The Emerging Engineering Applications of Artificial Neural Networks: A Visionary Study

Abstract

In various engineering disciplines, researchers are always ambitious to understand and predict the behavior of an element or a system. Thus, for a specific area of research, various experimental and numerical studies are typically implemented resulting in a massive amount of data and findings. The findings are sometimes contradicting or lack the full picture due to the many entailed parameters that are difficult to consider in one study. To address this challenge, smart technologies such as Artificial Neural Networks (ANN) are recognized as vital techniques that allow system designers to compile and manage the huge collected data points pertinent to the problem under investigation. The ANN is capable of identifying the factors that significantly impact the behavior and performance of different engineering systems. This can be accomplished through the ANN systematic process that entails three consecutive stages: training, testing, and validation based on a sufficient number of data points. The outcomes of the ANN-based research study in the engineering area range from the development of a new model for the prediction of a performance characteristic, modifying a design code equation, or validation of experimental/numerical results. This visionary paper highlights a number of innovative applications of the ANN technique in various emerging engineering fields. Specifically, the transfer length of prestressing strands, strength of recycled aggregate concrete, and fracture parameters of fiber-reinforced concrete are highlighted in this study.