Application of the Carrera unified formulation and machine learning for vibration analysis of composite structures as the main part of construction robotics

This study presents an in-depth analysis of the vibration and dynamic deflection characteristics of a composite conical shell, which forms the main part of construction robotics. The Carrera unified formulation (CUF) is employed to derive the governing equations, accounting for the shell's complex geometrical and material properties. The composite shell is reinforced with graphene platelets (GPL) nanocomposites, providing enhanced mechanical performance crucial for the demanding conditions in construction robotics. Also, for more accuracy of the nanocomposites, porosity effect is considered. The incorporation of GPL reinforcement aims to improve the shell's structural integrity, offering superior strength-to-weight ratios and increased resistance to dynamic loading. The vibration and dynamic deflection analysis are conducted to evaluate the shell's response under various operating conditions, which is essential for ensuring the reliability and stability of robotic systems during construction tasks. Also, for more verification of the results, XGBoost and LightGBM algorithm as a hybrid machine learning algorithm is used to simulate the current system with low computational cost. This approach helps to cross-check the predicted outcomes and refine the model through comparison with simulated data. The use of advanced machine learning techniques facilitates an improved understanding of the dynamic behavior of the composite conical shell and enhances the precision of the results. The findings demonstrate that the reinforcement of the conical shell with GPL nanocomposites significantly enhances its dynamic performance, making it a promising candidate for future construction robotic applications. This research provides valuable insights into the application of composite materials in construction robotics and highlights the potential of CUF-based approaches in dynamic structural analysis.