## 1 Introduction  
The application of methods and techniques of Machine Learning (ML) in many scientific fields has been increasing rapidly over recent decades. It has been recognized for many years that the nature of several problems allows the implementation of numerical procedures which imitate the function of the biological brain. However, two basic problems have prevented the wide research of ML-based methods in several scientific areas during the past decades, even though the efficiency of ML demonstrated very promising results.  
The first one concerns the lack of appropriate basic knowledge of the researchers, who are experts in sciences not closely related to the mathematic background required for the understanding of the basic principles of ML. Although collaboration with scientists specialized in ML was always an option, there is still a minimum level of knowledge of the basic principles of ML that is required for the proper description of the examined problems. In other words, a \"lingua franca\" for the co-operation of scientists active in any research area with scientists specialized in ML was not widely available in previous decades. However, the last 15 years, in approximate terms, have seen a significant increase in the number of scientists who have the appropriate level of knowledge for efficient co-operation with ML scientists. Additionally, postgraduate programs which offer expertise in ML are widely available nowadays. Thus, the problem of the required inter-science collaboration is now solvable.  
The second problem concerns the availability of reliable software dedicated to the numerical implementation of ML algorithms. The lack of this type of software during the previous decades was the second significant factor for the low number of published research works concerning ML applications. Nowadays, nevertheless, many software platforms with ML packages are widely available, accompanied by user-friendly interfaces, as well as the appropriate documentation.  
The overcoming of the problems described above led to the exponential growth of research works based on the implementation of ML algorithms for the solution of several scientific problems. This modern \"scientific environment\" created an opportunity for the invitation of researchers activated in the scientific area of seismic design and assessment of structures to publish their latest research works in the current Special Issue of _Applied Sciences_ on \"Application of Artificial Neural Networks for Seismic Design and Assessment\". The Artificial Neural Networks (ANN) are calculational tools which are classified to ML methods. Their extended application in the solution of civil engineering problems and, more specifically, in the area of earthquake engineering during the past few years led to the idea of the investigation of the recent advances in this scientific field.  
Five papers were submitted and accepted. These papers cover a wide range of applications of ANNs to the seismic design and assessment of structures. The central conclusion extracted from the published articles is that the ANNs, in combination with the available modern hardware and software, open new horizons for the significant improvement of the traditional procedures in earthquake engineering.