提升RC建築構架耐震能力之最佳化設計

機構典藏 > College of Engineering > Graduate Institute & Department of Civil Engineering > Thesis > Item 987654321/114617

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题名:	提升RC建築構架耐震能力之最佳化設計
其它题名:	Optimum design of enhancing the seismic capacity of RC building frames
作者:	紀彥豪;Ji, Yan-Hao
贡献者:	淡江大學土木工程學系碩士班高金盛;Kou, Chin-sheng
关键词:	類神經網路;最佳化設計;耐震能力評估;側推分析;Artifician Neural Networks;Optimal Design;Seidmic Capacity Assessment;Push-over Analysis
日期:	2017
上传时间:	2018-08-03 14:58:25 (UTC+8)
摘要:	國內產官學界在將近二十年努力之後，針對低矮樓層建築結構之耐震能力評估與耐震補強工作已近乎完成，接下來需要面對的是中高樓層之建築結構，為此，目前採用的耐震能力評估模式是否適用於中高樓層之建築結構，以及如何以最經濟手段提升中高樓層建築結構之耐震能力，便成為一個值得探討之課題。本文針對RC建築構架，先以ETABS軟體進行分析設計，並根據國內耐震設計規範，探討週期、等效阻尼比及有效地震力折減係數等參數，對於目前業界常用的兩種耐震評估方式TEASPA與SERCB所評估耐震能力的差異性影響，並提出修正建議；本文亦利用兩種最佳化模式進行提升RC建築構架耐震能力最佳化設計之研究，藉以獲得RC建築構架耐震能力之最佳化提升模式。本文研究結果顯示，利用本文所建立之RC建築構架之最佳化提升模式，可得到十樓構架耐震能力性價比最高之最佳化提升設計；另外，本文建議於後續研究可加入更多其他設計條件，藉以將此模式能更廣泛的應用在各種複雜的工程問題上。 After nearly two decades of effort, the seismic capacity assessment and seismic reinforcement of the low-rise building has been almost completed. Next, the structural design of the high-rise building needs to be optimized for seismic capacity. Therefore, whether the capacity assessment model is suitable for the high-rise building structure and to increase the seismic capacity of the high-rise building structure through the most economical way to enhance have become a worthy subject. First, we analyze the RC seismic structure using ETABS. Next, we compare the results derived from TEASPA and SERCB using the ETABS output data and the periodicity, equivalent damping ratio and effective seismic force reduction coefficient defined by the relevant seismic design code and propose the modifying suggestions. Then, apply two sets of optimization methodology on RC-resistant building frame to get a single best optimization design model. The results show that through the method proposed in this study we are able to establish the most cost-efficient seismic structure design model for a 10-story building. Also, the seismic rating of the structure is improved by a fix amount through the optimization of the design. Furthermore, future research can add more design features to the proposed model to make it more versatile in tackling complex engineering problems.
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