Determination of Optimum Seismic Scale Factors for the ASCE 7-22 Scaling Method.

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Determination of Optimum Seismic Scale Factors for the ASCE 7-22 Scaling Method.

ارائه دهنده: Provider: Seyed Sina Ghanbari

اساتید راهنما: Supervisors: Mohammad Shooshtari (Ph.D)

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Mustafa Moghdisi (Ph.D), Amir Rezaei Samati(Ph.D)

زمان و تاریخ ارائه: Time and date of presentation: 2024

مکان ارائه: Place of presentation: 44

چکیده: Abstract: The scaling of earthquakes for time history analysis is a subject on which there is no consensus. Changing the prescribed methods of codes in different editions is proof of this. The scaling method is very important because it directly impacts the structural performance evaluation, and different methods lead to different structural responses. The scaling method in the 2016 and 2022 editions of ASCE 7 is very different from the method in previous editions. The new method selects the component with the highest spectral acceleration from each pair of accelerograms. The average spectrum obtained from the maximum components should not be lower than the target spectrum. After achieving the mentioned condition, the other component is also multiplied by the same coefficient of the component with the maximum spectral acceleration. As a result, each accelerogram pair has its specific coefficient. In this scaling method, infinitely many solutions will satisfy the scaling condition. The main goal of this research is to solve the problem of multiple answers in the described scaling method and somehow condition the scaling coefficients to the expected damage amount. For this purpose, a total of 237 pairs of accelerograms are selected from four different soil types. 6, 12, and 18-story structures with two types of structural systems are designed and analyzed for all four soil types. The results showed that the scale coefficients of a group of earthquakes selected for time history analysis can be estimated using artificial neural networks. Also, if the soil type is the same, the damage index of structures with the same structural systems but different heights, have the same variation pattern with the average intensity index.

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