{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T22:35:39Z","timestamp":1789770939441,"version":"4.0.1"},"reference-count":25,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/computation11070126","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:43:30Z","timestamp":1688085810000},"page":"126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2379-6633","authenticated-orcid":false,"given":"Manolis","family":"Georgioudakis","sequence":"first","affiliation":[{"name":"Institute of Structural Analysis & Antiseismic Research, School of Civil Engineering, National Technical University of Athens, Zografou Campus, GR 15780 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7377-781X","authenticated-orcid":false,"given":"Vagelis","family":"Plevris","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1049616","DOI":"10.3389\/fbuil.2022.1049616","article-title":"Computational intelligence methods in simulation and modeling of structures: A state-of-the-art review using bibliometric maps","volume":"8","author":"Solorzano","year":"2022","journal-title":"Front. Built Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Georgioudakis, M., and Plevris, V. (2018). A Combined Modal Correlation Criterion for Structural Damage Identification with Noisy Modal Data. Adv. Civ. Eng., 3183067.","DOI":"10.1155\/2018\/3183067"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5457","DOI":"10.1007\/s11831-022-09773-0","article-title":"The Mosaic of Metaheuristic Algorithms in Structural Optimization","volume":"29","author":"Lagaros","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_4","unstructured":"Plevris, V., Lagaros, N.D., Charmpis, D., and Papadrakakis, M. (2006, January 28\u201330). Metamodel assisted techniques for structural optimization. Proceedings of the First South-East European Conference on Computational Mechanics (SEECCM-06), Kragujevac, Serbia."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/BF02736645","article-title":"Large scale structural optimization: Computational methods and optimization algorithms","volume":"8","author":"Papadrakakis","year":"2001","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tsompanakis, Y., Lagaros, N., and Papadrakakis, M. (2008). Structural Design Optimization Considering Uncertainties, Taylor & Francis. Chapter 21.","DOI":"10.1201\/b10995"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"876077","DOI":"10.3389\/fbuil.2022.876077","article-title":"Editorial: Artificial Intelligence-Powered Methodologies and Applications in Earthquake and Structural Engineering","volume":"8","author":"Lu","year":"2022","journal-title":"Front. Built Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1177\/8755293020919419","article-title":"The promise of implementing machine learning in earthquake engineering: A state-of-the-art review","volume":"36","author":"Xie","year":"2020","journal-title":"Earthq. Spectra"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.strusafe.2017.12.001","article-title":"A machine learning framework for assessing post-earthquake structural safety","volume":"72","author":"Zhang","year":"2018","journal-title":"Struct. Saf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112518","DOI":"10.1016\/j.engstruct.2021.112518","article-title":"Prediction of seismic drift responses of planar steel moment frames using artificial neural network and extreme gradient boosting","volume":"242","author":"Nguyen","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"111582","DOI":"10.1016\/j.engstruct.2020.111582","article-title":"DynNet: Physics-based neural architecture design for nonlinear structural response modeling and prediction","volume":"229","author":"Pakzad","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Abd-Elhamed, A., Shaban, Y., and Mahmoud, S. (2018). Predicting Dynamic Response of Structures under Earthquake Loads Using Logical Analysis of Data. Buildings, 8.","DOI":"10.3390\/buildings8040061"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106584","DOI":"10.1016\/j.compstruc.2021.106584","article-title":"Prediction of seismic damage spectra using computational intelligence methods","volume":"253","author":"Gharehbaghi","year":"2021","journal-title":"Comput. Struct."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1007\/s43452-023-00631-9","article-title":"Machine learning-based seismic response and performance assessment of reinforced concrete buildings","volume":"23","author":"Kazemi","year":"2018","journal-title":"Arch. Civ. Mech. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106886","DOI":"10.1016\/j.compstruc.2022.106886","article-title":"Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction","volume":"274","author":"Kazemi","year":"2023","journal-title":"Comput. Struct."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1016\/j.istruc.2022.08.023","article-title":"Explainable machine learning based efficient prediction tool for lateral cyclic response of post-tensioned base rocking steel bridge piers","volume":"44","author":"Wakjira","year":"2022","journal-title":"Structures"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111222","DOI":"10.1016\/j.engstruct.2020.111222","article-title":"A simplified performance based approach for the evaluation of seismic performances of steel frames","volume":"224","author":"Montuori","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_18","unstructured":"(2004). Design of Structures for Earthquake Resistance\u2014Part 1: General Rules, Seismic Actions and Rules for Buildings (Standard No. EN 1998-1 (Eurocode 8))."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge Regression: Biased Estimation for Nonorthogonal Problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_22","unstructured":"Bengio, S., Wallach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R. (2018, January 4\u20136). CatBoost: Unbiased boosting with categorical features. Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, QC, Canada."},{"key":"ref_23","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Plevris, V., Solorzano, G., Bakas, N., and Ben Seghier, M.E.A. (2022, January 5\u20139). Investigation of performance metrics in regression analysis and machine learning-based prediction models. Proceedings of the 8th European Congress on Computational Methods in Applied Sciences and Engineering, Oslo, Norway.","DOI":"10.23967\/eccomas.2022.155"},{"key":"ref_25","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Advances in Neural Information Processing Systems, Curran Associates, Inc."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/11\/7\/126\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:03:53Z","timestamp":1760126633000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/11\/7\/126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"references-count":25,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["computation11070126"],"URL":"https:\/\/doi.org\/10.3390\/computation11070126","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,29]]}}}