APPLICATION OF MACHINE LEARNING AIDED STOCHASTIC ELASTOPLASTIC ANALYSIS USING AI

The application of machine learning in the field of stochastic elastoplastic analysis has shown promising results in enhancing the accuracy and efficiency of the analysis process. By leveraging AI techniques, such as deep learning algorithms and datadriven modelling, the traditional limitations of deterministic approaches can be overcome, allowing for a more comprehensive understanding of the complex behaviour of elastoplastic materials under uncertain conditions. the use of machine learning in aiding stochastic elastoplastic analysis using AI. Firstly, the fundamentals of stochastic elastoplastic analysis are discussed, highlighting the challenges associated with incorporating uncertainties in material properties, loading conditions, and boundary conditions. Traditional methods, based on Monte Carlo simulations or response surface modelling, are limited by their computational cost and assumptions. Machine learning offers an alternative approach by utilizing historical data to capture the complex relationships between uncertain parameters and the elastoplastic response. By training AI models on a diverse dataset of material properties, loading scenarios, and corresponding responses, these models can learn to predict the elastoplastic behaviour under new stochastic conditions. This data-driven approach reduces the computational burden associated with traditional methods while providing accurate and reliable predictions. The application of machine learning in stochastic elastoplastic analysis has several advantages. Firstly, it enables the consideration of a wide range of uncertain parameters and their interactions, which is crucial for capturing the inherent variability in material behaviour. Secondly, AI models can provide rapid predictions, allowing for efficient sensitivity analysis and optimization studies. The incorporation of real-time sensor data can improve the accuracy of predictions, enabling the monitoring and assessment of structural health in real-world scenarios. machine learning aided stochastic elastoplastic analysis has been successfully applied.