Machine learning-based seismic response and performance assessment of reinforced concrete buildings

• Autorzy: Kazemi F. , Asgarkhani N. , Jankowski R.

Identyfikatory

DOI

10.1007/s43452-023-00631-9

• Języki publikacji: EN

Abstrakty

EN

Complexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data points of training dataset for developing data-driven techniques, Incremental Dynamic Analyses (IDAs) were performed considering 165 RC MRFs with two-, to twelve-Story elevations having the bay lengths of 5.0 m, 6.1 m, and 7.6 m assuming near-fault seismic excitations. Then, important structural features were considered in datasets to train and test the ML-based prediction models, which were improved with innovative techniques. The results show that improved algorithms have higher R² values for estimating the Maximum Interstory Drift Ratio (IDR_max), and two improved algorithms of artificial neural networks and extreme gradient boosting can estimate the Median of IDA curves (M-IDAs) of RC MRFs, which can be used to estimate the seismic limit-state capacity and performance assessment of existing or newly constructed RC buildings. To validate the generality and accuracy of the proposed ML-based prediction model, a five-Story RC building with different input features was used, and the results are promising. Therefore, graphical user interface is introduced as user-friendly tool to help researchers in estimating the seismic limit-state capacity of RC buildings, while reducing the computational cost and analytical efforts.

Słowa kluczowe

EN

machine learning method; maximum interstory drift ratio; seismic limit state capacity; predicting seismic performance; seismic probabilistic assessment

PL

uczenie maszynowe; przewidywanie wstrząsów sejsmicznych; ocena probabilistyczna

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2023
• Tom: Vol. 23, no. 2
• Strony: art. no. e94, 2023
• Opis fizyczny: Bibliogr. 63 poz., rys., tab., wykr.

Twórcy

autor

Kazemi F.

• Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, Ul. Narutowicza 11/12, 80‑233 Gdansk, Poland

• https://orcid.org/0000-0002-2448-1465

autor

Asgarkhani N.

• Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, Ul. Narutowicza 11/12, 80‑233 Gdansk, Poland

autor

Jankowski R.

• Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, Ul. Narutowicza 11/12, 80‑233 Gdansk, Poland

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).