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The use of meshfree and particle methods in the field of bioengineering and biomechanics has significantly increased. This may be attributed to their unique abilities to overcome most of the inherent limitations of mesh-based methods in dealing with problems involving large deformation and complex geometry that are common in bioengineering and computational biomechanics in particular. This review article is intended to identify, highlight and summarize research works on topics that are of substantial interest in the field of computational biomechanics in which meshfree or particle methods have been employed for analysis, simulation or/and modeling of biological systems such as soft matters, cells, biological soft and hard tissues and organs. We also anticipate that this review will serve as a useful resource and guide to researchers who intend to extend their work into these research areas. This review article includes 333 references. Highlights  We present an up to date review of meshfree and particle methods, including their advantages and limitations.  We comprehensively discuss past and recent applications of meshfree and particle methods in bioengineering and biomechanics.  We identify research areas, directions, and opportunities for the application of meshfree and particle methods in bioengineering and biomechanics.

In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented. Highlights We present an up to date review of the application of artificial intelligence in materials modeling and design.  We comprehensively discuss past and recent applications in modeling and design of polymers, metals, ceramics and other materials. We identify current research focal points, challenges, and opportunities for the application of artificial intelligence in materials modeling and design.

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PurposeThe use of recycled concrete (RC) can reduce the greenhouse emissions associated with the production of cement, which is one of the primary materials used for the execution of construction projects. This research aims to review the state of knowledge in the field of RC research. An understanding of the state of the art in the RC domain justifies future research in this field.Design/methodology/approachA systematic and comprehensive search of RC-related literature was conducted using the Scopus database. In this research, the bibliometrix R-package was used for the analysis of bibliometric information of the selected papers. The software was used to create a map, which highlights the trends and gaps in the RC knowledge domain.FindingsThe results reveal the research themes, clusters, collaboration networks and advancement of knowledge in the field of RC research. The study integrates the literature focussed on RC research and provides a platform for progression of knowledge in this field.Originality/valueThe study contributes to the growing body of knowledge by providing an up-to-date RC knowledge map based on an analysis of bibliographic data. Information gleaned from previous studies suggests that bibliometric review can strengthen and complement the findings emerging from other forms of literature reviews. The study reported here is one of the first studies to provide insights into the evolution of RC research.

Molecular dynamics (MD) simulations are employed in this paper to study the behavior of single-layer and rotated double-layer graphene sheets under a high velocity impact. The AIREBO force field is used for MD simulations. Stress wave propagation is investigated, and cone-wave and axial-wave velocities are determined. The coefficient of restitution for the double-layer graphene sheet is calculated at different impact incident angles and velocities. Impact and rebound kinetic energy of projectile under the impact simulation of different rotation angles double-layer graphene sheet is monitored. High cone-wave and axial-wave velocities show that single-layer and double-layer graphene sheets have potential applications in impact protection materials.

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