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	<title>DS/AI &#8211; TERAZAWA Lab.</title>
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	<title>DS/AI &#8211; TERAZAWA Lab.</title>
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		<title>DEVELOPMENT OF A MACHINE-LEARNING-BASED WEB APPLICATION TOOL FOR PARAMETRIC DAMPER DESIGN</title>
		<link>https://www.zawawatech.arch.ens.titech.ac.jp/development-of-a-machine-learning-based-web-application-tool-for-parametric-damper-design/</link>
		
		<dc:creator><![CDATA[ZawawatechEditor]]></dc:creator>
		<pubDate>Tue, 13 Aug 2024 09:16:22 +0000</pubDate>
				<category><![CDATA[Research]]></category>
		<category><![CDATA[DS/AI]]></category>
		<category><![CDATA[GRSA]]></category>
		<category><![CDATA[Seismic optimization]]></category>
		<guid isPermaLink="false">https://www.zawawatech.arch.ens.titech.ac.jp/?p=798</guid>

					<description><![CDATA[<p><img src="https://www.zawawatech.arch.ens.titech.ac.jp/wp-content/uploads/2024/08/34_NSAIDD-1024x548.png" class="webfeedsFeaturedVisual" /></p>　日鉄エンジニアリングとの共同で、免・制振部材の膨大な配置ケースの地震応答を人工知能により数分以内に算出し、設計者が基本計画時の設計条件（耐震性能・コスト）を満たすおおよその部材配置を検討するためのWebアプリケーション [&#8230;]]]></description>
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<p>　日鉄エンジニアリングとの共同で、免・制振部材の膨大な配置ケースの地震応答を人工知能により数分以内に算出し、設計者が基本計画時の設計条件（耐震性能・コスト）を満たすおおよその部材配置を検討するためのWebアプリケーションNS-AIDD （エヌエス-エイド、AI-based Damper Design System）を開発いたしました。私が発明者と開発者の1人です。 ここ数年にわたって、耐震設計のパラメトリックデザインとも呼べる「対話的な耐震最適化手法」を検討しており、NS-AIDDはこれからの耐震設計ツールの一方向性を示唆するために開発しました。 長いこと悩んで作っていましたが、Done is better than perfectということで、とにかく世に出してみることにしました。結果的にですが、NS-AIDDは、耐震設計の領域において、人工知能を実用レベルで活用した初めての事例であり、これから様々な議論の的として使っていただき、皆様と暖かく育てていければと思います。このページの動画は2024年7月にイタリアで開催された世界地震工学会議での発表スライドです。また，日本語では<a rel="noreferrer noopener" href="https://t2r2.star.titech.ac.jp/rrws/file/CTT100884230/ATD100000413/" target="_blank"><strong>2022年10月号の鉄構技術</strong></a>に特集記事を執筆しましたのでご笑覧いただければ幸いです。最後に開発にあたりご助言いただいたAI研究会の皆様に感謝申し上げます。<strong><a rel="noreferrer noopener" href="https://www.researchgate.net/publication/382068056_DEVELOPMENT_OF_A_MACHINE-LEARNING-BASED_WEB_APPLICATION_TOOL_FOR_PARAMETRIC_DAMPER_DESIGN" data-type="URL" data-id="https://www.researchgate.net/publication/382068056_DEVELOPMENT_OF_A_MACHINE-LEARNING-BASED_WEB_APPLICATION_TOOL_FOR_PARAMETRIC_DAMPER_DESIGN" target="_blank">会議論文はこちら</a></strong></p>



<p>According to the common use of seismic energy-dissipation devices (i.e. dampers), in Japan, practical computational optimal damper design methods satisfying multiple design constraints are attracting attention, and are becoming to be actually used. Nevertheless, while these methods produce an optimal design solution exactly following the optimization problem, another (not optimal) design option is often adopted in design practice because it is still a challenge to include highly indeterminate engineering judgments into the procedure. On the other hand, the manual trial-and-error study with numerical simulation can proceed with the damper design while incorporating the engineering judgments, but perhaps miss better design options in the limited design period. Therefore, the Tokyo Institute of Technology and Nippon Steel Engineering developed a web application tool called NS-AIDD ® (AI-based Damper Design System), where machine-learning models quickly evaluates seismic responses of a huge number of damper design options, and the results are organized on the clear graphical user interface, for the efficient seismic design of supplementary damped structures and seismically isolated structures while combining the digital tools and engineering judgment. This paper presents the motivation in Japanese design practice, an overview of the tool, and the limitations of the machine-learning models. <a rel="noreferrer noopener" href="https://www.researchgate.net/publication/382068056_DEVELOPMENT_OF_A_MACHINE-LEARNING-BASED_WEB_APPLICATION_TOOL_FOR_PARAMETRIC_DAMPER_DESIGN" data-type="URL" data-id="https://www.researchgate.net/publication/382068056_DEVELOPMENT_OF_A_MACHINE-LEARNING-BASED_WEB_APPLICATION_TOOL_FOR_PARAMETRIC_DAMPER_DESIGN" target="_blank"><strong>The paper is here.</strong></a></p>
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		<title>First mode damping ratio oriented optimal design procedure for damped outrigger systems with additional linear viscous dampers</title>
		<link>https://www.zawawatech.arch.ens.titech.ac.jp/first-mode-damping-ratio-oriented-optimal-design-procedure-for-damped-outrigger-systems-with-additional-linear-viscous-dampers/</link>
		
		<dc:creator><![CDATA[ZawawatechEditor]]></dc:creator>
		<pubDate>Mon, 01 Nov 2021 06:39:00 +0000</pubDate>
				<category><![CDATA[Research]]></category>
		<category><![CDATA[DS/AI]]></category>
		<category><![CDATA[Tall building]]></category>
		<guid isPermaLink="false">https://www.zawawatech.arch.ens.titech.ac.jp/?p=701</guid>

					<description><![CDATA[<p><img src="https://www.zawawatech.arch.ens.titech.ac.jp/wp-content/uploads/2024/08/16_asai2-1024x729.jpg" class="webfeedsFeaturedVisual" /></p>　本研究は 一次モード減衰比に着目した線形粘性ダンパー付多層アウトリガー構造の最適設計法を提案しました。本研究は超高層建物のパッシブ応答制御に関する研究シリーズの一つであり，骨組モデルを用いた厳密な減衰性能評価から我々が [&#8230;]]]></description>
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<p>　本研究は 一次モード減衰比に着目した線形粘性ダンパー付多層アウトリガー構造の最適設計法を提案しました。本研究は超高層建物のパッシブ応答制御に関する研究シリーズの一つであり，骨組モデルを用いた厳密な減衰性能評価から我々が推奨する同構造の一次モード減衰比最大化の有効性を示し，さらにその最適設計のための具体的なツール群を整備しています。併せてAI(機械学習)を利用した<a href="https://www.zawawatech.arch.ens.titech.ac.jp/software/" data-type="page" data-id="73"><strong>最適設計ツールのデモアプリ</strong></a>も公開しているので遊んでみていただければ幸いです。私の星付き論文です。<a rel="noreferrer noopener" href="https://doi.org/10.1016/j.engstruct.2021.113229" data-type="URL" data-id="https://doi.org/10.1016/j.engstruct.2021.113229" target="_blank"><strong>論文はこちら</strong></a></p>



<p>The damped outrigger system is in widespread use as a damping modification system for tall buildings that provides high additional damping in addition to the bending back effect against the core. However, while the enhanced seismic performance of damped outrigger systems was confirmed in previous studies all over the world, a general-purpose optimal design method focusing on modal damping ratios has not been established yet. This paper proposes an optimal damper design kit composed of a first mode damping ratio oriented design policy, simple equations of optimal damper-connection stiffness ratio to maximize first mode damping ratio, a machine learning model to estimate first mode natural period and damping ratio. The tenability of the first mode damping ratio-oriented design policy was confirmed by performing complex modal analyses on single to quad damped outrigger systems incorporating linear viscous dampers and assigning realistic stiffness values to the outrigger trusses. The simple design equations of optimal damper-connection stiffness ratio and the machine learning model for first mode characteristics were developed based on a large number of analytical results. The proposed optimal design kit has been made available as a web application-based design tool.</p>
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