Geotechnical engineering faces challenges in subsurface characterization due to reliance on sparse site investigations and empirical correlations, which limit the accuracy of soil property estimations. This study addresses this issue by leveraging machine learning (ML) methods to predict cone penetration test (CPT) measurements using data from driven cast in-situ (DCIS) pile installations. A comprehensive dataset, including cone resistance, sleeve friction, and friction ratio measurements, was used to train and evaluate ML models—Random Forest (RF), eXtreme Gradient Boosting (XGB), and TabNet. Among these, XGB trained at a 30 cm depth resolution demonstrated a strong balance of predictive accuracy, efficiency, and granularity, achieving R2 values exceeding 80% for cone resistance predictions even with limited data availability. By improving predictions at unseen locations, the study showcases the feasibility of integrating ML models into geotechnical workflows to enhance real-time decision-making, optimize construction practices, and support the development of digital twins for pile-supported structures.
Interpretability methods, specifically SHAP values, provided insights into feature significance and highlighted the critical roles of depth, spatial coordinates, machine-related features and representative sampling strategies in achieving reliable predictions. These findings demonstrate ML’s potential to align predictive capabilities with engineering principles, bolstering confidence in its adoption for practical applications.

The Netherlands faces an urgent need to reinforce its flood defences against rising sea levels. This study investigates the potential of using locally sourced Dutch clays as a sustainable alternative to imported materials for dike reinforcement. A comprehensive experimental campaign was conducted on three types of laboratory-compacted Dutch clays, with clay content varying from 42% to 23% and differing levels of erodibility. Soil characterisation, microstructural analysis (ESEM), and mechanical testing through oedometer and triaxial tests assessed the impact of composition on engineering properties. Results showed that compression behaviour is primarily influenced by the clay content, while shear response, exhibiting both contractive and dilative tendencies, remained consistent across the different compositions. Triaxial tests revealed that samples acted as representative volume elements (RVEs) only up to a certain strain level, beyond which localised deformations occurred due to end-restraint. Additionally, mixing local soils with erosion-resistant materials proved effective in improving the erodibility of unsuitable soils, thereby reducing the quantity of imported material needed. Finally, a new advanced model, JMC-clay bounding surface, was calibrated and validated across the range of soil compositions. The model accurately captured pre-failure and failure behaviour, demonstrating its applicability for predicting geotechnical structure performance and stability.

Despite the growing popularity of diaphragm walls, there is still a lack of understanding regarding soil-concrete interaction and nonlinear behavior analysis for combining the soil and concrete part, which hinders the optimization of reinforced concrete design. This study aims to fill that gap by refining reinforced concrete design and ensuring structural integrity during construction without compromise the safety philosophy. The primary objectives are to enhance the efficiency, reliability, and sustainability of diaphragm walls through a thorough analysis of soil-concrete interactions and nonlinear behaviors.

In the years to come, the Netherlands will face a substantial challenge as over 1,500 kilometers of aging quay walls and sheet pile walls approach the end of their technical lifespan. Infrastructure managers anticipate that the necessary replacements will necessitate investments amounting to billions of euros. Moreover, this task carries a significant environmental footprint, notably in terms of CO2 emissions. The construction work required for these replacements will also result in disruptions and reduced accessibility, inconveniencing users.
This study addresses two pivotal aspects. Firstly, it focuses on enhancing the design aspects of new structures and optimizing costs, with a specific focus exploring how these enhancements can ease the financial challenges faced by infrastructure managers. Secondly, it investigates the safety of existing structures and explores ways to maximize their loadbearing capacity while maintaining safety standards. The expected outcomes of this study promise improved design aspects, cost-efficiency, and enhanced safety measures.
Quay walls can fail due to various mechanisms. This research investigates three primary causes: yielding of soil, yielding of quay wall and anchor yielding. Quay walls illustrate the complexities of soil-structure interaction. To address this, models were developed in both Plaxis and D-Sheet Piling. D-Sheet Piling was the preferred choice due to its computational speed. The reliability analysis was conducted with Probabilistic Toolkit. Considering the calculation methods, First Order Reliability Method (FORM) was employed, emphasizing in efficient computational results in contrast to the Monte-Carlo approach.
In the first aspect, the partial factors were recalculated and compared them with the existing EC partial factor approach. To optimize the current design methodology, the retaining height of the structure was adjusted based on its reliability index. Additionally, the maximum anchor force required was re-evaluated for the structure. This procedure has been conducted for two scenarios, considering and not considering model uncertainty.
Furthermore, an analysis was conducted to understand how altering the retaining height can lead to reduced steel usage, subsequently impacting costs and CO2 emissions. In the second aspect, it was pursued to enhance the structure’s performance by introducing a factor "n" across four distinct scenarios: 1. Simultaneously increasing all loads. 2. Increasing the surcharge loads on the terrain. 3. Increasing the bollard load. 4. Raising the final excavation level in front of the quay wall. While this study aligns with the extensive body of research in the field of civil engineering, It seeks to offer a new and sustainable approach on understanding quay wall design, focusing specifically on the designers’ viewpoint. Through the exploration of innovative design frameworks and approaches, this research seeks to make a valuable contribution to the long-term sustainability of quay wall structures. It aims to redefine our approach to accessibility and safety in these crucial structures. The comprehensive investigations conducted throughout this study provide an enhanced comprehension of quay wall design, reliability, and the optimization of performance.

The rapid growth of Floating Offshore Wind (FOW) has spurred intensive research across various aspects of the floating system. A standard practice in mooring system design within the industry is that anchors remain fixed on the seabed. In contrast, plate-type anchors exhibit mobility under loading, a crucial factor considering the thousands of loading cycles experienced by FOW turbines during operation. Understanding the strain accumulation mechanism during cyclic loading has substantial implications for design. This thesis delves into the behavior of Drag Embedded Anchors (DEAs) subjected to static monotonic and cyclic loading, utilizing 3-dimensional Finite Element simulations. The installation trajectory of DEAs is initially defined through established analytical methodologies. Subsequently, the movement of the anchor and soil response under monotonic and cyclic loads is elucidated. Analytical expressions for monotonic force-displacement curves are examined, identifying optimal models and offering relevant parameter values for different anchor trajectory points. Under cyclic loading, the influence of average load and cyclic load amplitude is studied, along with exploring the feasibility of applying an existing 1-dimensional model for predicting anchor response.