A Review on Damage and Rupture Modelling for Soft Tissues

Computational modelling of damage and rupture of non-connective and connective soft tissues due to pathological and supra-physiological mechanisms is vital in the fundamental understanding of failures. Recent advancements in soft tissue damage models play an essential role in developing artificial tissues, medical devices/implants, and surgical intervention practices. The current article reviews the recently developed damage models and rupture models that considered the microstructure of the tissues. Earlier review works presented damage and rupture separately, wherein this work reviews both damage and rupture in soft tissues. Wherein the present article provides a detailed review of various models on the damage evolution and tear in soft tissues focusing on key conceptual ideas, advantages, limitations, and challenges. Some key challenges of damage and rupture models are outlined in the article, which helps extend the present damage and rupture models to various soft tissues.

A Quasi-Brittle damage model in the framework of Bond-based Peridynamics with Adaptive Dynamic Relaxation method

Peridynamics (PD) is a new tool, based on the non-local theory for modelling fracture mechanics, where particles connected through physical interaction used to represent a domain. By using the PD theory, damage or crack in a material domain can be shown in much practical representation. This study compares between Prototype Microelastic Brittle (PMB) damage model and a new Quasi-Brittle (QBR) damage model in the framework of the Bond-based Peridynamics (BBPD) in terms of the damage plot. An in-house code using Matlab was developed for BBPD with inclusion of both damage models, and tested for a quasi-static problem with the implementation of Adaptive Dynamic Relaxation (ADR) method in the theory in order to get a faster steady state solutions. This paper is the first attempt to include ADR method in the framework of BBPD for QBR damage model. This paper analysed a numerical problem with the absence of failure and compared the displacement with literature result that used Finite Element Method (FEM). The obtained numerical results are in good agreement with the result from FEM. The same problem was used with the allowance of the failure to happen for both of the damage models; PMB and QBR, to observe the damage pattern between these two damage models. PMB damage model produced damage value of roughly twice compared to the damage value from QBR damage model. It is found that the QBR damage model with ADR under quasi-static loading significantly improves the prediction of the progressive failure process, and managed to model a more realistic damage model with respect to the PMB damage model.

INSYDE-BE: Adaptation of the INSYDE model to the Walloon Region (Belgium)

The spatial transfer of flood damage models among regions and countries is a challenging but unavoidable approach, for performing flood risk assessments in data and model scarce regions. In these cases, similarities and differences between the contexts of application should be considered to obtain reliable damage estimations and, in some cases, the adaptation of the original model to the new conditions is required. This study exemplifies a replicable procedure for the adaptation to the Belgian context of a multi-variable, synthetic flood damage model for the residential sector originally developed for Italy (INSYDE). The study illustrates necessary amendments in model assumptions, especially regarding input default values for the hazard and building parameters and damage functions describing the modelled damage mechanisms.

GTN Damage Modelling of the AA6063 Using Image Processing

In this paper, an innovative way of calculating the Gurson–Tvergaard–Needleman parameter has been developed for AA 6063. AA 6063 is an aluminum alloy comprising the alloying ingredients magnesium and silicon. The Aluminum Association maintains the standard that governs its composition. It has strong mechanical properties and may be heat treated and welded. Image processing technique has been used to calculate the damage constant for the AA 6063. The image of the sample has been taken under a microscope of undeformed and fractured material. Then the images are analyzed using the Open CV tool in a python open-source environment. The initial and final void fraction of the sheet has been calculated. Damage models, particularly the Gurson–Tvergaard–Needleman (GTN) model, are widely used in numerical simulation of material deformations. Each damage model has some constants which must be identified for each material. The direct identification methods are costly and time-consuming. A combination of experimental, numerical simulation and optimization have been used to determine the constants in the current work. Numerical simulation of the dynamic test was performed utilizing the constants obtained from quasi-static experiments. The results showed a high precision in predicting the specimen's profile in the dynamic testing.

The Influence of Different Lay-Up Parameters on the Fatigue Response of Carbon/Epoxy Laminates under Internal Multiaxial Stress States

The inherent anisotropy of composites complicates their damage response. The influence of multiaxiality, particularly in carbon-based composites, is not thoroughly understood due to obstacles related to damage monitoring during loading. In this study, the response of different carbon/epoxy laminates under fatigue is examined through dedicated in situ microscopic observations. By varying the orientation of off-axis layers, the impact of multiaxiality on the mechanical and damage response is evaluated. Furthermore, balanced and unbalanced laminates are compared, considering the limited information for the latter. The influence of the number of off-axis layers is finally assessed leading to important conclusions about optimal fatigue response. The fatigue response is evaluated in all cases considering both the mechanical properties and the damage characteristics. Significant conclusions are drawn, especially for the benefits of unbalanced laminates and the impact of shear stresses, allowing for the utilization of the obtained data as important input for the establishment of reliable fatigue damage models.

A Machine Learning Model for Predicting Progressive Crack Extension based on Direct Current Potential Drop Fatigue Data

Time history data collected from a Direct Current Potential Drop (DCPD) fatigue experiment at a range of temperatures was used to train a Bidirectional Long-Short Term Memory Neural Network (BiLSTM) model. The model was trained on high sampling rate experimental data from crack initiation up through the Paris regime. The BiLSTM model was able to predict the progressive crack extension at intermediate temperatures and stress intensities. The model was able to reproduce crack jumps and overall crack progression. The BiLSTM model demonstrated the potential to be used as a tool for future investigation into fundamental mechanisms such as high-temperature oxidation and new damage models.

Update article: applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events

In a companion article, previously published in Royal Society Open Science , the authors used graph theory to evaluate artificial neural network models for potential social and building variables interactions contributing to building wind damage. The results promisingly highlighted the importance of social variables in modelling damage as opposed to the traditional approach of solely considering the physical characteristics of a building. Within this update article, the same methods are used to evaluate two different artificial neural networks for modelling building repair and/or rebuild (recovery) time. By contrast to the damage models, the recovery models (RMs) consider (A) primarily social variables and then (B) introduce structural variables. These two models are then evaluated using centrality and shortest path concepts of graph theory as well as validated against data from the 2011 Joplin tornado. The results of this analysis do not show the same distinctions as were found in the analysis of the damage models from the companion article. The overarching lack of discernible and consistent differences in the RMs suggests that social variables that drive damage are not necessarily contributors to recovery. The differences also serve to reinforce that machine learning methods are best used when the contributing variables are already well understood.