AI-driven seismic optimization of outrigger systems in high-rise buildings: A machine learning framework for enhanced performance in earthquake-prone regions
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F25%3A10258521" target="_blank" >RIV/61989100:27730/25:10258521 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2352710225021011" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352710225021011</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jobe.2025.113864" target="_blank" >10.1016/j.jobe.2025.113864</a>
Alternative languages
Result language
angličtina
Original language name
AI-driven seismic optimization of outrigger systems in high-rise buildings: A machine learning framework for enhanced performance in earthquake-prone regions
Original language description
This study introduces a novel machine learning-based framework to optimize outrigger systems for enhancing the seismic performance of tall buildings. Unlike conventional static or heuristic design methods, the proposed model integrates Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for seismic response prediction, and applies Genetic Algorithms (GA) within a closed-loop optimization cycle. The framework uniquely combines supervised learning, reinforcement-based refinement, and structural simulations (pushover and time-history analysis) to automatically identify the optimal outrigger configuration position, stiffness, damping, and material properties under varying seismic loads. Empirical data and synthetic simulations are used to train the models, resulting in significant reductions in lateral displacement (46.67 %), inter-story drift (55.56 %), and improved energy dissipation (33.33 %). Benchmarking against standalone ANN and SVM models demonstrates superior prediction accuracy (RMSE = 0.089) and design efficiency. The proposed ANN-SVM-GA model is code-adaptable, resource-efficient, and scalable across seismic zones, achieving an 18 % RMSE reduction over ensemble baselines marking a significant advancement in intelligent structural design for earthquake-prone regions.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20100 - Civil engineering
Result continuities
Project
<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Building Engineering
ISSN
2352-7102
e-ISSN
2352-7102
Volume of the periodical
112
Issue of the periodical within the volume
Volume 112
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
32
Pages from-to
1-32
UT code for WoS article
001569209600008
EID of the result in the Scopus database
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