DocumentCode
3175326
Title
Research for Settlement Prediction on the Based of Neural Network and ADINA
Author
Deguang, Meng ; Tianzhi, Zhu ; Bingxin, Li ; Yanying, Dong
Author_Institution
Dept of Civil Eng., Hebei Normal Univ. of Sci. & Technol. (HBNUST), Qinhuangdao, China
Volume
3
fYear
2009
fDate
25-27 Dec. 2009
Firstpage
123
Lastpage
126
Abstract
It is important geotechnical problem to consolidation of soft soil foundation. Three-dimensional finite element model is established on the based of Biot consolidation theory, the model is loaded and calculated on the based of considering lateral deformation and spatial seepage, elastic-plasticity character of soil, construction stage loading progress etc. ANN has the advantages of self-organizing, adaptive identifying, self-studying and being tolerant towards errors, and is trained and tested by the data sample to back analysis parameter of soft soil foundation, and then the settlement prediction model is established by finite element method, prediction data compare with measured data, the result indicate that the model can give a precise forecast and is rationality. The method can provide the reference for the construction.
Keywords
finite element analysis; foundations; geotechnical engineering; neural nets; soil; structural engineering computing; ADINA; Biot consolidation theory; construction stage loading progress; finite element model; lateral deformation; neural network; settlement prediction model; settlement prediction research; soft soil foundation; soil elastic-plasticity characteristic; spatial seepage; Artificial neural networks; Computer networks; Differential equations; Finite element methods; Levee; Neural networks; Partial differential equations; Predictive models; Soil; Stress; back analysis; embankment; finite element method; neural network; settlement prediction;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on
Conference_Location
Chongqing
Print_ISBN
978-0-7695-3930-0
Electronic_ISBN
978-1-4244-5423-5
Type
conf
DOI
10.1109/IFCSTA.2009.269
Filename
5384760
Link To Document