[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124750-en":3,"doc-seo-124750-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124750,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A general framework of high-performance machine learning algorithms - application in structural mechanics","Data-driven machine learning models using advanced AI techniques have been adopted over the past decades to emulate computationally expensive engineering simulations. Many workflows build training data from physical or numerical experiments to generate predictive relations for mechanical responses, yet efficient methods for accurate surrogate modeling of intensive engineering tasks remain a challenge. This study proposes high-performance ML algorithms for structural mechanics, implemented with parallel and distributed computing, and evaluates four algorithms on three structural engineering problems. Results show XGBoost with extended hyperparameter optimization (XGBoost-HYT-CV) achieving a 4.54% residual error across all test cases, supported by residual and sensitivity analyses, and outperforming existing ML approaches in several cases while demonstrating generic framework capabilities.","Computational Mechanics  \n[https://doi.org/10.1007/s00466-023-02386-9](https://doi.org/10.1007/s00466-023-02386-9)  \nA general framework of high-performance machine learning algorithms: application in structural mechanics  \nGeorge Markou1 · Nikolaos P. Bakas2,3 · Savvas A. Chatzichristofis4 · Manolis Papadrakakis5  \nReceived: 19 June 2023 / Accepted: 21 August 2023 © The Author(s) 2024  \nAbstract  \nData-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 hyperparameter 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 anda 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.  \nKeywords Machine learning · Deep learning artificial neural networks · Parallel training · Finite element method · Structural mechanics  \n1 Introduction  \nArtificial intelligence (AI) techniques have emerged over the last decades as an effective and efficient tool to predict analysis outputs for computationally demanding engineering  \nB George Markou [george.markou@up.ac.za](george.markou@up.ac.za)  \n1 Civil Engineering Department, University of Pretoria, Hatfield Campus, Pretoria 0028, South Africa  \n2 National Infrastructures for Research and Technology – GRNET, 7 Kifisias Avenue, Athens 11523, Greece  \n3 School of Liberal Arts and Sciences, Technology & AI Lab, The American College of Greece, Deree, Athens, Greece  \n4 Intelligent Systems Lab and Department of Computer Science, Neapolis University Pafos, 2 Danais Avenue, Pafos 8042, Cyprus  \n5 Department of Civil Engineering, National Technical University of Athens, 9, Iroon Polytechniou str, Athens 15780, Zografou, Greece  \nproblems. Application areas requiring multiple algorithmic operations (nonlinear dynamics analysis, design optimization, structural reliability, stochastic simulations) have benefited from AI computational approaches eliminating the need for performing full-scale numerical analyses by providing adequate estimations for the outputs of interest [1–5] . Furthermore, significant work was performed on machine learning (ML) in computational science and engineering [6– 10], while the use of ML algorithms in handling constitutive  \nmaterial modeling is also an emerging field [11–15] .  \nFurthermore, work related to the prediction of the fundamental period of infilled frame structures can be found in [16], where an artificial bee colony-based neural network was proposed. Another research work that studied the development of predictive models for computing the fundamental period of frames is [17], where ML al","cbCaipfCUXZFSTbd","https://ap.wps.com/l/cbCaipfCUXZFSTbd","pdf",3452060,1,25,"English","en",105,"# Abstract\n# Keywords\n# 1 Introduction","[{\"question\":\"What problem does the proposed framework address in structural mechanics?\",\"answer\":\"It targets the challenge of building accurate predictive models for computationally intensive structural mechanics tasks, where AI training must be efficient enough to be practical.\"},{\"question\":\"How is parallel and distributed computing used in the study?\",\"answer\":\"The ML algorithms are implemented in parallel and distributed computing environments to handle the heavy computational cost of training deep models and managing large data structures.\"},{\"question\":\"Which algorithm achieved the best generalization accuracy?\",\"answer\":\"The extreme gradient boosting method with extended hyperparameter optimization (XGBoost-HYT-CV) showed the best performance, producing a 4.54% residual error across the considered test cases.\"}]","A general framework of high-performance machine learning algorithms - 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