[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127338-en":3,"doc-seo-127338-105":30,"detail-sidebar-cat-0-en-105":92},{"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},127338,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques","Dynamic analysis of structural systems is computationally demanding, which can limit efficient seismic performance assessment in civil and structural engineering. The study evaluates multiple machine learning algorithms to predict dynamic-analysis responses of multi-story shear buildings, aiming to reduce reliance on time-intensive procedures. Large datasets are generated from standard sampling and conventional response spectrum modal analysis, then extensive hyper-parameter search and feature-importance evaluation identify the best model. The resulting model is deployed via a web application for prediction based on building characteristics.","computation  \nArticle  \nResponse Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques  \nManolis Georgioudakis 1, * and Vagelis Plevris 2  \nCitation: Georgioudakis, M.; Plevris, V. Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques. Computation 2023, 11, 126 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)computation11070126  \nAcademic Editors: Gavril Grebenisan, Alin Pop and Dan Claudiu Negr˘au  \nReceived: 6 May 2023  \nRevised: 9 June 2023  \nAccepted: 20 June 2023  \nPublished: 29 June 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Institute of Structural Analysis & Antiseismic Research, School of Civil Engineering, National Technical University of Athens, Zografou Campus, GR 15780 Athens, Greece  \n2 Department of Civil and Environmental Engineering, Qatar University, Doha P.O. Box 2713, Qatar; [vplevris@qu.edu.qa](vplevris@qu.edu.qa)  \n* Correspondence: [geoem@mail.ntua.gr](geoem@mail.ntua.gr)  \nAbstract: The dynamic analysis of structures is a computationally intensive procedure that must be considered, in order to make accurate seismic performance assessments in civil and structural engineering applications. To avoid these computationally demanding tasks, simplified methods are often used by engineers in practice, to estimate the behavior of complex structures under dynamic loading. This paper presents an assessment of several machine learning (ML) algorithms, with different characteristics, that aim to predict the dynamic analysis response of multi-story buildings. Large datasets of dynamic response analyses results were generated through standard sampling methods and conventional response spectrum modal analysis procedures. In an effort to obtain the best algorithm performance, an extensive hyper-parameter search was elaborated, followed by the corresponding feature importance. The ML model which exhibited the best performance was deployed in a web application, with the aim of providing predictions of the dynamic responses of multi-story buildings, according to their characteristics.  \nKeywords: response spectrum analysis; ensemble algorithms; machine learning; shear building; SHAP explainability  \n1. Introduction  \nMachine learning (ML) has numerous applications in modeling and simulation of structures [1] . One of the most common applications of ML in structural analysis is the prediction of structural behavior under different loads and environmental conditions. ML algorithms can be trained on data from previous structural analyses, to learn how different factors—such as material properties, geometry, and loading conditions—affect structural response. This information can then be used to predict the behavior of new structures, without the need for time-consuming and expensive additional analyses. Another interesting field of application is structural health monitoring (SHM) and damage identification [2], where, by analyzing the changes in structural response over time, and using data collected by SHM systems, ML algorithms can learn to detect and localize damage in structures and, in general, assess the health and condition of a structure over time. In design optimization [3], by analyzing the relationships between different design parameters and structural performance, ML algorithms can identify optimal design configurations that minimize weight, maximize stiffness, or achieve other desired performance characteristics [4,5] . ML can be also used to quantify the uncertainties associated with structural analyses, improving the accuracy of predictions and reducing the risk of failure [6] . Overall, the use ","cbCaibBe0m1nWRtW","https://ap.wps.com/l/cbCaibBe0m1nWRtW","pdf",1400142,1,22,"English","en",105,"# Introduction\n## Machine learning in structural analysis and earthquake engineering\n# Data generation and methodology\n## Response spectrum modal analysis and sampling\n## Hyper-parameter search and feature importance\n# Model evaluation and deployment\n## Best-performing algorithm selection\n## Web application for response prediction","[{\"question\":\"Why are simplified methods or machine learning used in seismic dynamic analysis?\",\"answer\":\"Dynamic structural analysis can be computationally intensive, so engineers often seek simplified estimation approaches. Machine learning helps predict structural responses without repeating expensive analyses.\"},{\"question\":\"How is the training data for the machine learning models generated?\",\"answer\":\"The study generates large datasets using standard sampling methods and conventional response spectrum modal analysis procedures for multi-story buildings.\"},{\"question\":\"What steps are used to select the best machine learning algorithm?\",\"answer\":\"An extensive hyper-parameter search is conducted, followed by feature-importance evaluation to determine which model performs best.\"}]","Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques | PDF",1785938372,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"response-spectrum-analysis-of-multi-story-shear-buildings-using-machine-learning-techniques","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/response-spectrum-analysis-of-multi-story-shear-buildings-using-machine-learning-techniques/127338/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are simplified methods or machine learning used in seismic dynamic analysis?","Question",{"text":76,"@type":77},"Dynamic structural analysis can be computationally intensive, so engineers often seek simplified estimation approaches. Machine learning helps predict structural responses without repeating expensive analyses.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the training data for the machine learning models generated?",{"text":81,"@type":77},"The study generates large datasets using standard sampling methods and conventional response spectrum modal analysis procedures for multi-story buildings.",{"name":83,"@type":74,"acceptedAnswer":84},"What steps are used to select the best machine learning algorithm?",{"text":85,"@type":77},"An extensive hyper-parameter search is conducted, followed by feature-importance evaluation to determine which model performs best.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]