This paper focuses on the application of topology optimisation algorithms to improve the crashworthiness of heavy passenger vehicles, in frontal impact conditions. The objective is to find the optimal arrangement of material to minimise compliance, satisfying a volume constraint. Ultimately, it is intended to analyse the influence of this component in the energy absorption capability of the vehicle structure during a crash. Crashworthiness design plays a crucial role in the automotive industry, particularly in enhancing passenger safety. It aims to develop structures that can absorb more energy while minimising intrusion, to maintain the driver’s survival space. If so, crashworthiness design deals with conflicting objectives, and optimisation methods can be used to find a compromise between these parameters. The application of topological optimisation in the context of vehicle structure crashworthiness is still limited, mainly due to the high computational costs associated with crash simulations that deem these approaches impractical. To tackle this difficulty, this study performed the optimisation process on a single component, a crash box, employing optimisation algorithms built within a Matlab code, that iteratively interfaces with Abaqus® where the non-linear crash simulation is performed. Afterwards, the optimised component was incorporated into the coach chassis baseline and tested using an already established finite element model on VPS/PamCrash®, simulating a frontal impact, according to the ECE R29 regulation. The application of this methodology demonstrated that evolutionary algorithms can effectively be applied for topology optimisation under crashworthiness conditions, generating an optimised crash box that improves the crashworthiness metrics of the coach baseline structure.

Experimental protocols are fundamental for quantifying the mechanical behaviour of soft tissue. These data are crucial for advancing the understanding of soft tissue mechanics, developing and calibrating constitutive models, and informing the development of more accurate and predictive computational simulations and artificial intelligence tools. This paper offers a comprehensive review of experimental tests conducted on soft aortic tissues, employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, based on the Scopus, Web of Science, IEEE, Google Scholar and PubMed databases. This study includes a detailed overview of the test method protocols, providing insights into practical methodologies, specimen preparation and full-field measurements. The review also briefly discusses the post-processing methods applied to extract material parameters from experimental data. In particular, the results are analysed and discussed providing representative domains of stress–strain curves for both uniaxial and biaxial tests on human aortic tissue.

This work provides a detailed description of the procedures employed to characterize the mechanical behaviour of two materials present in a coach’s exterior panels, including glass-fibre-reinforced polymer (GFRP) and neat DCPD (dicyclopentadiene)-based polymers. Tensile tests were conducted at quasi-static, intermediate [1 s⁻¹, 10 s⁻¹], and high strain rates [150 s⁻¹, 250 s⁻¹] to obtain a comprehensive understanding of their behaviour. The results indicate positive and significant dependence on the strain rate. Additionally, GFRP demonstrates superior energy absorption capacity for higher strain rates, unlike DCPD, which exhibits a higher energy absorption capacity for QS tests. In the case of DCPD, raising the strain rate to 10 s⁻¹ the maximum stress was not affected but decreased the elongation at fracture. At higher strain rates, there was an increase in maximum stress alongside greater elongation. DCPD maintained consistent stiffness across all rates ranging between 2087 MPa and 2389 MPa, and the tests disclosed a failure mode characterized by numerous surface-transverse fissures. Regarding GFRP, a more pronounced variation in stiffness is observed, decreasing from 11,005 MPa to 4532 MPa at 133 s⁻¹, recovering to 7288 MPa at 252 s⁻¹. In addition, the maximum stress and failure elongation tends to increase with the strain rate increase. The detailed analysis of these results provides valuable insights into the mechanical behaviour of these materials under different loading conditions.

This study focuses on the development of a machine learning (ML) model to elaborate on predictions of structural damage in submerged structures due to ocean states and subsequently compares it to a real-life case of a 6-month experiment with a benthic lander bearing a multitude of sensors. The ML model uses wave parameters such as height, period and direction as input layers, which describe the ocean conditions, and strains in selected points of the lander structure as output layers. To streamline the dataset generation, a simplified approach was adopted, integrating analytical formulations based on Morison equations and numerical simulations through the Finite Element Method (FEM) of the designed lander. Subsequent validation involved Fluid–Structure Interaction (FSI) simulations, using a 2D Computational Fluid Dynamics (CFD)-based numerical wave tank of the entire ocean depth to access velocity profiles, and a restricted 3D CFD model incorporating the lander structure. A case study was conducted to empirically validate the simulated ML model, with the design and deployment of a benthic lander at 30 m depth. The lander was monitored using electrical and optical strain gauges. The strains measured during the testing period will provide empirical validation and may be used for extensive training of a more reliable model.

Degraded mechanical properties in the aortic wall can lead to the formation of aortic aneurysms, potentially resulting in life-threatening ruptures. Current diagnostic criteria using maximum aortic diameter often fail to predict this critical moment, underscoring the need for more accurate patient-based prediction methods. A hospital-compatible experimental apparatus was designed for quasi-static ex vivo inflation testing of intact Ascending Thoracic Aortic Aneurysm (ATAA) specimens with 360° full-field three-dimensional digital image correlation (3D-DIC). Given hospital handling constraints, liquid pressurization was not feasible; instead, pressure was applied via a balloon-driven pneumatic system, and synchronized stereo imaging was used to measure surface displacement fields between 80 and 120 mmHg. The system was validated using a CT-derived ATAA silicone phantom. Full-field displacement measurements showed close agreement with finite element simulations, supporting the mechanical reliability of the apparatus and the repeatability of the measurement workflow. In addition, a frozen–thawed healthy porcine thoracic aorta was tested to demonstrate biological feasibility, particularly regarding the speckle application and DIC tracking, without aiming to extract tissue constitutive parameters. Overall, the setup provides a practical framework for acquiring full-field inflation-induced deformation data from intact aortic specimens in a hospital setting, enabling future studies on resected human ATAA tissue and model calibration that may contribute to more accurate methods for rupture prediction.