In 2005, a pipeline construction was undertaken under the river IJ in Amsterdam. The microtunneling method was used and it consisted of a closed front TBM of 1800mm diameter over a length of 785m in Pleistocene Sand and extremely soft Holocene sediments and anthropogenic sediments. This construction was accompanied by instrumentation that registered the drilling process with force measurements in the main jacks and intermediate jacking stations, strain in the concrete, joint width, tilt of the element and displacement measurements.
All this data was analysed with the focus on the friction development over the entire boring length. The first part of data analysis was to plot and describe the findings of the available parameters, along the total route, that could have an influence on the friction. Next, the microtunnel route was divided into six sections based on changes in soil conditions and in alignment so the analysed parameters (horizontal and vertical deviations, tilt, main jacking force , front force and friction) could be correlated. In order to better understand the results, a Pearson’s correlation analysis was created to identify any statistically relevant correlation between the available parameters. The final analysis was performed to estimate the impact that subsequent pipe segment installations have on the friction over time at a specific location.
The friction development over the entire length at the boring under the river IJ (less than 2kPa, with the exception of the start) was compared with the friction coefficient value described in the NEN 3650 when overcut and lubrication are used for concrete pipes (f =7.5 kPa). The friction coefficient is overestimated during design phase and can be optimized. Also for all six sections, after a standstill, an increase in friction is observed. At locations where correlation between alignment and forces are apparently present, the horizontal deviation is observed as the influencing parameter. This is also confirmed by the Pearson’s correlation analysis results. Regarding the impact that subsequent pipe segment installations have on the friction, the results of this analysis clearly shows a tendency for a decrease in friction when considering soil type and changes in alignment.
Overall, this work indicates the need of a more thorough friction prediction calculation to be included in the design standards. One that includes more influencing parameters other than overcut and lubricant. Such understanding would enable more accurate predictions in future projects, reducing both risks and costs.

This thesis aims to find the best way to construct a surrogate model for the inner slope stability of a dike and combine this surrogate model with other (machine learning) models to generate conceptual flood defenses, making it possible to optimize the dike design using an interdisciplinary MCA.

The Random Finite Element Method (RFEM) is a robust stochastic method for slope reliability analysis that incorporates the spatial variability of soil properties. However, the extensive computational time associated with the direct Monte Carlo simulation limits its practical application. To overcome this problem, this study investigates the use of machine learning (ML) models as surrogate models for the RFEM in both 2D and 3D contexts. It investigates the performance of three ML models in predicting slope stability by means of the factor of safety (FoS) based on a generated random field of the undrained shear strength. Additionally, a data augmentation technique is employed to improve performance. The models' performance is assessed for various slope cases, characterised by varying spatial variability.
Two surrogate modeling approaches are employed: semi-surrogate modeling and full-surrogate modeling. In the semi-surrogate modeling approach, a small number of RFEM simulations are conducted for a specified case. The machine learning models are trained using the generated random fields as input data and the calculated factors of safety as output data. The mathematical models are then used to predict outcomes of FoS for a large number of random fields for the same specific slope case. In the full-surrogate modeling approach, many RFEM simulations are conducted for the training set, covering a range of spatial correlation lengths. Once trained, the full-surrogate models are ready for application to another different slope case without the need for any additional numerical simulation.
The results indicate that the prediction accuracy of the ML models typically decreases for slope cases with smaller scales of fluctuation. Nonetheless, the FoS predictions by the best-performing semi-surrogate model are highly consistent with the results from RFEM simulations for the whole range of considered slope cases. In terms of predicting the probability of failure for 2D-modeled slopes, the accuracy is high, with relative errors within 10% across the cases considered. This level of accuracy is achieved using no more than 13% of the total number of realisations needed for RFEM analysis. Consequently, the computational time for reliability analysis involving 4000 realisations reduces from 67 hours using the RFEM to between 4 and 8 hours using a semi-surrogate model, with the time increasing as the spatial correlation length decreases. Predicting the p_f for 3D slopes using a semi-surrogate model showed larger errors, indicating a need for improvement.
The full-surrogate models prove to be accurate for testing cases characterised by spatial correlation lengths within the training set's range. Notably, the best-performing full-surrogate model in 3D predicted the p_f within a relative error of 10% for two slope cases. This model performs a stochastic analysis of 4000 simulations within seconds, compared to 83 days of computational time required for RFEM reliability analysis.

Due to quick population growth and urbanisation in Kumasi, Ghana, groundwater depletion is accelerating, and land cover changes reduce the rate of natural infiltration. A promising measure to combat rapid aquifer depletion is implementing Managed Aquifer Recharge (MAR), by rooftop rainwater harvesting and pumping this into wells. The objective of this paper is to delineate the (qualitative) impact of precipitation through Managed Aquifer Recharge on the groundwater level, by analyzing groundwater level changes of sites with and without MAR around Kumasi. To achieve this, multiple groundwater level and flow models have been constructed over different time periods with varying temporal resolutions to show the short- and long-term effect of precipitation on the groundwater level on sites with and without MAR. A rapid increase of groundwater level is observed during rain events, followed by a decelerating curve of infiltration towards areas with lower elevations. This dissipation is much faster in areas with high hydraulic conductivity (hours) than with low hydraulic conductivity (weeks). The groundwater level is recharged by MAR less in the dry season than in the wet seasons. MAR has a highly positive influence on the groundwater recharge. It will be most crucial to implement MAR in high elevations, where the overburden has low hydraulic conductivity, as natural recharge is limited here. The lack of soil and hydraulic head data limited the reliability of the models. Therefore, it is recommended to extend the database in these and additional research areas, aiming to differentiate the effect of MAR and the natural infiltration on the hydraulic head level.

Multiple studies have shown the potential for CO2 plume geothermal (CPG) to be a sustainable, reliable energy source that can be utilized in numerous regions worldwide. Compared to conventional
brine-based systems, a significant benefit is that CO2 allows for direct electricity generation at lower temperatures than brine. It could serve as both a continuous source of energy generation and a dispatchable source when energy demand is high. In addition, it could serve as a pre-carbon capture and sequestration (CCS) phase. Where it could verify the integrity of the reservoir and acquire information to characterize the reservoir and understand its behaviour under CO2 injection. Before a proof-ofconcept site can be chosen, candidate fields should be evaluated to find the optimal environment for CPG. In this work, we investigate which systems, aquifer or gasfield, injection-production scheme and what kind of environment provide the best performance for CPG. We use the Open Delft Advanced Research Terra Simulator (Open-DARTS) to simulate this on a reservoir scale. Open-DARTS uses the Operator-Based Linearization (OBL) approach to model all non-linear physics involved. To get an estimate of the electricity and heat generated by the system, we extend the open-DARTs framework to include a simple wellbore model and surface infrastructure. In our results, we look at the performance of two types of reservoirs: aquifers and gas fields. Where we consider the amount of electricity generated energy and other performance metrics. We find major differences between CPG performance in aquifers and gas fields. The results show that maintaining steady electrical energy generation through CO2 production in an aquifer appears to be much easier than it is for gas fields. With Aquifers consistently having a higher water cut. Furthermore, the inclusion of a plume establishment (PE) phase does boost performance once the CPG stage starts for both the aquifer and gas field types.

Site characterization is indispensable in the design phase of geotechnical engineering projects. As a key factor in site characterization, the characterization of soil undrained shear strength (S_u) is always in the spotlight. Various methods, including laboratory and in-situ tests, have been developed to measure S_u. Nevertheless, these measurements are usually sparse at a specific site due to limited time and budget. To enhance S_u characterization, other relevant geotechnical investigation data (e.g., cone penetration test data), can be transformed into S_u through empirical correlations (referred to as transformation models) to provide more information on S_u. Considering this process introduces the transformation uncertainty and a developed transformation model may not be fully applicable to a local site, probabilistic transformation models (PTMs) have been developed to characterize soil parameters in a site-specific way and quantify the uncertainty to augment engineers’ judgement. However, few PTMs incorporate the spatial correlation of soil parameters, especially in the horizontal direction. This limitation hampers the ability to probabilistically characterize S_u in 2D/3D space, which is significant in practice. Moreover, estimating the horizontal spatial correlation from pure geotechnical data is challenging because they are typically sparse. In light of these circumstances, this thesis first proposes a PTM-based scheme to probabilistically characterize S_u in 2D. Then it is proposed to integrate geophysical data into the scheme. Compared to typical geotechnical investigations, geophysical surveys provide abundant 2D/3D measurement data, which are often correlated with geotechnical data. The fusion of these two data sources benefits characterizing geotechnical data including S_u. Particularly the horizontal spatial correlation of S_u 2D domain can be estimated from the abundant geophysical data. To be specific, a well-established PTM, MUSIC-X, by which measured S_u and other relevant soil parameters can be used to preliminarily characterize S_u, is first adopted. In this case, characterization specifically refers to simulating 1D vertical profiles of S_u. It is then combined with the intrinsic collocated co-kriging (ICCK) model, by which primary data (i.e., S_u) in 2D or theoretically 3D space can be estimated through linearly combining the preliminarily characterized S_u from MUSIC-X modelling and observed secondary data (i.e., geophysical data). The secondary parameter considered in this study is interval velocity (V_int). The scheme, to combine the MUSIC-X and ICCK model to estimate S_u in 2D space by the fusion of geotechnical and geophysical data, is applied to a real case study at Hollandse Kust (west) wind farm zone to demonstrate its effectiveness. The results indicate that such a scheme can robustly estimate a 2D cross section of S_u with quantified uncertainty. A comparative analysis is conducted between the proposed scheme and two alternatives, one lacking preliminary S_u characterization (i.e., without MUSIC-X modelling) and one lacking geophysical data, confirming that the proposed scheme has a relatively high accuracy in the estimated cross section. The research reveals it is sensible to combine MUSIC-X and ICCK for 2D S_u characterization and brings a new perspective that integrating geotechnical and geophysical data is promising to characterize soil parameters in higher dimensional space.

Variable density groundwater models are essential for managing coastal groundwater resources. However, their practical applicability can be questioned due to limited validation opportunities on long timescales associated with the development of fresh-saline distributions. This study addresses this challenge by applying upscaled metamodeling techniques to a state-of-the-art variable density groundwater model (the original model) for the Meijendel-Berkheijde drinking water reservoir. Model validation is performed using a Hydrochemical Facies Analysis (HyFA) conducted by Stuyfzand (1993) in the same area. The primary objective of this study is to enhance validation techniques for variable density groundwater models by incorporating the HyFA. Unlike traditional snapshot-based validation, the HyFA enables validation of groundwater pathways calculated by the model. The applied metamodeling approach significantly reduces calculation times by implementing an upscaled horizontal grid size, parameter rescaling, and linear boundary conditions, thereby enhancing computational efficiency. Although the original model lacks long-term salinity validation, it has not been invalidated based on the similarity of metamodel outputs to the HyFA. However, in the northern part of the study area, a potentially excessive conductance term may result in higher infiltration rates. Incorporating the HyFA into the metamodel is straightforward by adding a "species" dimension in the SEAWAT structure. This validation technique proves valuable for assessing variable density groundwater models on shorter timescales, particularly in areas affected by extensive human interventions. This study contributes to collaborative efforts by Dunea, Deltares, and Arcadis (2021), aimed at advancing efficient modeling for the coastal groundwater reserve of Meijendel-Berkheijde. Transparent documentation of detailed model scripts ensures reproducibility and provides a valuable resource for future research. The insights gained from this study have implications for global advancements in coastal groundwater management. Keywords: Hydrochemical Facies Analysis, variable density groundwater modeling, upscaled metamodeling, coastal groundwater management.

The need of shear strength measurements of soil in the design phase of geotechnical engineering is almost indispensable. Many methods have been applied to estimate the shear strength of soil, including various laboratory test, in-situ test and analytical methods. As an in-situ test method, cone penetration test (CPT) is a powerful and cost-effective tool for the investigation of subsoil conditions. CPT data is usually complemented by the laboratory test data for verification. The laboratory-based studies of subsoil, however, can be not only a complex but also tedious and expensive task for large projects involving large amount of data. Therefore, new approaches for estimating the soil shear strength are demanded. Having demonstrated superior predictive ability for many material properties compared to traditional methods, machine learning methods have been increasingly popular and widely used. This thesis focus on the prediction of soil undrained shear strength through cone penetration test data. The major objectives of this master thesis include testing how machine learning could help us lower the need for laboratory test data. At first, the research starts with a literature review of various methods used to evaluate the soil shear strength. Comparing to the machine learning methods, the laboratory and in-situ test methods are relatively more time-consuming, costly and labour-intensive. And the analytical methods are considered lacking in precision. Then the training dataset which consists of 526 samples is introduced. In each sample, there are four input variables obtained from cone penetration test, namely the effective stress (σ′v ), cone tip resistance (qt − σv), effective cone tip resistance (qt − u2) and the excess pore pressure (u2 − u0). The undrained shear strength obtained from laboratory test is taken as the output variable. Next, the training dataset is fed to five machine learning techniques, namely the artificial neural network, support vector machine, Gaussian process regression, random forest and XGBoost, to train models. The hyperparameters are tuned with k-fold and group k-fold cross-validation strategies in the validation process. After that, the testing dataset which consists of 20 samples is established. Cone penetration test data that are in close vicinity to the location of the samples are processed by Gaussian process regression to obtain representative cone penetration test data at the sample location, which is taken as the inputs in the testing dataset. The undrained shear strengths of the samples are measured by Consolidated-Undrained shear test and are taken as the outputs of the testing dataset. Finally, the five machine learning models are tested on the testing dataset. The crossvalidation results, together with the prediction results of the models on the training and testing dataset are evaluated, gathered and compared by various statistic metrics to show the relative performance of the models. XGBoost appears to be the most accurate of all the tested algorithms on this dataset. And Gaussian process regression is chosen as the second option due to its ability to capture uncertainties. The robustness of these two models are then validated from a statistical point of view by applying Monte Carlo analysis. The importance of the input parameters in this study is evaluated by applying random forest for the sensitivity analysis. The results from random forest indicate that the excess pore pressure and the cone tip resistance - total vertical stress are the most influential inputs to the undrained shear strength

Compositional simulation is computationally intensive for high-fidelity models due to thermodynamic equilibrium relations and the coupling of flow, transport and mass transfer. In this report, two methods for accelerated compositional simulation are outlined and demonstrated for a gas vaporization problem. The first method uses a proxy model that reduces the number of components and the second method reduces the number of grid blocks (i.e. upscaling). Both methods are implemented within the operator-based linearization framework of the Delft Advanced Research Terra Simulator.

Lebesgue integration is applied in the loss function of a neural network allowing the neural network to discover the operator space of the reference model in reduced dimensions. Training is carried out for a one-dimensional homogeneous reservoir and minimizes the misfit of the leading and trailing shocks of a compressible pseudo-binary model with respect to observations of the reference model. The operator space of the pseudo-binary model is initially approximated with the method of multiscale reconstruction of physics, a numerical representation of the method of characteristics. Training is carried out in a two-stage transfer learning scheme to increase computational efficiency. In the first stage, neural networks are trained to approximate the analytical reconstruction. In the second stage, a solver is embedded in the loss function of the neural network and the forward solution is used to calculate the Lebesgue integral. The transfer training scheme minimizes the misfit of the leading and trailing shocks for 10 discrete time steps in a one-dimensional homogeneous reservoir. The misfit of the trained model shows a significant improvement in the location of the trailing shock and a modest improvement in the estimation of the leading shock. The trained proxy is applied to the top and bottom 15 layers of the SPE10 model and the estimation of the first and last breakthrough is assessed in conjunction with the error of the phase-state classification. The phase-state classification is significantly improved through time which is also expressed in improvements of the estimation of breakthrough times. The average difference in breakthrough time for the trained and untrained models with respect to the reference model is 293days versus 570days for the trailing shocks and 15days versus 16days for the leading shock. The established training framework enables the development of proxies with increased complexity.

Rigorous upscaling defines the upscaled operator space with dynamic and non-equilibrium thermodynamic upscaling functions. These functions combined, define the upscaled operator space for the three-dimensional compositional space and are inferred from data points gathered from a limited, characteristic portion of the full-size model. Gathered data points are interpreted with an interpolation function or neural networks to construct structured OBL meshes for implementation within DARTS. This upscaled operator space can effectively be used for different boundary conditions without reevaluating the upscaling functions.