首页|Study Findings from Shahid Beheshti University Advance Knowledge in Machine Lear ning (Optimizing the geometry of hunchbacked block-type gravity quay walls using non-linear dynamic analyses and supervised machine learning technique)
Study Findings from Shahid Beheshti University Advance Knowledge in Machine Lear ning (Optimizing the geometry of hunchbacked block-type gravity quay walls using non-linear dynamic analyses and supervised machine learning technique)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on artificial intelligence is now available. According to news originating fromTehran, Iran, by NewsRx co rrespondents, research stated, “In the present study, the seismic behavior of hunchbacked block-type gravity quay walls rested on non-liquefiable dense seabed s oil layer is investigated,and the optimal geometries for these wall types are p roposed by performing non-linear time history dynamicanalyses using Lagrangian explicit finite difference method. For this purpose, first, a reference numerical model of the hunchbacked quay wall is developed, and its seismic response is v alidated against thewell-documented physical model tests.”
NETL
NSTL
