Abstract

The integration of advanced nanocomposites into concrete structures presents a promising avenue for enhancing their mechanical properties and durability. However, accurately predicting the vibrations of such structures remains a complex and challenging task due to the nonlinear behavior of the materials involved and the intricate interplay of various influencing factors. In this study, we propose the application of machine learning techniques as a powerful tool for estimating the vibrations of concrete structures reinforced with advanced nanocomposites. By leveraging the vast amounts of data generated from mathematical modeling, machine learning algorithms can effectively capture the intricate relationships between material properties, structural configurations, environmental conditions, and vibration responses. This article provides mathematical modeling simulation as the input of machine learning methods suitable for vibration prediction in concrete structures, including artificial neural networks. Furthermore, we discuss the key considerations and challenges associated with developing accurate and reliable machine learning models for this specific application domain, such as feature selection, model complexity, data quality, and interpretability. Through a comprehensive mathematical modeling, we highlight the potential of machine learning as a versatile and efficient tool for predicting vibrations in concrete structures reinforced with graphene oxide powders (GOPs). The integration of machine learning into the design, analysis, and maintenance of such structures not only facilitates more accurate predictions of vibration behavior but also enables proactive decision-making and optimization of structural performance. Finally, we outline future research directions and opportunities for further advancing the application of machine learning in the field of structural engineering and nanocomposite materials.