Steel and Composite Structures

  • 제42권1호
  • /
  • Pages.123-137
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  • 2022
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Application of GMDH model for predicting the fundamental period of regular RC infilled frames

  • Tran, Viet-Linh (Department of Civil and Environmental Engineering, Sejong University) ;
  • Kim, Seung-Eock (Department of Civil and Environmental Engineering, Sejong University)
  • 투고 : 2020.10.07
  • 심사 : 2021.11.24
  • 발행 : 2022.01.10
⟨ 이전 논문 다음 논문 ⟩

초록

The fundamental period (FP) is one of the most critical parameters for the seismic design of structures. In the reinforced concrete (RC) infilled frame, the infill walls significantly affect the FP because they change the stiffness and mass of the structure. Although several formulas have been proposed for estimating the FP of the RC infilled frame, they are often associated with high bias and variance. In this study, an efficient soft computing model, namely the group method of data handling (GMDH), is proposed to predict the FP of regular RC infilled frames. For this purpose, 4026 data sets are obtained from the open literature, and the quality of the database is examined and evaluated in detail. Based on the cleaning database, several GMDH models are constructed and the best prediction model, which considers the height of the building, the span length, the opening percentage, and the infill wall stiffness as the input variables for predicting the FP of regular RC infilled frames, is chosen. The performance of the proposed GMDH model is further underscored through comparison of its FP predictions with those of existing design codes and empirical models. The accuracy of the proposed GMDH model is proven to be superior to others. Finally, explicit formulas and a graphical user-friendly interface (GUI) tool are developed to apply the GMDH model for practical use. They can provide a rapid prediction and design for the FP of regular RC infilled frames.

키워드

과제정보

This research was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (No. 2019R1A4A1021702 and No. 2021R1A2B5B01002577).

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