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Structural engineers have to address the climate change challenge by designing sustainable and resilient structures. At this juncture, Electric Arc Furnace Slags (EAFS), a steel-industry waste, are used in replacement of natural aggregates to enhance concrete properties. Moreover, steel and synthetic fibers are added to improve the postcracking behavior while the traditional bar reinforcement enhances the tensile performance. This makes EAFS concrete substantially ductile compared to normal concrete, which contributes to a higher structural resiliency, and hence minimizes functionality disruptions. However the use of fiber and bar -reinforced EAFS concrete in structures is still limited due to the uncertainties introduced by EAFS and fibers. This justify the development of advanced modeling techniques (ie. Finite element Analysis, FEA), which can be used to predict the behavior of EAFS concrete structures at the designing stage. This work build up from the extensive work of the coauthors in the testing of EAFS concrete and, more recently, in the developed FEA of fiber-reinforced EAFS concrete. In this paper the modeling of bar reinforcement is added to the FEA to study the behavior of structural elements made with fiber-reinforced EAFS concrete. The presented FEA is validated through full-scale experiments (four-point flexural test), which shows that the presented FEA is appropriate. The presented numerical model enables to study phenomena difficult to study from experiments or in-situ such as the cracking. It is worth noting that the addition of steel fibers reduced the crack mouth opening displacement in 29.3% and the depth of the cracks in 12.7% in the presented EAFS concrete.

Endovascular techniques, such as endoluminal or endosaccular reconstruction, have emerged as the preferred method for treating both ruptured and unruptured intracranial aneurysms, replacing open surgery in most cases. The minimally invasive approach has been shown to result in better surgical outcomes and lower mortality rates. Before the procedure, neuroradiologists rely only on their experience and visual aids from medical imaging techniques to select the appropriate endovascular option, device model and size for each patient. Despite the benefits of endovascular techniques, significant complications can arise during and after the procedures, including intraprocedural aneurysm perforation, delayed rupture, aneurysm regrowth, in-stent restenosis and thromboembolic events. Therefore, predictive virtual replicas of these interventions can serve as a valuable tool to assist neuroradiologists in the decision-making process and optimise treatment success, especially in cases involving complex geometries. Computational modelling can enable the simulation of different treatment strategies considering the most clinically relevant short- and long-term outcomes of the deployment and the postoperative complications that may arise over time.Statement of significance: This review explores the state of the art in modelling the mechanics of the main neurovascular devices, their deployment within patient-specific geometries, their interaction with the vessel wall and their influence on the local hemodynamics. As it strongly affects their applicability in clinical practice, particular attention is paid to the computational accuracy and efficiency of the different modelling strategies. The aim is to evaluate how these scientific tools and discoveries can support practitioners in making informed decisions and highlight the challenges that require further study.

Endoluminal reconstruction using flow diverters represents a novel paradigm for the minimally invasive treatment of intracranial aneurysms. The configuration assumed by these very dense braided stents once deployed within the parent vessel is not easily predictable and medical volumetric images alone may be insufficient to plan the treatment satisfactorily. Therefore, here we propose a fast and accurate machine learning and reduced order modelling framework, based on finite element simulations, to assist practitioners in the planning and interventional stages. It consists of a first classification step to determine a priori whether a simulation will be successful (good conformity between stent and vessel) or not from a clinical perspective, followed by a regression step that provides an approximated solution of the deployed stent configuration. The latter is achieved using a non-intrusive reduced order modelling scheme that combines the proper orthogonal decomposition algorithm and Gaussian process regression. The workflow was validated on an idealized intracranial artery with a saccular aneurysm and the effect of six geometrical and surgical parameters on the outcome of stent deployment was studied. We trained six machine learning models on a dataset of varying size and obtained classifiers with up to 95% accuracy in predicting the deployment outcome. The support vector machine model outperformed the others when considering a small dataset of 50 training cases, with an accuracy of 93% and a specificity of 97%. On the other hand, real-time predictions of the stent deployed configuration were achieved with an average validation error between predicted and high-fidelity results never greater than the spatial resolution of 3D rotational angiography, the imaging technique with the best spatial resolution (0.15 mm). Such accurate predictions can be reached even with a small database of 47 simulations: by increasing the training simulations to 147, the average prediction error is reduced to 0.07 mm. These results are promising as they demonstrate the ability of these techniques to achieve simulations within a few milliseconds while retaining the mechanical realism and predictability of the stent deployed configuration.

Abstract We implement a nonlinear rotation-free shell formulation capable of handling large deformations for applications in vascular biomechanics. The formulation employs a previously reported shell element that calculates both the membrane and bending behavior via displacement degrees of freedom for a triangular element. The thickness stretch is statically condensed to enforce vessel wall incompressibility via a plane stress condition. Consequently, the formulation allows incorporation of appropriate 3D constitutive material models. We also incorporate external tissue support conditions to model the effect of surrounding tissue. We present theoretical and variational details of the formulation and verify our implementation against axisymmetric results and literature data. We also adapt a previously reported prestress methodology to identify the unloaded configuration corresponding to the medically imaged in vivo vessel geometry. We verify the prestress methodology in an idealized bifurcation model and demonstrate the significance of including prestress. Lastly, we demonstrate the robustness of our formulation via its application to mouse-specific models of arterial mechanics using an experimentally informed four-fiber constitutive model.

In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization.