Simulation based on computational solid mechanics models describe the response of structures, as a function of their geometry, loading, boundary conditions, material properties and manufacturing process. Digital Twin Validation i.e. 'the process of determining the degree to which a model is an accurate representation of the real world, from the perspective of the intended uses of the model', is of the most important aspects of engineering simulation. It is the responsibility of the digital twin users to perform sufficient validation of the models developed, by reference to experiments specifically designed for this purpose. Optical measurement and other relevant experimental methods have reached a sufficient technology readiness level that enable displacement or strain data over large areas or even the entire structure to be reliably captured during an experimental test and thereafter visualized and analyzed. Such developments have provided the background for a more comprehensive approach to model validation used in engineering design and evaluation of structural integrity, which could lead to optimized and less conservative designs. An important parameter in digital twins is the mechanical performance of the materials and how it is affected by the manufacturing process, especially when recycled materials are used. During the session, important recent advances on simulation model development, validation methodologies and the performance of recycled composites will be presented by researchers from industry and academia, focusing on validation of novel aircraft structural components and structural details.
 

Impact and crashworthiness are two critical aspects of composite structures, approached in contrasting ways. Impacts, whether high-energy or low-energy, can lead to failures in these materials. Therefore, it is essential to understand these phenomena in order to improve design requirements and enhance the overall performance of composite structures. Conversely, crashworthiness refers to the effective use of material failure as a beneficial mechanism, allowing for the dissipation of impact energy to protect occupants or goods within a vehicle.

This session will explore innovative research, methodologies, and applications related to the performance of composite materials and structures in impact and crash scenarios. The session particularly encourages contributions that focus on advancements in energy absorption mechanisms, progressive damage evolution, high-rate material behaviour, and failure mode characterisation. Case studies from the automotive, aerospace, and emerging mobility sectors are welcome, alongside fundamental academic investigations. Additionally, experimental, analytical, and/or numerical studies are welcome
 

This session will focus on advanced non-destructive testing (NDT) and structural health monitoring (SHM) technologies, highlighting their role in transitioning from damage detection to proactive failure prevention. This session is open to all applications of all NDT methods (including but not limited to ultrasonic, acoustic emission, X-ray, thermography, eddy current, etc.) and SHM methods on any structures/components made of different materials, including but not limited to composites, concrete, ceramics, 3D printed materials, cultural heritage items. Presentations on novel applications of NDT/SHM techniques in various fields, such as aerospace, civil engineering, materials characterisation, etc. are expected. Potential topics include, but are not limited to, damage detection, identification, and localization, modelling/simulation, signal processing, and various industrial applications
 

The aim of this session is to explore emerging challenges in sustainability-driven design, with particular emphasis on multi-material solutions and function integration, illustrated through selected contributions. The session also addresses evolving design trends that are reshaping engineering practice.

In particular, topics of interest include, but are not limited to:

• Recent advances in sustainability-driven design methodologies
• Multi-criteria optimization approaches and integrated design methods
• Practical implementation of sustainability as a core design criterion in mechanical components

Materials adopted in civil, mechanical, automotive, naval, aerospace, and industrial engineering applications are rarely subjected to constant amplitude loading. While standard fatigue assessment is often based on simplified loading assumptions, the structural strength and durability under real-world service conditions, specifically variable amplitude and spectrum loading, remain a major concern in the design of engineering structures. In order to fully optimize design and ensure reliability, understanding and estimating the mechanical performance of materials and structures under complex, random, or block loading histories is of paramount importance. Therefore, the special session entitled “State of the art approaches for fatigue analysis of materials and structures subjected to variable amplitude and spectrum loading” will focus on state-of-the-art theoretical, numerical, and experimental approaches to investigate the fatigue behaviour of materials and structures under variable amplitude loading. Special attention will be dedicated to emerging technologies, specifically the application of Machine Learning (ML) algorithms for life prediction and Digital Twin frameworks for real-time structural health monitoring and prognosis.
 

Structural Health Monitoring (SHM) methods and architectures for damage detection and localization in structural components and assemblies (metallic, composite or hybrid). Techniques based on analytical or/and numerical modeling procedures. Statistical pattern recognition and Machine Learning based methods. Uncertainty quantification in the SHM problem accounting for environmental and operational variability. Approaches for generating structural digital twins. 
 

Additive manufacturing (AM) continues to transform the design and production of materials and components, enabling complex geometries, lightweight structures, and bespoke designs that were previously impossible to achieve with traditional manufacturing methods. However, these new possibilities also bring unique challenges in understanding and predicting the fracture and failure behavior of AM materials, components, and the novel structures and metamaterials being developed for advanced applications.

This special session will focus on the fracture and failure mechanisms of additively manufactured materials, components, structures, and metamaterials. It will explore both the scientific foundations and the practical challenges associated with ensuring the structural integrity of AM-produced parts. 

Topics of interest include:
 

• Fracture mechanics of AM materials and structures: Investigating the influence of microstructure, defects, and residual stresses on fracture behavior, with a particular focus on the unique characteristics of AM-produced parts.
• Failure analysis in AM components: Exploring the different failure modes (e.g., brittle, ductile, fatigue) in various AM processes such as powder bed fusion, directed energy deposition, and material extrusion, and their implications for part reliability.
• Fatigue and durability of AM components/structures: Examining the performance of AM components under cyclic loading and long-term use, and developing strategies to improve the durability of additively manufactured parts.
• Designing for failure resistance in AM structures: Best practices for improving the strength and resilience of AM parts, including material selection, process optimization, and post-processing techniques.
• Metamaterials and innovative structures in AM: The role of advanced materials and structures, such as lattice structures and metamaterials, in improving performance and creating lightweight, high-strength components. Understanding how the unique properties of these materials impact fracture and failure behavior.
• Non-destructive testing (NDT) and in-situ monitoring for AM parts: Investigating the latest techniques for detecting and characterizing defects, cracks, and fractures in AM components during production and service life.
• Case studies and real-world applications: Insights from industries such as aerospace, automotive, biomedical, and energy, where AM materials and metamaterials are being integrated into high-performance and safety-critical components.
• Optimisation and improvements based on case failures identification, 
• other related.

The session aims to bring together researchers, engineers, and industry experts to share the latest advancements in the understanding of fracture and failure mechanisms in AM materials and components, with a special emphasis on the challenges and opportunities presented by new structures and metamaterials. Attendees will gain valuable insights into the state-of-the-art techniques for enhancing the reliability, performance, and safety of additively manufactured parts in demanding applications.

We invite contributions that present new experimental data, theoretical models, or innovative design strategies aimed at improving the fracture resistance and performance of AM structures and metamaterials, as well as addressing the growing need for reliable, high-performance AM components in industry.
 

The session will be a focused discussion on life prediction approaches and models for multiaxial fatigue, fatigue under variable amplitude loading, and fatigue of gradient materials. It will cover both the fundamental fatigue mechanisms and engineering prediction approaches for real world applications.
 

Understanding the fatigue behavior and properties of materials and structures is essential for ensuring their safety in engineering applications. Fatigue studies often require complex simulations, which can be resource-intensive and time-consuming. Recently, machine learning (ML) techniques have demonstrated significant potential in advancing fatigue analysis through accurate and efficient predictions. 
This session seeks to present cutting-edge research on applying machine learning approaches to investigate mechanical behaviors and properties related to fatigue. Topics of interest include, but are not limited to the following:

• Development of machine learning models for predicting fatigue life and damage evolution
• Data-driven analysis of mechanical properties under complex loading conditions
• Fracture surface and microstructure analysis utilizing machine learning methods
• Case studies demonstrating successful machine learning applications in fatigue analysis of components and structures 

We invite submissions from academia and industry that highlight innovative strategies and practical applications of machine learning in the field of fatigue research. We look forward to fostering discussions on the latest advancements and future directions in this exciting area. 
 

The prediction of material failure, encompassing fatigue, fracture, and progressive damage, remains a cornerstone of engineering design and structural integrity assessment. While traditional physics-based models have laid a solid foundation, they often struggle with complexity, multi-scale phenomena, and material uncertainties. The emergence of machine learning (ML) offers a powerful paradigm shift.

However, purely data-driven approaches can lack physical consistency and generalizability.
This session aims to spotlight the latest advancements in integrating mechanistic understanding with ML techniques to create robust, predictive models for failure analysis. We encourage submissions that leverage ML not as a black box, but as a tool to discover, enhance, or accelerate physics-based models. 

Topics of interest include, but are not limited to:

• Physics-informed neural networks (PINNs) for predicting crack propagation and fatigue life.
• ML-enhanced constitutive models for damage and plasticity.
• Multi-scale modeling bridged by machine learning.
• Discovery of failure-related governing equations from experimental or simulation data.
• Fusion of heterogeneous data (e.g., from digital image correlation, acoustic emission, microscopy) for damage state diagnosis and prognosis.
• Uncertainty quantification in ML-predicted failure modes.
   

We seek contributions that demonstrate a synergistic coupling between data-driven methods and the underlying physics of material deformation and failure, ultimately leading to more reliable and trustworthy engineering solutions against failure.