# Artificial Neural Networks Applied in Civil Engineering
###### Abstract  
In recent years, artificial neural networks (ANN) and artificial intelligence (AI), in general, have garnered significant attention with respect to their applications in several scientific fields, varying from big data management to medical diagnosis. ANN techniques are already used in everyday applications, such as personalized advertisements, virtual assistants, autonomous driving, etc. The start of regeneration breakthroughs in ANNs can be traced back to the year 2005 and can be attributed to the development of novel learning architectures such as convolutional neural networks (CNN) and deep belief networks (DBN), with significant progress having been achieved so far and new methodologies having been proposed, such as generative adversarial networks (GAN). At present, ANN-based models are widely used in several forms of engineering applications.  
This Special Issue was organized in the framework of the IMSFARE research project: \"Advanced Information Modelling for SAFER structures against manmade hazards\", aiming to collect state-of-the-art research studies on applications and advances of ANNs to civil engineering problems. This Special Issue contains the latest progress on the application of ANN-based models in civil engineering problems, promoting cross-fertilization between related scientific subjects. In particular, the focus of this Special Issue 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.  
ANN-based models have found a wide range of uses and applications in engineering fields, including civil and structural engineering, with impressive results [1; 2; 3]. Figure 1 shows the research articles related to the development of ANN-based models published in the field of civil engineering. In particular, these research articles were obtained from the Scopus database (www.scopus.com) on 26 December 2022, using the query \"_TITLE-ABS-KEY_ ((\"_neural networks\") and (\"_civil\" or \"structural\" or \"_transportation\"_ or \"_geotechnical\"_ or \"_hydraulic\"_ or \"_environmental\"_ or \"_construction\"_ or \"_stm\" or \"_structural health\"_)) and (LIMIT-TO_ (_SUBJAREA_, \"_ENGI_\"))\" and (LIMIT-TO_ (_SRTYPE_, \"_))\", which returned 21,246 document results in total. The boost in publishing of ANN-related studies in civil engineering subjects is proof that such models are gaining momentum, and in the coming years, the growth is expected to continue, resulting into new improvements and applications.  
The results of a co-occurrence analysis are also presented herein based on the top keywords, including author keywords and index keywords, of the 21,246 documents collected from the Scopus database. For the purposes of this analysis, we searched Scopus using the word \"neural networks\" within \"Article title, Abstract and Keywords\" and we limited the search to the \"Engineering\" field and to all years until 26 December 2022. Within the results, we found the top-50 keywords of the 21,246 articles. A network visualization of the co-occurrence of the top-50 keywords is presented in Figure 2, generated using VOSviewer software [4], with five clusters presented using different colors and a minimum strength equal to 60.  
The authors who published the most journal articles related to subjects in this Special Issue on \"Artificial neural networks applied in civil engineering\" are listed in Table 1:  
Figure 1: Published articles (in Scopus) where ANN-based models were applied to civil engineering-related subjects (until 26 December 2022).  
Figure 2: Bibliometric map of the co-occurrence of top-50 keywords of documents including “neural networks” in the “Engineering” field, in Scopus (until 26 December 2022).