Spatial and Temporal Analysis of Road Deformation based on Remote Sensing and Subsurface Exploration

Abstract

In the western part of the Netherlands, the soil contains mainly sand, peat, and clay which are known as soft soil layers. The buildings and infrastructures, such as roads, constructed on these soil layers are usually associated with substantial construction measures during the execution of the project and might suffer from damages induced by the post-construction deformations. In practice, one of the primary stages of road construction involves geotechnical in-situ investigations for determining the soil properties based on which the settlement is predicted through empirical models. There are several techniques for monitoring the post-construction deformation on roads, among which the most time and cost-efficient technique is advanced Differential InSAR (D-InSAR). Since no research has been dedicated to establishing a direct link between the geotechnical in situ measurements and deformation measurements, in this research, the main focus is to develop a fully data-driven methodology to model road deformation based on loading/unloading conditions and soil properties. The study area is the newly constructed part of the A4 highway (Delft-Schiedam) in the Netherlands.
The proposed methodology in this research consists of three steps. In the first step of the methodology, the measurements that represent soil properties, loading/unloading and deformation measurements should be determined and gathered. Cone Penetration Testing (CPT) measurements and boreholes are two freely available data sets that represent soil properties. Another important soil property is the variations in soil water content can be characterized by temperature and precipitation. The latest stage of loading/unloading history can be determined by comparing the elevation of the study area before and after the construction. Deformation time series produced by D-InSAR techniques are suitable measurements for investigating spatiotemporal deformations on roads. After determining pre-processing steps for each of the raw data sets, the relevant parameters from each data source are extracted. In the next step, the correlations and similarities between the soil properties, loading/unloading condition, and deformation are investigated. The last step deals with extracting suitable features from CPT profiles in order to use machine learning to model the relationship between soil properties, loading/unloading conditions, and deformation. To this end, the CPT profiles are segmented, then qualitative (soil types) or quantitative descriptors of the segments are used as features. To determine the soil classes, Support Vector Machines (SVM) classifier is used. The relationship between soil properties, loading/unloading and the linear rate of deformation is modeled through two tree-based algorithms, i.e. Random Forests and Gradient tree-boosting. The Pearson correlation and the coefficient of determination between soil properties, loading/unloading and the linear rate of deformation are 0.6 and 0.4, respectively. The correlation of deformation time series and temperature and precipitation is quite low and no consistent pattern could be found between the time delays. The soil classification by SVM classifier is more accurate compared to empirical charts. For the deformation modeling, the best performance metrics are obtained through the Gradient Boosting algorithm with quantitative descriptors as features, (Mean Absolute Error (MAE) is 1.1 mm/year, Root Mean Squared Error (RMSE) is 1.5 mm/year and the coefficient of determination is 0.5). In conclusion, the resulting models with different algorithms and different sets of features are of moderate accuracy. The uncertainty of the models is due to three main reasons: 1. The complexity of the study area in terms of construction history 2. Lack of other necessary data 3. The uncertainties caused by the proposed methodology.