Book: "Artificial Intelligence (AI) Applied in Civil Engineering", Eds: N.D. Lagaros and V. Plevris, MDPI, Basel, Switzerland, 698 pages, 2022.

Description

In recent years, the application of artificial intelligence (AI) in several scientific fields, varying from big data handling to medical diagnosis, has drawn significant attention. The use of AI is already present in our daily lives, as exemplified by personalized ads, virtual assistants, autonomous driving, etc. Not surprisingly, AI methodologies have demonstrated impressive results through a wide range of uses and applications in engineering fields, including civil and structural engineering. The increase in AI studies shows that the use of AI in civil engineering is gaining momentum and will keep increasing in the coming years, bringing new innovations and applications. This book collection contains applications and recent advances of AI with regard to civil engineering problems, promoting cross-fertilization between these scientific fields. In particular, the focus is on hybrid studies and applications related to structural engineering, transportation engineering, geotechnical engineering, hydraulic engineering, environmental engineering, coastal and ocean engineering, structural health monitoring, and construction management. The book contains 35 contributions in total from 19 different countries around the world, covering a broad range of topics related to the applications of AI in civil engineering.

D. Koutsantonis, K. Koutsantonis, N.P. Bakas*, V. Plevris, A. Langousis and S.A. Chatzichristofis, "Bibliometric Literature Review of Adaptive Learning Systems", Sustainability, 14(19) (DOI: 10.3390/su141912684), 2022.

Abstract:
In this review paper, we computationally analyze a vast volume of published articles in the field of Adaptive Learning, as obtained by the Scopus Database. Particularly, we use a query with search terms targeting the area of Adaptive Learning Systems by utilizing a combination of specific keywords. Accordingly, we apply a multidimensional scaling algorithm to construct bibliometric maps for keywords, authors, and references. Subsequently, we present the computational results for the studied dataset, reveal significant patterns appearing in the field of adaptive learning and the inter-item associations, and interpret the findings based on the current state-of-the-art literature in the area. Furthermore, we demonstrate the time-series of the evolution of the research terms, their trends over time, as well as their prevalent statistical associations.

Keywords:
Adaptive learning, intelligent tutoring systems, personalized learning, machine learning, bibliometrics.

G. Solorzano* and V. Plevris, “Computational intelligence methods in simulation and modeling of structures: A state-of-the-art review using bibliometric maps”, Frontiers in Built Environment, 8:1049616, 2022. DOI: 10.3389/fbuil.2022.1049616

Abstract:
The modeling and simulation of structural systems is a task that requires high precision and reliable results to ensure the stability and safety of construction projects of all kinds. For many years now, structural engineers have relied on hard computing strategies for solving engineering problems, such as the application of the Finite Element Method (FEM) for structural analysis. However, despite the great success of FEM, as the complexity and difficulty of modern constructions increases, the numerical procedures required for their appropriated design become much harder to process using traditional methods. Therefore, other alternatives such as Computational Intelligence (CI) techniques are gaining substantial popularity among professionals and researchers in the field. In this study, a data-driven bibliometric analysis is presented with the aim to investigate the current research directions and the applications of CI-based methodologies for the simulation and modeling of structures. The presented study is centered on a self-mined database of nearly 8000 publications from 1990 to 2022 with topics related to the aforementioned field. The database is processed to create various two-dimensional bibliometric maps and analyze the relevant research metrics. From the maps, some of the trending topics and research gaps are identified based on an analysis of the keywords. Similarly, the most contributing authors and their collaborations are assessed through an analysis of the corresponding citations. Finally, based on the discovered research directions, various recent publications are selected from the literature and discussed in detail to set examples of innovative CI-based applications for the modeling and simulation of structures. The full methodology that is used to obtain the data and generate the bibliometric maps is presented in detail as a means to provide a clearer interpretation of the bibliometric analysis results.

Keywords:
computational intelligence, structural analysis, soft computing, finite element method, structural engineering, bibliometric analysis, bibliometric maps.