Tunneling-induced ground movement and building damage prediction using hybrid artificial neural networks
The construction of tunnels in urban areas may cause ground displacement which distort and damage overlying buildings and services. Hence, it is a major concern to estimate tunneling-induced ground movements as well as to assess the building damage. Artificial neural networks (ANN), as flexible non-...
Saved in:
Main Author: | Hajihassani, Mohsen |
---|---|
Format: | Thesis |
Language: | English |
Published: |
2013
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/37932/1/MohsenHajihassaniPFKA2013.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Tunnel induced ground movement
by: Ismail, Nursyakirah
Published: (2021) -
Controlling tunnel induced ground surface and pile movements using micropiles
by: Sohaei, Houman
Published: (2017) -
Multistage artificial neural network in structural damage detection
by: Goh, Lyn Dee
Published: (2015) -
Vibration based damage detection using artificial neural network
by: Low, Tian Hock
Published: (2010) -
Seismic damage identification based on integrated artificial neural networks and wavelet transforms
by: Vafaei, Mohammadreza
Published: (2013)