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.

Given its geographical location and history, water defense is of utmost importance for the Netherlands. Structural Health Monitoring (SHM) offers a promising approach for system identification of hydraulic structures in this water defense system. The aim of SHM is to set sensors on structures and use the monitored responses to identify structural parameters of interest. However, many pertaining questions are unanswered concerning realistic hydraulic structures and monitoring systems: What type of sensors (e.g. strain gauge, SAAF, etc.) can and should be used in the monitoring system? How many sensors are needed, where and when to install these sensors? What is the influence of construction stages of structures on system identification? Considering the evaluation of a structure, what is the influence of system identification as well as construction stages on reliability (failure probability) of structures? Considering practical implementation: which computational algorithm is suitable and feasible? How to construct a proper surrogate model of the mechanical model to reduce computational time?
To answer these questions, a single anchored sheet pile wall is studied using a probabilistic approach. The sheet pile wall is modeled using the finite element (FE) method, synthetic data are used and Bayesian approach is adopted to cope with measurement uncertainty and model uncertainty. The information conveyed by sensors is quantified by the Kullback–Leibler (KL) divergence between prior and posterior distributions. Moreover, the correlation in model uncertainty of various structural responses is quantified by comparing a full-scale experiment from the literature and a corresponding calibrated 3D finite element model.
The results show that:
• A combination of different sensor types (in our case they are SAAF and strain gauge) should be used in the monitoring system (e.g. the combination of four different types of sensors outperforms the strain sensors on the sheet pile wall by conveying 40% more information with respect to the former); 
• Even limited number of sensors can convey sufficient information. In our case, 3 sensors placed at proper locations can convey 90% information carried by 6, 8 and 9 sensors considering different responses. They should be installed as early as possible;
• The failure probability computed using posterior from system identification largely decreases compared with that computed using prior (the ratio of prior and posterior failure probabilities can go up to 15¹⁰ in our case); 
• Delay of the start of monitoring during the construction stages decreases the information conveyed by sensors in system identification (the conveyed information can decrease by 50% in our case) and increases the computed failure probability in reliability analysis: the ratio of prior and posterior failure probabilities can be as large as 30¹⁰ ); 
• MultiNest performs well in Bayesian inference in high dimensional problems; 
• Gaussian process regression (GPR) with anisotropic radial basis function (RBF) kernel and white kernel as well as an adaptive infilling criterion is capable of constructing an accurate surrogate model even when it goes to high dimensionality. The error of surrogate model prediction can be explicitly explained.
To my knowledge the work presented in this thesis is the first application of combined system identification and reliability assessment for hydraulic structures, and the first detailed analysis of the effect of sensor installation time on system identification and structural reliability of hydraulic structures.
The findings imply that probabilistic system identification is a promising approach to substantially reduce our uncertainty in modelling hydraulic structures and in turns to increase their calculated safety. The approach has the potential to extend the working life of aging hydraulics structures and save costly strengthening and replacement. The analysis framework can also be applied to other structures in civil engineering.