Gas extraction in the north-eastern Groningen province of the Netherlands has resulted in an increase in induced earthquakes, causing significant damage to homes and affecting residents’ mental health. While reinforcement measures and compensations are being implemented, progress has been slow. The structural analysis for reinforcing these buildings follows various methodologies in accordance with Dutch standard NPR 9998, but these methods are time-consuming and repetitive. This study investigates the use of machine learning techniques to optimize and accelerate the structural analysis of unreinforced masonry (URM) residential buildings. The traditional seismic analysis of URM buildings is computationally expensive, requiring extensive time for model development and assessment. To address this, a classification model and a surrogate model were developed using Deep Neural Network. The data required for both models are obtained from the existing database of Royal HaskoningDHV. Both models provided results indicating whether the building requires reinforcement measures or not. The classification model employs binary classification, using specific building features to categorize structures into those requiring reinforcement
and those that do not. The model generates rapid, implementable outcomes, enabling efficient categorization of buildings. This swift assessment allows for prompt decision-making, particularly in identifying and prioritizing high-risk structures that need more detailed analysis. Surrogate models are efficient approximations of complex analysis, capable of predicting reliable structural responses. Here surrogate for SLaMA analysis is built. The SLaMA surrogate will serve as a proof of concept for developing surrogates of more complex and computationally demanding structural analyses. The results show that, for the classification model, despite sufficient data points, the correlation between input features and the target variable remains weak, primarily due to changes in NPR regulations over the years. This did not allow for a consistent assessment procedure; hence, the same building might be compliant with a recent NPR and not with the older NPRs. For the surrogate model, two different input combinations were experimented with, the analysis provides insights into the impact of input parameters, the need for dimensionality reduction, and the number of data points required for computationally expensive models. The input combinations helped to analyze the curse of dimensionality. The results showed that as more parameters were added, the model required significantly more data to maintain performance. Additionally, the relevance of the selected
reduced input features was observed, to see how well it captures the key aspects of the original analysis with less computational cost. It was observed that the selected features need to remain closely aligned with the original analysis to produce comparable results. In conclusion, while machine learning approaches offer significant potential for improving the efficiency of seismic analysis, further refinement and validation of the models are necessary to address their current limitations and enhance their applicability in real-world scenarios.

Military vehicles are often subjected to dynamic loads from mines. To protect these vehicles, steel plating underneath the vehicle is applied. However, these steel plates can be quite heavy, resulting in a slower vehicle. Composite laminates, however, are much lighter and also prove to be capable of protecting these vehicles from mines.
During an explosion, the energy released from the blast will be absorbed by the composite material. This often results in the delamination of plies within the laminate. Due to the delamination, bending loads will be taken over by membrane loads. This is proven advantageous for composite materials as they are stronger in membrane loading.
Unfortunately, modelling sizeable composite structures with a Direct Numerical Simulation (DNS) requires the use of a lot of elements. This, in turn, results in long computational times, particularly for non-linear analyses. Multiscale modelling is a possible solution to this problem.
This study explores the method of Computational Homogenisation for delamination in composite laminates as an alternative to 3D DNS modelling. Two-dimensional Shell-Interface-Shell elements (SIFS elements) are introduced on the macroscale. These double-layered shell elements consist of two stacked Mindlin-Reissner shell elements with an interface element connecting the two shells. Each integration point of a SIFS element is linked to a mesoscopic 3D coupled Representative Volume Element (cRVE), which is also split into two shells with an interface in between. By applying linear and periodic boundary conditions that incorporate the macroscopic strains on the cRVE, mesoscopic stresses are determined, leading to macroscopic stresses and the macroscopic stiffness matrix.
The proposed multiscale framework is validated by a set of load cases with different ply configurations. The results are then compared to those of a 3D DNS. The multiscale framework performs reasonably well; however, it is not without its limitations. Firstly, the cRVE exhibits width dependence, requiring the implementation of a sufficiently narrow cRVE for accurate results. Additionally, SIFS elements may lack kinematic consistency with 3D solid elements, constraining certain deformations and resulting in overly stiff responses for SIFS analyses. The proposed multiscale framework might not perform as accurately as the 3D DNS in specific load cases, one of which is explored in this work.
Returning to the original goal of this work for the multiscale model, certain extensions still need to be implemented to design composite laminates for blast protection. Implementation of the arc-length method will provide insight into snapback behaviour that could occur during loading. Next, the macroscale and mesoscale models need to be adapted for multiple delaminations over the height of a laminate. Furthermore, the implementation of dynamic loading is a necessary step, as blast loads induce strong dynamic behaviour. Finally, the integration of Artificial Intelligence / Machine Learning into the framework could improve the model by further reducing computational time.