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Nonmodel Seismic Assessment of Eccentrically Braced Steel Frames with Masonry Infills Using Machine Learning Techniques

Chalabi, Romina | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56370 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Mohtasham Dolatshahi, Kiarash
  7. Abstract:
  8. This study investigates the seismic behavior of eccentrically braced frames (EBFs) taking into account the influence of masonry infill walls. The primary objectives of the study are to predict the seismic global response and develop associated fragility curves using a nonmodel scenario-based machine learning framework. To model the infill walls, equivalent diagonal struts are employed, and a nonlinear pushover analysis is conducted to assess the overall impact of infills on 4- and 8-story EBF structures. An extensive database of 4 bare and 48 infilled EBFs with various infill properties is assembled to predict story-based engineering demand parameters (EDPs) containing peak and residual story drift ratios and peak floor accelerations (PFA). The collected raw database consists of 37164 and 461480 data for each input variable of bare and infilled EBFs, respectively, resulting from the incremental dynamic analysis under 44 far-field ground motions. It encompasses seismic intensity measure (Saavg), wavelet-based rDSF, derived from roof absolute acceleration, frame geometric information, and infill parameter as input features. Both regression analysis and machine learning techniques are utilized to estimate EDPs under two scenarios of Input-Output and Output-Only based on the availability of Saavg. Comparing error measures, particularly in the ExGBT algorithm, reveals strong observed-predicted EDP compatibility. Linear equations from bare frame data predict infilled frame EDPs without modification coefficients. Data preprocessing demonstrates that infills decrease drift-based EDPs due to stiffness improvement, while their impact on PFA remains inconclusive. Saavg and rDSF-based fragility curves at three damage states show accurate predictions and effectively reduce vulnerability in infilled 4-story EBFs. However, infills undesirably affect non-ductile behavior in 8-story EBFs, leading to increased global damage as supported by pushover analysis
  9. Keywords:
  10. Eccentrically Braced Frame (EBF) ; Masonry Infilled Walls ; Engineering Demand Parameters (EDP) ; Machine Learning ; Fragility Curve ; Nonmodel Approach

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