A general framework of high-performance machine learning algorithms: application in structural mechanics

• Published in: Computational mechanics 2024-04, Vol.73 (4), p.705-729
• Main Authors: Markou, George, Bakas, Nikolaos P., Chatzichristofis, Savvas A., Papadrakakis, Manolis
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Classical and Continuum Physics; Computational Science and Engineering; Computer networks; Data processing; Distributed processing; Engineering; Machine learning; Mathematical models; Mechanical analysis; Mechanics; Mechanics (physics); Original Paper; Prediction models; Sensitivity analysis; Statistical analysis; Structural engineering; Theoretical and Applied Mechanics
• ISSN: 0178-7675; 1432-0924
• DOI: 10.1007/s00466-023-02386-9

Data-driven models utilizing powerful artificial intelligence (AI) algorithms have been implemented over the past two decades in different fields of simulation-based engineering science. Most numerical procedures involve processing data sets developed from physical or numerical experiments to create closed-form formulae to predict the corresponding systems’ mechanical response. Efficient AI methodologies that will allow the development and use of accurate predictive models for solving computational intensive engineering problems remain an open issue. In this research work, high-performance machine learning (ML) algorithms are proposed for modeling structural mechanics-related problems, which are implemented in parallel and distributed computing environments to address extremely computationally demanding problems. Four machine learning algorithms are proposed in this work and their performance is investigated in three different structural engineering problems. According to the parametric investigation of the prediction accuracy, the extreme gradient boosting with extended hyper-parameter optimization (XGBoost-HYT-CV) was found to be more efficient regarding the generalization errors deriving a 4.54% residual error for all test cases considered. Furthermore, a comprehensive statistical analysis of the residual errors and a sensitivity analysis of the predictors concerning the target variable are reported. Overall, the proposed models were found to outperform the existing ML methods, where in one case the residual error was decreased by 3-fold. Furthermore, the proposed algorithms demonstrated the generic characteristic of the proposed ML framework for structural mechanics problems.