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.

在土木工程中，仪表对于在结构的构建和运行过程中进行监视是有益的。收集的数据可用于观察实时响应并开发数据驱动的模型以预测未来的行为。然而，由于成本限制和实用性，通常用于现场土木工程施工的传感器数量有限。这导致相对较小的原始数据集，其中通常包含错误和异常。解释和明智地使用可用数据集来开发可靠的预测模型是一项重大挑战。因此，必须进行预处理和清理数据以提高其质量。至今，在将这种数据清洗方法应用于从大规模站点收集的岩土工程数据集方面，几乎没有进行任何调查。这项研究的目的是将简单有效的数据预处理技术应用于从高速公路路堤上采集的站点数据，该高速公路路堤由一系列具有不同物理组成和非线性固结特征的土层构成。将各种清洁方法应用于磁引伸计数据，以监测路堤下方基础土壤中的沉降。PCA被用于探索原始数据，识别并消除异常值。使用了多种滤波和平滑方法来清除数据中的噪声，并使用RMSE和NMSE进一步比较了它们的结果。事实证明，用于数据预处理和清理的方法对于捕获现场的原始沉降行为非常有效。这项研究的发现对于路堤施工引起的地面响应相关的复杂决策，对于现场工程师而言非常有用。这对于开发路堤沉降动态预测模型也具有积极的前景。