BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:New configuration of truss structures through new numerical modeli
 ng and optimization method
DTSTART:20190625T103000
DTEND:20190625T113000
DTSTAMP:20260405T164526Z
UID:f36349d0826b3b59d511e7f1837dfdef0c80d350ac0ca5b462af7454
CATEGORIES:Conferences - Seminars
DESCRIPTION:Kazuki Hayashi | Department of Architecture and Architectural 
 Engineering | Kyoto University\, Japan\nDeveloping methods to obtain optim
 al cross-section\, topology\, and geometry of trusses is an established fi
 eld of research. However\, due to nonlinearity and multi-objectivity of st
 ructural requirements\, it is still difficult to obtain desirable solution
 s. This presentation comprises two parts with respect to core methods hand
 ling the difficulty. In the first part\, based on force density method\, a
  new method of simultaneous optimization of geometry and topology is prese
 nted for trusses. The difficulties due to existence of melting nodes are s
 uccessfully avoided by considering force density\, which is the ratio of a
 xial force to the member length\, as design variable. This method does not
  need constraints on nodal locations to avoid coalescent nodes\, and enabl
 es to generate optimal solutions with a variety in topology and geometry. 
 In the second part\, several machine learning methods\, such as clustering
  analysis and q-learning\, are integrated into the optimization process. S
 ince machine learning is able to extract specific feature of the data stru
 cture\, it has potential to enhance the efficacy of optimization\, despite
  the nonlinearity of the optimization problem.\n\nKazuki Hayashi is curren
 tly a doctoral student at Kyoto University. He obtained his Masters of Eng
 ineering at Kyoto University in 2018. During his master program\, he studi
 ed in the Building Technology discipline group at the Massachusetts Instit
 ute of Technology (MIT) and worked in the research center of MakMax group\
 , a company specializing in membrane structures. His current research agen
 da is to develop a novel design process that allows close collaboration be
 tween architects and structural engineers\, by means of machine learning a
 nd structural optimization. For more details about his projects\, visit: h
 ayashikazuki.net
LOCATION:GC G1 515 https://plan.epfl.ch/?room=GCG1515
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR