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SUMMARY:ENAC Seminar Series by Dr S. Moghtadernejad
DTSTART:20210203T090000
DTEND:20210203T094500
DTSTAMP:20260506T224323Z
UID:c0ae003c453bbd34881f2af14598355cafaebb665e48936850aaa270
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Saviz Moghtadernejad\n09:00 – 09:45 – Dr Saviz Moghtade
 rnejad\nPostdoctoral fellow at ETH Zürich\, Switzerland\n\nMulti-criteria
  and data-driven decision-making frameworks for the development and manage
 ment of sustainable and resilient infrastructure\n\nExtreme climate events
  and conditions\, such as floods and heavy storms\, whose frequency of occ
 urrence and severity is increasing due to climate change\, are threatening
  the life-cycle performance of infrastructures. Moreover\, as the construc
 tion industry is playing a significant role in the current climate change 
 crisis\, there is an urgent need for minimizing such negative environmenta
 l impacts and adopting new strategies to develop and manage resilient and 
 high-performance infrastructure.  \nAs the first step in the development 
 of high-performance and resilient infrastructure\,  a systematic and mult
 i-objective design paradigm has been introduced and applied to building en
 closures to increase the overall building performance. In this design para
 digm\, Fuzzy multi-criteria decision-making methods and machine learning t
 ools such as artificial neural networks are incorporated to model and asse
 ss the individual and collective importance of the objectives. In the seco
 nd step\, to manage and assess the condition states of infrastructure\, an
  automated structural health-monitoring tool was developed and tested on t
 he old Champlain Bridge in Montreal. Application of these methods in real-
 time monitoring of infrastructure results in significant savings in interv
 ention costs caused by the lack of adequate and timely inspections.\nMoreo
 ver\, due to the fundamental role of infrastructure in the functioning of 
 societies\, the speed and costs of restoring them following extreme climat
 e events are of utmost importance. Hence\, a novel approach to identify op
 timal restoration programs was introduced that reduce the time between the
  occurrence of the extreme event and the moment the restoration work start
 s\, using a double-staged optimization model. The effectiveness of the mod
 el was tested in a real-world case study after an extreme flood event in C
 hur\, Switzerland.\n\n\nShort bio:\nSaviz Moghtadernejad is a postdoctoral
  fellow in the Institute for Construction and Infrastructure Management\, 
 in the Department of Civil\, Environmental and Geomatic Engineering\, at t
 he Swiss Federal Institute of Technology in Zürich (ETHZ)\, Switzerland. 
 At ETHZ\, she works on the determination of optimal restoration programs\,
  to improve asset management schemes for authorities and infrastructure ma
 nagers and allow for more resilient multi-modal transport infrastructures.
  Moreover\, she works on the application of data-driven methods to estimat
 e deterioration curves of railway supporting structures and to provide sol
 utions for tackling measurement errors and discrepancies in real-world tim
 e history inspection data using machine learning algorithms and Markov mod
 els. Dr. Moghtadernejad received her PhD in Civil Engineering from McGill 
 University\, Canada\, where she developed an integrated and systematic par
 adigm to maximize the resilience and sustainability of buildings. The appl
 ication of artificial intelligence and expert systems such as Fuzzy integr
 als and Neural Networks has been incorporated in her research to provide a
  reliable performance assessment and decision-making tool for choosing eli
 te design alternatives.\n 
LOCATION:Zoom https://epfl.zoom.us/j/86779987109
STATUS:CONFIRMED
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