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SUMMARY:Exploring the usefulness of available experimental data: a machine
  learning-focused approach to predicting seismic capacity
DTSTART:20180907T150000
DTEND:20180907T160000
DTSTAMP:20260511T085237Z
UID:17918880f7ea7d679c770eb59909f8b6451315fa0625fae1dbee8b62
CATEGORIES:Conferences - Seminars
DESCRIPTION:Stephanie G. Paal\, Ph.D. Zachry Department of Civil Engineeri
 ng Texas A&M University\, College Station\, Texas\, USA\nA fundamental und
 erstanding of the knowledge that can be gained from data-driven models wit
 hin the realm of civil engineering will have a translational impact on the
  analysis\, design\, maintenance\, and construction of civil infrastructur
 e. With models firmly grounded in real-world data\, material and structura
 l properties and behavior can be precisely predicted from each other witho
 ut computationally expensive analytical or empirical evaluations. Moreover
 \, by integrating data-driven and physics-based models\, transformative in
 sights into the behavior of materials and structures their relation to one
  another can be discovered\, actuating next-generation modeling approaches
 \, experimental methods\, empirical relations\, designs\, and construction
  methods. With the recent rapid development of machine learning (ML) techn
 iques\, various ML based approaches have been successfully applied in the 
 general realm of structural engineering and have been validated as effecti
 ve in reproducing accurate and robust results from experimental tests. In 
 this presentation\, a general framework for data-driven understanding of s
 tructural performance will be demonstrated and two novel machine learning 
 models aimed at predicting the shear capacity and overall performance of r
 einforced concrete (RC) columns under reversed cyclic loading will be intr
 oduced. An exploration of various existing machine learning models and the
 ir application and suitability within the realm of civil engineering (CE) 
 will be discussed\, demonstrating the significant potential impact machine
  learning approaches could have on conventional CE methods and practices.
   Furthermore\, a comprehensive comparison of the novel machine learning 
 approaches with traditional empirical and modeling approaches popularly us
 ed in the field.
LOCATION:GC G1 515 https://plan.epfl.ch/?room=GCG1515
STATUS:CONFIRMED
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