[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117593-en":3,"doc-seo-117593-105":30,"detail-sidebar-cat-0-en-105":95},{"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},117593,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Damage Class Prediction using Machine Learning Algorithm - paper abstract","This paper investigates machine learning algorithms for vulnerability assessment of buildings and structures, aiming to replace time-intensive nonlinear dynamic earthquake analyses. The study builds damage class prediction models using building data generated from numerous incremental dynamic analyses with varied ground and structural parameters such as peak ground acceleration, peak ground velocity, aspect ratio, member size, and axial load ratio. Material nonlinearity is modeled with a plastic hinge approach and geometric nonlinearity via P-delta effects. Results show Random Forest and XGBoost provide generally better accuracy with reduced computation efforts.","Vocational Training Council  \nVTC Institutional Repository  \n\n| Department of Construction, Environment and Engineering | Research Repository |\n| --- | --- |\n\n2023  \nDamage Class Prediction using Machine Learning Algorithm Sung-Hei Luk  \nFollow this and additional works at: [https://repository.vtc.edu.hk/thei-dcee-sp](https://repository.vtc.edu.hk/thei-dcee-sp)  \nDamage Class Prediction using Machine Learning Algorithm  \n*Sung-Hei LUK 1)  \n1) Department of Construction Technology and Engineering, THEi, Hong Kong  \n1) [henrylsh@thei.edu.hk](henrylsh@thei.edu.hk)  \nABSTRACT  \nThis paper aims to investigate the use of machine learning algorithms in vulnerability assessment of buildings and structures. Traditionally , dynamic performance of buildings under earthquakes is determined by means of non-linear time-history analyses. This method is accurate, but it is known as a time-consuming process and it requires advance knowledge on modelling. As an alternative, responses of building under earthquakes can be obtained using well-trained machine learning models. Nowadays, this is also called a data-driven approach.  \nIn the current study, machine learning models for damage class prediction are developed using the building data generated from numerous incremental dynamic analyses. Building models with variety of ground and structural parameters, such aspeak ground acceleration, peak ground velocity , aspect ratio of building, member’s size, axial load ratio, etc. , are considered in this study as the input parameters. Different past earthquake histories with increasing peak ground acceleration are used in the study. Material non-linearity is modelled using plastic hinge model , while geometric nonlinearity is considered using P-delta effects. The effectiveness of typical machine learning models, including ensemble models and deep learning via artificial neural network, is investigated. The results reveal that well-prepared machine learning models are also capable of predicting structural response and damage level with adequate accuracy and minimum computation efforts. The performance of Random Forest and XGBoost is generally better. Other possible applications of machine learning models have been investigated as well in the study.  \nAcknowledgement  \nThe work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. UGC/FDS25/E05/21)  \n1) Lecturer","cbCaippBDXYVfq8E","https://ap.wps.com/l/cbCaippBDXYVfq8E","pdf",179054,1,2,"English","en",105,"# Abstract\n# Acknowledgement","[{\"question\":\"What problem does the paper address in earthquake vulnerability assessment?\",\"answer\":\"The paper targets the high cost of nonlinear time-history analyses used to determine building performance under earthquakes by proposing data-driven machine learning alternatives.\"},{\"question\":\"How is the training data generated for the machine learning models?\",\"answer\":\"Training data come from building responses produced through numerous incremental dynamic analyses using diverse ground and structural parameters and multiple earthquake histories with increasing peak ground acceleration.\"},{\"question\":\"What modeling approaches represent material and geometric nonlinearity?\",\"answer\":\"Material nonlinearity is represented using a plastic hinge model, while geometric nonlinearity is captured using P-delta effects.\"},{\"question\":\"Which machine learning methods perform better according to the results?\",\"answer\":\"Random Forest and XGBoost generally achieve better predictive performance with adequate accuracy and minimum computation efforts.\"}]","Damage Class Prediction using Machine Learning Algorithm - 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