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Deep neural network based approach for seismic fragility assessment of an ordinary standard bridge | |
Author | Devkota, Ajit |
Call Number | AIT Thesis no.ST-19-08 |
Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Structural Engineering, School of Engineering and Technology |
Publisher | Asian Institute of Technology |
Series Statement | Thesis ; no. ST-19-08 |
Abstract | Fragility curves are widely used in seismic risk assessment of bridges. Using the IDA method for analytical fragility evaluation requires performing a series of NLTHA using a set of GMs, each of which is scaled to several levels of intensity to force the structure from elastic to inelastic to failure. Since, such an approach is computationally demanding and very time consuming, a DNN based approach is used that learns from data obtained though IDA analysis. The DNN is used to predict the Sa(T1) values for each limit states considered for all ground motions; from which the log-normal parameters to generate the fragility curves are obtained. The DNN methodology can then be used as a quick alternative tool to predict the limit-state fragilities for the bridge structure. |
Year | 2019 |
Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. ST-19-08 |
Type | Thesis |
School | School of Engineering and Technology |
Department | Department of Civil and Infrastucture Engineering (DCIE) |
Academic Program/FoS | Structural Engineering (STE) /Former Name = Structural Engineering and Construction (ST) |
Chairperson(s) | Punchet Thammarak;Anwar, Naveed; |
Examination Committee(s) | Pennung Warnitchai;Thanakorn Pheeraphan; |
Scholarship Donor(s) | AIT Fellowship; |
Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2019 |