Carsten Könke

Waste management and the second-life utilization of components have attracted significant interest over the past few decades. This study examines the mechanical behavior of concrete modified with recycled wind turbine blade (RWTB) powder used as a partial supplementary material. COMSOL Multiphysics was employed to simulate the response of the modified concrete under loading, using a linear elastic material model. The model investigates the initial mechanical performance and stress distribution. Key mechanical properties have been compared with available experimental literature; however, different powder properties and preparation methods are considered. The results indicate that incorporating small percentages of RWTB powder can improve certain mechanical characteristics, such as stress distribution efficiency, stiffness, and elastic strain energy capacity. The mechanical behavior as an indicative response has been presented. This trend confirms the existence of an optimal powder content that enhances structural performance. In addition, the work underscores the environmental value of integrating RWTB powder into concrete, supporting waste reduction and circular economy practices. The non-linear relationship between the related parameters has been confirmed.

To reduce the significant environmental impact of the construction sector, developing renewable, load-bearing building materials is crucial. Straw, an abundant agricultural by-product, presents a promising resource for this purpose. This study focuses on a novel building material made from highly compressed straw, referred to as Straw-Brick, aiming to provide a reliable numerical model for predicting its complex mechanical behavior. To characterize the material, a series of uniaxial single-cycle and cyclic compression tests were conducted. Based on the experimental results, a one-dimensional, nonlinear rheological model was developed to capture the material’s key mechanical features. The model parameters were identified by fitting the simulation to the experimental data. The results show that the material behavior is characterized by strong nonlinearity, hysteresis, and stress relaxation. The proposed model is capable of capturing these main characteristics, showing good agreement with the experimental data. This work provides a validated material model that establishes a robust foundation for future numerical simulations of structural components made from this innovative and sustainable building material.

The influence of specimen thickness under biaxial loading represents an important aspect in fracture and fatigue analysis. Since earlier studies have not incorporated thickness effects within two‐dimensional numerical simulations, the present work employs the finite element (FE) method to investigate the role of thickness in a cruciform aluminum specimen subjected to biaxial loading. The notch effect is modeled by introducing transverse cracks oriented at 0° and 90° with respect to the x ‐ and y ‐axes at the center of the specimen. Various specimen thicknesses and biaxial stress ratios were examined to evaluate their impact on crack behavior. FE simulations were performed using the Fracture Analysis Code (FRANC2D) to compute the stress intensity factor (SIF) and to simulate crack propagation under different combinations of biaxial stress ratios and thicknesses. The results indicate that, for identical boundary conditions and loading configurations, the crack path remains unchanged for all thicknesses and is governed solely by the biaxial ratio ( λ ). However, the magnitude of the SIF, the crack‐driving force, and the predicted fatigue life vary with thickness. The proposed model was first validated against thickness values reported in previous studies and subsequently extended to additional thickness configurations. The approach proved effective in evaluating the influence of the biaxial stress ratio ( λ ) on crack trajectory and fatigue performance. Furthermore, geometric correction functions were derived as a function of the crack length‐to‐width ratio ( a / w ). It was observed that increasing specimen thickness reduces the SIF at the crack tip due to lower localized stress, resulting in an extended fatigue life.

Within the present paper, Physics-Informed Neural Networks (PINN) are investigated for the analysis of frame structures in two dimensions. The individual structural elements are represented by Euler–Bernoulli beams with additional axial stiffness. The transverse and axial displacements are approximated by individual neural networks and the differential equations are considered by minimizing a joined global loss function within the simultaneous training process. The boundary conditions at the supports of the structure and the coupling conditions at the element connections are considered in the global loss function and specific weighting factors are defined and tuned within the training. The combination of several structural elements within one analysis by training a set of neural networks simultaneously by a joined loss function is the main novelty of the current study. The formulation of coupling conditions for different scenarios is illustrated. Additionally, a nondimensionalization approach is introduced in order to achieve an automatic scaling of the individual loss function terms. Several examples have been investigated as follows: a simple beam structure first with quadratic load and second with varying cross-section properties is analyzed with respect to the convergency of the networks accuracy compared to the analytical solutions. Two more sophisticated examples with several elements connected at rigid corners were investigated, where the fulfillment of the consistency of the displacements and the equilibrium conditions of the internal forces is a crucial condition within the loss function of the network training. The results of the PINN framework are verified successfully with traditional finite element solutions for the presented examples. Nevertheless, the weighting of the individual loss function terms is the crucial point in the presented approach, which will be discussed in the paper.

Within the present paper, a framework for Physics Informed Neural Networks (PINN) is formulated for the analysis of frame structures in two dimensions. The individual structural elements are represented by Euler-Bernoulli beams with additional axial stiffness. The transverse and axial displacements are approximated by individual neural networks and the differential equations are considered by minimizing a global loss function within the simultaneous training process. As a novelty, the boundary conditions at the supports of the structure and the coupling conditions at the element connections are considered in a joined loss function and specific weighting factors are defined and tuned within the training. Several examples have been investigated: first a simple beam structure with varying cross section properties and second with a concentrated discrete load in order to investigated the applicability of the PINNs for structural analysis. Two more sophisticated examples with several elements connected at rigid corners were analyzed, where the fulfillment of the consistency of the displacements and the equilibrium conditions of the internal forces is a crucial condition within the loss function of the network training. The results of the PINN framework are verified successfully with traditional finite element solutions for the presented examples. Nevertheless, the weighting of the individual loss function terms is the crucial point in the presented approach, which will be discussed in the paper.

The presence of mixed-mode stresses, combining both opening and shearing components, complicates fatigue life estimation when applying the Paris law. To address this, the crack path, along with Mode-I (opening) and Mode-II (shear) components, was numerically analyzed using Fracture Analysis Code (Franc2D) based on the linear elastic fracture mechanics (LEFM) approach. Accordingly, fatigue life and stress intensity factors (SIFs) under various biaxial loading ratios (λ) were calculated using the Paris law and compared with available data in the literature. The results show that crack growth is primarily driven by the Mode-I component, which exhibits the largest magnitude. Thus, the Mode-I stress intensity factor (K_I) was adopted for the numerical integration of the fatigue life equation. Furthermore, the influence of normal and transverse loads (σ_y and σ_x, respectively) on the crack path plane and SIF was examined for λ. The analysis revealed that lower λ values led to faster crack propagation, while higher λ values resulted in extended fatigue life due to an increased number of cycles to failure. The comparison demonstrated good agreement with reference data, confirming the reliability of the proposed modeling approach over a wide range of biaxial loading conditions.

The presence of mixed-mode stresses, combining both opening and shearing components, complicates fatigue life estimation when applying the Paris law. To address this, the crack path, along with mode I (opening) and mode II (shear) components, was numerically analyzed using Franc2D based on the linear elastic fracture mechanics (LEFM) approach. Accordingly, fatigue life and stress intensity factors (SIFs) under various biaxial loading ratios (λ) were calculated using the Paris law and compared with available data in the literature. The results show that crack growth is primarily driven by the mode I component, which exhibited the largest magnitude. Thus, KI was adopted for the numerical integration of the fatigue life equation. Furthermore, the influence of normal and transverse loads (σx and σy, respectively) on the crack path and SIF was examined for λ. The analysis revealed that lower λ values lead to faster crack propagation, while higher λ values result in extended fatigue life due to an increased number of cycles to failure. The comparison demonstrates good agreement with reference data, confirming the reliability of the proposed modeling approach over a wide range of biaxial loading conditions.

The German Research Foundation has established the priority program SPP 100+. Its subject is monitoring bridge structures in civil engineering. The data‐driven methods cluster deals with the use of measurements and their special global and local analysis methods, which complement each other in an overall multi‐scale concept in order to realize condition monitoring. The presented methods aim for damage detection, localization, and quantification of the monitored structure. Static and dynamic investigations based on mechanical multi‐scale models were carried out, and process‐oriented models combined with image processing methods and machine learning were created. The methods are tested on several laboratory and real‐life experimental mechanical structures. The underlying theoretical concept and first experimental results of the research group are presented in this article. This study successfully employs a series of multi‐scale experiments, integrating mechanical models and advanced image processing to effectively detect, localize, and quantify damage in bridge structures for enhanced structural health monitoring.