[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119623-en":3,"doc-seo-119623-105":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":4,"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},119623,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Developing a robust machine learning framework for predicting the behavior of large-scale structure - Article Abstract","A rapid and accurate analysis is crucial to structural design and control. This paper introduces a comprehensive, robust framework for analyzing and predicting the behavior of large-scale structures by combining sample selection, dimensionality reduction, and advanced machine-learning techniques. Numerical studies cover a tower truss, a steel building, and a reinforced concrete structure. The framework is trained with 26 machine-learning methods and validated using extensive performance metrics. Chaos game optimization automates parameter updates, while SHAP quantifies feature contributions to support safer, more efficient engineering decisions.","Journal of Building Engineering 105 (2025) 112204  \nContents lists available at ScienceDirect  \nJournal of Building Engineering  \njournal [homepage: www.elsevier.com/locate/jobe](homepage: www.elsevier.com/locate/jobe)  \n| Developing a robust machine learning framework for predicting the behavior of large-scale structure |  |  |  |\n| --- | --- | --- | --- |\n| Siamak Talataharia, Fang Chena, Amir H. Gandomia,b,*\u003Cbr>a Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia b University Research and Innovation Center (EKIK), ´Obuda University, 1034, Budapest, Hungary |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Analyzing framework Structural analysis and design Machine learning\u003Cbr>Large-scale structures Performance predicting |  | A rapid and accurate analysis is crucial to structural design and control. This paper presents a comprehensive and robust framework for the analysis and prediction of the behavior of largescale structures. To ensure accurate prediction of structural responses, the proposed framework integrates sample selection, dimensionality reduction, and advanced machine learning analysis techniques. Three different types of structures are used as numerical examples: a tower truss structure, a steel building, and a reinforced concrete structure. The framework is trained using 26 different machine-learning methods and validated using a comprehensive set of performance metrics. Additionally, an enhanced machine learning method is introduced to achieve more accurate results. This technique leverages chaos game optimization to automate the parameter updating of the machine learning method. Shapley Additive Explanations (SHAP), as the interpretability technique, was incorporated into the framework to quantify each feature’s contribution, helping engineers identify key factors influencing structural behavior and ensuring safer, more efficient designs. The validation results demonstrate the high accuracy of the proposed framework in predicting the behavior of large-scale structures. The implications for structural engineering are significant, as this framework has the potential to enhance the design and assessment of large-scale structures, thereby enhancing their safety, durability, and performance. |  |\n\n1. Introduction  \nDesigning engineering problems is a complicated and challenging task [1] due to the existing large number of variables (large problem spaces), various influencing parameters (such as properties of materials, loading definitions, and geometry conditions), complicated relationships between the variables/parameters (nonlinear response space), and hardships in creating or solving the related equations (large uncertainty). Furthermore, the decisions (selecting appropriate designs) made in the design process should be grounded in a clear understanding of the problem, which requires engineers to thoroughly comprehend the problem and its implications to identify the most effective solutions.  \nThis requires a deep understanding of the physical nature of the problem, its governing equations, and the limitations of the available technology and materials ensuring that the chosen design solutions are both feasible and optimal. The problem can often be so complex that it may not be easy to define a meaningful description of it. For instance, designing buildings requires a profound  \n* Corresponding author. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia  \nE-mail addresses: [siamak.talatahari@mq.edu.au](siamak.talatahari@mq.edu.au), [siamak.talat@gmail.com](siamak.talat@gmail.com) (S. Talatahari), [Fang.Chen@uts.edu.au](Fang.Chen@uts.edu.au) (F. Chen), [gandomi@uts.edu](gandomi@uts.edu).  \nau (A.H. Gandomi).  \n[https://doi.org/10.1016/j.jobe.2025.112204](https://doi.org/10.1016/j.jobe.2025.112204)  \nReceived 13 September 2024; Received in revised form 3 February 2025;","cbCaiiJwa3tSfccO","https://ap.wps.com/l/cbCaiiJwa3tSfccO","pdf",9587082,1,33,"English","en",105,"# Introduction\n## Structural analysis and design challenges\n## Need for prompt, reliable analysis in optimization\n# Methods and framework overview\n## Sample selection and dimensionality reduction\n## Advanced machine-learning prediction and validation\n# Interpretability and enhanced optimization\n## Chaos game optimization for parameter updating\n## SHAP feature contribution analysis\n# Validation and implications\n## Accuracy and engineering impact","[{\"question\":\"What problem does the proposed framework address in structural engineering?\",\"answer\":\"It targets the need for rapid and accurate structural analysis and prediction to support design and control, especially within iterative optimization loops.\"},{\"question\":\"How does the framework improve prediction accuracy?\",\"answer\":\"It integrates sample selection, dimensionality reduction, and advanced machine-learning models, trained across 26 methods and assessed with comprehensive performance metrics.\"},{\"question\":\"How are predictions interpreted for engineering decision-making?\",\"answer\":\"The framework incorporates SHAP to quantify each feature’s contribution, helping engineers identify key factors affecting structural behavior.\"}]","Developing a robust machine learning framework for predicting the behavior of large-scale structure - 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