Identifiers and Pagination:
Year: 2020Volume: 14
Issue: Suppl-1, M4
First Page: 230
Last Page: 236
Publisher ID: TOBCTJ-14-230
DOI: 10.2174/1874836802014010230
Article History:
Received Date: 23/12/2019Revision Received Date: 03/03/2020
Acceptance Date: 18/05/2020
Electronic publication date: 24/08/2020
Collection year: 2020
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Objective:
Spatial variability is one of the largest sources of uncertainty in geotechnical applications. This variability is primarily characterized by the scale of fluctuation, a parameter that describes the distance over which the parameters of a material are similar. Spatial variability is generally described with traditional methods of time series analysis. In statistics, the Auto-Regressive Moving Average (ARMA) model is commonly used to describe the relationship between two points in time. Instead of assuming an autocorrelation model, the ARMA model calculates the necessary auto-regressive components (AR), as well as a decaying Mean Structure (MA). The advantage of this method is that it is calculated for each specific field study, so that the data is not forced to fit into a fixed autocorrelation model (e.g. Markovian, Gaussian, etc).
Methods:
In this study, the ARMA model is introduced as a means of measuring scale of fluctuation, and two case studies and a simulation are used to compare the scale of fluctuation values from the ARMA model to the other estimates.
Results:
In the first case study, the ARMA model estimated a value of 0.26 m while the other methods ranged from 0.22-0.29 m. In the second case study, the ARMA model estimated a value of 0.40 m while the other methods ranged from 0.40-0.54 m. In the simulated example, where the true value was 5.0 m, the ARMA model estimated a value of 4.73 m while the other methods ranged from 3.24-3.51 m.
Conclusion:
This paper concludes that ARMA is a promising new method for estimating the scale of fluctuation but requires a considerable amount of research before it can become established in the geotechnical sphere.
