Finite element data collected and Machine learning algorithms to predict the mechanical properties of innovative CLT

This folder includes the data collected from the finite element simulations of the innovative CLT to compute its mechanical properties, the error of the closed-form solutions predicting the bending stiffness in the minor direction D22, the variation of the distance between the Reissner Mindlin and Bending Gradient theory in terms of spacing between lateral lamellas, the hyperparameters tuning of several ML algorithms (Regression Tree, Random Forest, Gradient Boosting and Artificial Neural Network), the ML evaluations, the saved artificial neural network algorithms to predict each mechanical property of innovative CLT, and the ML application to use it.