The use of PCA and signal processing techniques for processing time-based construction settlement data of road embankments

Faisal Siddiqui, Paul Sargent, Gary Montague

Research output: Contribution to journalArticlepeer-review

Abstract

Instrumentation is beneficial in civil engineering for monitoring structures during their construction and operation.
The data collected can be used to observe real-time response and develop data-driven models for predicting
future behaviour. However, a limited number of sensors are usually used for on-site civil engineering
construction due to cost restrictions and practicalities. This results in relatively small raw datasets, which often
contain errors and anomalies. Interpreting and making judicious use of the available dataset for developing
reliable predictive model represents a significant challenge. Therefore, it is essential to pre-process and clean the
data for improving their quality. To date, little investigation has been performed in the application of such data
cleaning methods to geotechnical engineering datasets collected from full-scale sites. The purpose of this study is
to apply simple and effective data pre-processing techniques to site-data collected from a highway embankment
constructed on a sequence of soil layers of different physical make-up and non-linear consolidation characteristics.
Various cleaning methods were applied to magnetic extensometer data collected for monitoring settlement
within foundation soils beneath the embankment. PCA was used to explore raw data, identify and remove
outliers. Numerous filtering and smoothing methods were used to clean noise in the data and their results were
further compared using RMSE and NMSE. The methods adopted for data pre-processing and cleaning proved very
effective for capturing the raw settlement behaviour on site. The findings from this study would be useful to site
engineers regarding complex decision-making relating to ground response due to embankment construction. This
also has positive prospects for developing dynamic prediction models for embankment settlement.
Original languageEnglish
Number of pages14
JournalAdvanced Engineering Informatics
Volume46
Issue number101181
DOIs
Publication statusPublished - 1 Oct 2020

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