I. Chamatidis, M. Stoumpos, G. Kazakis, N.A. Kallioras, S. Triantafyllou, V. Plevris and N.D. Lagaros, "Overview on Machine Learning Assisted Topology Optimization Methodologies", in Machine Learning in Modeling and Simulation (Part of the book series: Computational Methods in Engineering & the Sciences (CMES)), T. Rabczuk and K-J. Bathe (Eds.), Springer, pp. 373-394, 2023.

Abstract:

The past two decades saw tremendous developments in artificial intelligence (AI). Advancements in software, algorithms, and hardware led to the development of significantly more accurate and versatile artificial intelligence models. This rendered artificial intelligence a powerful tool that is used in diverse scientific areas, e.g. medicine and drug design, economics, and self-driving cars, among many others. These methods, having been successfully implemented in the simulation and modeling of structures, found their way to topology optimization problems, where artificial intelligence appears to have great potential for successful implementation. In conventional topology optimization, the optimal design of a specific domain must be calculated subject to specific constraints and the objective is to minimize the total compliance of the structure and use a specific amount of material. This is typically an iterative process that involves large matrices and can be very timeconsuming. By means of artificial intelligence models, referred to also as surrogate models (or surrogates), the computing time can be reduced significantly. The surrogate model is apriori trained offline. Following, during the optimization process the model is inferred based on input data, which is a lot faster due to limited matrix multiplications that the surrogate performs. The usual process involves either an artificial intelligence surrogate that complements the conventional procedure to reduce computational costs or a standalone surrogate which calculates the whole optimized structures by itself. The AI surrogates that are used belong to two main categories, i.e. Surrogates that use density and surrogates that use images. The surrogates that use density have similar inputs as the conventional method since the optimization process uses the density of the structure and is updated in each iteration of the AImodel. The surrogates that perform optimization on images are a bit different because they use techniques like image segmentation and filtering to output the optimized image (structure) which then is mapped into density. Most surrogates can be used for 2D and 3D structures and they are transferable, meaning that once trained they can be used in another topology optimization problem (thermodynamics or different material). The Background section contains an introduction to artificial intelligence, the surrogate models that will be used and an introduction to conventional topology optimization. The Literature Survey section provides areview ofrecent advancements of topology optimization using artificial intelligence models. This section is divided into two parts, the first describing the models that use density and the second the models that use image-based approaches.

V. Plevris, L. Hadji and R. Madan, "Exploring porosity impact on the free vibration of FG plates using trigonometric shear deformation theory", Structural Engineering and Mechanics, 92(3), pp. 267-275 (DOI: 10.12989/sem.2024.92.3.267), 2024.

Abstract:
This study investigates the free vibration behavior of functionally graded (FG) plates using trigonometric shear deformation plate theory. The novelty of this work lies in the incorporation of porosities, which are inherent in FG materials due to manufacturing processes, and their detailed impact on the vibrational performance of these plates. Unlike existing studies, this research comprehensively examines multiple porosity distribution patterns, including homogeneous, "O", "X", and "V" configurations, which are seldom analyzed together. The governing equations of motion are derived using Hamilton's principle and solved analytically with the Navier method for simply supported boundary conditions. A key contribution of this study is the exploration of how porosity levels, distribution types, and geometry parameters collectively influence the natural frequencies of FG plates. The results highlight the significant effect of different porosity patterns, with "X"-shaped porosity yielding the highest natural frequency and homogeneous distribution leading to the lowest. Furthermore, the findings reveal that increased porosity levels can either enhance or diminish the vibrational characteristics depending on the distribution pattern. These insights provide valuable guidance for optimizing the design of FG plates for various engineering applications, such as aerospace and biomedical industries.

Keywords:
FG plate; free vibration; functionally graded (FG) materials; porosity; trigonometric shear deformation theory.

Book: "Insights: Frontiers in Built Environment", Eds: Z. Chen, A. Matsumoto, J.R. Casas, V. Plevris, G. Tsiatas, H. Guo, Y. Li and S. Kaewunruen, Frontiers Media SA, Lausanne, 2024.

Description

We are now entering the third decade of the 21st Century, and, especially in recent years, the achievements made by researchers and professionals have been exceptional, leading to major advancements in the fast-growing field of the Built Environment.Frontiers has organised a series of Research Topics to highlight the latest advancements in research across the field of Built Environment with articles from the members of our accomplished Editorial Boards. This editorial initiative in question, led by Dr. Zhen Chen, Specialty Chief Editor of Frontiers Construction Management is focused on new insights, novel developments, current challenges, latest discoveries, recent advances, and future perspectives in the field of Built Environment. The Research Topic solicits brief, forward-looking articles from the Editorial Board Members that describe the state of the art, outlining recent developments and major accomplishments that have been achieved and that need to occur to move the field forward. Authors are encouraged to identify the greatest challenges in the sub-disciplines, and how to address those challenges.The goal of this special edition Research Topic is to shed light on the progress made in the past decade in the Built Environment field, and on its future challenges to provide a thorough overview of the field. This article collection will inspire, inform, and provide direction and guidance to researchers and practitioners in the field. We welcome original research, reviews, perspective, outstanding achievements in the Built Environment field and thought-provoking opinion pieces to this Research Topic.