| Affiliation: | 1. School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK;2. School of Health and Life Sciences, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK;1. School of Automobile, Chang’an University, Xi’an 710064, China;2. Shandong Transportation Institute, Jinan, Shandong 250102, China;3. Shandong Jianzhu University, Jinan, Shandong 250101, China;1. School of Business, Shanghai Dianji University, Shanghai, China;2. School of Mechanical Engineering, Tongji University, Shanghai, China;1. Department of Civil & Environmental Engineering, Universidad de los Andes, Bogotá, Colombia;2. Department of Civil Engineering, Faculty of Engineering of University of Porto, Porto, Portugal |
| 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. |
