Session: 02-06-01: Machine Learning in Structural Dynamics and Aeroelasticity

Paper Number: 182583

182583 - Evaluation of the Influence of Generalized Variables and Geometry on the Dynamic Response of Structures Using Explainable Machine Learning Techniques 

This work exploits Machine Learning (ML) for evaluating 1D structural theories, with a particular focus on the influence of generalized displacement variables and physical parameters on the accuracy of dynamic analysis. The structural theories are generated using the Carrera Unified Formulation (CUF), which enables systematic multi-fidelity analysis by varying the order of the kinematic expansion.

A Neural Network (NN) is developed and trained using a set of input features that includes the generalized variables, geometric parameters, materials, and boundary conditions. The NN aims to estimate how accurately a given theory detects dynamic behavior for a given set of problem features, e.g., cross-section geometries and boundary conditions.

To interpret the predictive behavior of the trained model, explainable ML tools based on SHAP values are employed. This analysis evaluates the importance of each input variable on the output, providing a measure of how much the generalized variables and the physical structural parameters influence the accuracy achieved by each kinematic theory. The aim is to extract the most influential terms and use them to build optimized reduced models with the minimum number of unknown variables and the maximum fidelity. Numerical examples include metallic and composite structures and cross-section geometries adopted for aerospace applications, e.g., spars and stiffeners.

Presenting Author: Marco Petrolo Politecnico di Torino

Presenting Author Biography: Associate Professor and a member of the MUL2 Lab in the Department of Mechanical and Aerospace Engineering of Politecnico di Torino (www.mul2.com). My current research activities include the multiscale analysis of composites, micromechanics, and the development of higher-order structural theories. I cooperate with various institutions, including the University of British Columbia, the University of Washington, Deakin University, and NASA. I am a member of the Governing Board of the Italian Association of Aeronautics and Astronautics (AIDAA).