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Failure assessment diagrams (FADs) are essential engineering tools for evaluating the structural integrity of components. However, their widespread application can be limited by complexity and computational expense. This study presents a novel machine learning-based approach to streamline FAD analysis, offering accuracy and efficiency while overcoming these limitations. The approach integrates numerical contour integral-based FADs with artificial neural networks (ANNs). To ensure reliable material modeling for the Finite Element Analysis (FEA) used to generate J -integral based FADs that train the ANNs, careful experimental and numerical procedures were employed. This involved uniaxial tensile tests, an iterative method for obtaining precise true stress–strain curves, and a Ramberg–Osgood material model for accurate material behavior representation. The ANNs themselves not only analyze large datasets to generate precise FAD envelopes but also predict limit loads and the Φ parameter, incorporating the effect of residual stress on the FAD methodology. To verify and test the proposed method, hypothetical fitness-for-service assessment cases were conducted, incorporating experimental residual stress measurements from split-ring tests on P110 and L80 pipes. These assessments were compared to both traditional FAD methods and computationally intensive FEA-based FADs. Results demonstrate a closer agreement with FEA-based calculations than traditional methods provided in engineering standards. Ultimately, this work provides a rather innovative and adaptable approach for structural integrity evaluations and critical engineering assessments through the proposal of an ANN enhanced FAD approach, simplifying these calculations while maintaining high fidelity. • Developed an ANN-based approach for streamlined Failure Assessment Diagram (FAD) analysis. • Integrated ANNs with numerical FADs, enabling precise envelope generation from extensive datasets. • Conducted experimental residual stress measurements using split-ring test data as an input for hypothetical fitness-for-service (FFS) evaluations. • Demonstrated superior accuracy of the ANN-based FAD methodology for enhanced fitness-for-service (FFS) evaluations. [ABSTRACT FROM AUTHOR]

Hydrogen (H), emerging as a sustainable and promising clean energy source, holds significant potential for transitioning towards a H-based economy, offering a cleaner alternative to traditional fossil fuels. However, hydrogen embrittlement (HE) poses a substantial obstacle to this transition, impacting critical sectors such as transportation, defense, energy production, and construction. Computational modeling, driven by the continuous development of new algorithms and high-performance computing platforms, emerges as an attractive avenue to unravel and address the complexities associated with HE. In particular, a multidisciplinary modeling approach shows potential in investigating the intricate interactions between H and materials across different temporal and spatial scales. Over the last few decades, there have already been many developments in computational modeling investigations based on a coupled study of H diffusion, deformation, and fracture processes to address multifaceted aspects of the HE problem. This comprehensive review sheds light on these advancements, providing insights into the modeling methodologies adopted in these investigations and their results. The review begins with a concise overview of commonly adopted mechanisms to explain HE. Thereafter, the discussion shifts to various advancements in H diffusion modeling, from early works to most recent developments, encompassing diverse aspects, such as H uptake and diffusion through the lattice structure and the role of microstructural traps and material microstructure. The last section of the review focuses on several theoretical and numerical studies that simulate how H affects the fracture characteristics and mechanical properties of various metals and alloys. This discussion includes applications of various state-of-the-art fracture models to predict H-assisted crack growth, as well as a range of theoretical models, continuum-based finite element simulations, and micro-meso scale modeling studies. • Advances in hydrogen diffusion modeling using coupled deformation-diffusion models. • Comprehensive review of coupled deformation-diffusion-fracture models. • Insights into micro-meso scale investigations to study hydrogen embrittlement mechanisms. [ABSTRACT FROM AUTHOR]

[Display omitted] • Pyramidal lattice structures (PLS) with I-shaped struts were 3D printed via DLP. • The utilization of I-shaped struts led to an increase in energy absorption of up to 68%. • Lateral buckling of the lattice struts was observed in the tailored PLS. • The measurements and observed collapse modes were in good agreement with FE predictions. • Specific cross-sectional designs can achieve enhancements in strength of up to 93%. This study examines the compressive response of geometrically tailored pyramidal lattice structures composed of struts with I-shaped cross-sections. Geometrically tailored pyramidal lattices with micro-scale features are 3D printed using the Digital Light Processing (DLP) technique, and their effective elastic modulus, collapse strength and energy absorption capacity are experimentally evaluated under quasi-static compressive loading. Furthermore, detailed non-linear finite element (FE) calculations are performed to examine underlying collapse mechanisms and explore the vast design space offered by the proposed geometrical tailoring scheme. Both the experimental and numerical results show that the geometrically tailored lattice structures outperform conventional pyramidal lattices of equal weight in terms of elastic modulus (+24 %), collapse strength (+21 %) and energy absorption (+68 %). Notably, these strength improvements are attributed to lateral buckling that prompts the I-shaped struts to bend sideways during collapse. Specific cross-sectional designs demonstrate remarkable enhancements in strength and energy absorption, reaching up to 93 % and 161 %, respectively, differentiating them significantly from conventional designs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Porous bone plates with cubic (C), diamond (BCC) and body-centered cubic (CBCC) structures were prepared. • Axial compression, torsional and bending tests were carried out to check the designs' reliability. • CBCC structure with enhanced performance was used with varying rows' and porosities. • Finite element analysis was used to check the biomechanical performance of bone plates. • Increased porosity enhanced the callus volume but displayed poor mechanical performance. The current study investigates the biomechanical performance of porous bone plates augmented with three different cellular lattice structures, e.g., body-centered cube (BCC), simple cube (SC), and the superposition of simple and body-centered cube (SC-BCC) structures. The SC-BCC cellular structures, exhibiting improved torsional, compression, and bending stiffness, were strategically integrated into the bone plates. Configurations ranging from one to three rows, with porosity ranging from 30% to 90%. Increasing the number of rows and porosity maximized the interfragmentary movement at the fracture site. Specifically, SC-BCC configurations with one, two and three rows at 90% porosity demonstrated callus volume improvements of 31.33%, 42%, and 43.2%, respectively, compared with the lowest callus volume observed with SC-BCC at one row and 30% porosity. Regardless of the improved volume, callus stiffness was highest at 30% and 90% porosity levels across all cases, indicating more mature tissue formation in calluses and better physiological load support. High stresses located at 90% porosity, followed by 50% porosity, discouraged their mechanical performance. Therefore, employing 30% porosity configurations with appropriate vertical rows for desired movement is recommended for optimal biomechanical performance. However, 90% porosity may be suitable in scenarios involving minimal forces and restricted patient movement. [ABSTRACT FROM AUTHOR]