Abstract
This work is oriented to studying the learning capabilities of neural networks and their impact on the development of a method for the generation of spatial variation of peak ground accelerations PGAs. This spatial variation is based on a limited number of accelerograms registered in specific geotechnical zones, along with a map of soil periods on the Mexico City area. The continuous surfaces that resulted are compared with the most common methods of interpolation, aiming to evidence the substantial advantages of NNs on those of these methods. Based on the results presented, it can be argued that using multi-parameters approaches to define spatial interpolations of a variable as important as PGA, allows to make safer engineering decisions.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
