Finite element analysis (FEA)-based computational fluid dynamics (CFD) simulations are essential in biomedical engineering for studying haemodynamics, yet their high computational cost limits large-scale parametric studies. This paper presents a comparative analysis of FEA and surrogate modelling techniques applied to renal artery haemodynamics. The aortic–renal bifurcation strongly influences renal perfusion, affecting conditions such as hypertension, infarction, and transplant rejection. This study evaluates GPU-accelerated voxel simulations (Ansys 2024 R2 Discovery), 2D and 3D FEA simulations (COMSOL Multiphysics 6.3), finite volume CFD (Ansys 2020 R2 Fluent), and deep neural networks (DNNs) as surrogate models. Branching angles and blood pressure were systematically varied, and their effects on velocity, pressure, and turbulent kinetic energy were assessed in a time-dependent framework. Fluent provided accurate baseline results, while COMSOL 2D gave sufficient accuracy with much lower runtimes. In contrast, COMSOL 3D required over 160 times longer, making it prohibitive. Surrogate models trained on 6500 or more FEA-derived samples achieved high predictive accuracy (R² > 0.98 for velocity and pressure), balancing training cost and result quality. Cost analysis showed surrogate models become advantageous after 76–93 simulations. Symmetry is expressed in balancing model fidelity and computational efficiency, providing a resource-effective methodology with broad potential in vascular applications.

The aim of this paper is to show that the fuzzy signature model based on non-destructive in-situ measurements and visual inspection can be used to obtain equally reliable results on the strength of masonry as destructive tests. The fuzzy signature model was constructed to describe a masonry based on different measurements of. The aggregations of the signature were given as weighted geometric means. Weights of the input values of the aggregations were determined by two different optimization methods: (i) an iteration procedure was chosen, which showed significant differences in the result; (ii) a genetic algorithm was used, which produced similar but more accurate approximations to the results of the destructive tests. The comparison of the two optimization results gave new conclusions that are valuable information for the further development.

Non-destructive testing methods are essential in civil engineering applications, such as evaluating the compressive strength of masonry. This paper presents a fuzzy signature model based on non-destructive in situ measurements and visual inspection, applying weighted geometric mean aggregation in the signature vertices determined by experts. The weights of the aggregation terms were optimized using the Monte Carlo method, genetic algorithm and particle swarm algorithm to ensure that the evaluation by the signature aligned with the results of destructive tests performed on existing masonry. The results of the methods were compared for single and multiple assembled masonry structures using the same objective function. All three methods provided relatively high confidence in finding the extreme values of the objective function on a generated dataset, which accounted for the correlations observed in actual measurements. Accordingly, validation based on real data yielded the expected results, thus demonstrating the model’s suitability for practical application. This study assessed the inherent, analyzing whether symmetric or asymmetric weight distributions affected evaluation consistency. While symmetric weighting simplified aggregation, asymmetry allowed local structural irregularities to be highlighted. In addition, the cost analysis of the optimization methods revealed a disparity in computational cost increments between the two approaches. The presented work outlines the advantages of the different methods and their applicability to structural assessment.