Based on the life cycle engineering aspects, as prediction, design, assessment, maintenance, and management of structures and according to performance based approach, civil engineering structures have to fulfill essential requirements for resilience, sustainability, and safety from possible risks, as earthquakes, fires, floods, extreme winds, and explosions.

Analysis of the performance indicators, which are of a great importance for the structural behavior and for fulfillment of the above-mentioned requirements, is impossible without conducting complex mathematical calculations.

Artificial Neural Networks and Fuzzy Neural Networks are a typical example of a modern interdisciplinary field which gives the basic knowledge principles that could be used for solving many different and complex engineering problems which could not be solved otherwise (using traditional modeling and statistical methods). Neural Networks are capable of collecting, memorizing, analyzing, and processing large amount of data gained from some experiments or numerical analyses. Because of that, Neural Networks are often better calculation and prediction methods compared to some of the classical and traditional calculation methods. They are excellent in predicting data and they can be used for creating prognostic models that could solve various engineering problems and tasks. Trained Neural Network serves as an analytical tool for qualified prognoses of the results, for any input data which have not been included in the learning process of the network. Their usage is reasonably simple and easy, yet correct and precise. These positive effects completely justify their application, as prognostic models, in engineering researches.

The use of the neural-network-based approach, as an unconventional approach for solving complex civil engineering problems, has a huge significance in the modernization of the construction design processes. Worldwide studies show that Artificial Neural Networks and Fuzzy Neural Networks can be successfully used as prognostic models in different engineering fields, especially in those cases where some prior (numerical or experimental) analyses were already made.

The objective of this special issue is to highlight the importance of more deep investigations on possibilities of using Artificial Neural Networks and Fuzzy Neural Networks as effective and powerful tools for solving engineering problems. High quality research papers with original results, as well as review articles which are closely related to the topic of the special issue are welcome.

Potential topics include but are not limited to the following:

• Theory and application of Fuzzy Neural Networks for solving civil engineering problems
• Theory and application of Artificial Neural Networks for solving civil engineering problems
• Theory and application of Neural Networks for multicriterion optimization of structures
• Neural Networks in theory of structures
• Neural Networks as prediction models based on experimental investigations
• Neural Networks as prediction models based on numerical investigations
• Application of Neural Networks for predicting the structural behavior for different load conditions
• Application of Neural Networks for defining material properties
• Application of Neural Networks for estimating the value of real estate