Enhancing the structural integrity of sport stadium roof panels using nano-reinforced composites and machine learning techniques

Abstract

The rising demand for strong lightweight materials which can withstand test of time has resulted in using nano-reinforced composite materials for roof panel systems which protect current sports stadiums against intense dynamic forces. The researchers created an analytical-computational framework which enhances stadium roof panel strength through graphene platelet-reinforced composite materials and deep neural network verification which functions as an advanced machine learning method. The roof system is modeled as a doubly curved graphene platelet-reinforced composite panel exposed to dynamic loading conditions that simulate wind gusts and seismic excitations. The effective material properties of the nano-reinforced composite are evaluated by incorporating the contribution of graphene platelets within the polymeric matrix. The panel's structural behavior operates under first-order shear deformation theory which defines transverse shear deformation through a specific shear correction factor. The researchers use energy principles to derive governing equations of motion which they solve analytically using Navier's solution technique that employs double trigonometric series expansions. The Laplace transform handles analytical work for dynamic system behavior through its ability to evaluate transient response which needs its inverse transformation to be solved using a modified Dubner and Abate numerical method. The research confirms roof panel dynamic response through deep neural network training which uses analytical method datasets to produce fast computational results. The research findings demonstrate that analytical methods and machine learning approaches generate identical results which confirm the system's accuracy. The results deliver important information which assists in designing nano-reinforced stadium roof panels that provide superior stability and strength, and vibration control performance.