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A platform for research: civil engineering, architecture and urbanism
Estimation of Wall Deflection in Deep Excavation — Neural Network Approach
The observational method, proposed by Peck in 1967, is often used to assist in predicting the wall deflection in various stages of deep excavation. Here, a back-analysis using finite element method is usually carried out to "update" soil parameters so that the after-the-fact prediction of the response at the current stage of excavation matches the measured response such as wall deflection. However, it is time consuming and requires much effort and considerable engineering judgment. This paper presents an alternative using artificial neural network (ANN) to predict the wall deflection in the next stage of excavation based on the observed response in the prior stages.
Estimation of Wall Deflection in Deep Excavation — Neural Network Approach
The observational method, proposed by Peck in 1967, is often used to assist in predicting the wall deflection in various stages of deep excavation. Here, a back-analysis using finite element method is usually carried out to "update" soil parameters so that the after-the-fact prediction of the response at the current stage of excavation matches the measured response such as wall deflection. However, it is time consuming and requires much effort and considerable engineering judgment. This paper presents an alternative using artificial neural network (ANN) to predict the wall deflection in the next stage of excavation based on the observed response in the prior stages.
Estimation of Wall Deflection in Deep Excavation — Neural Network Approach
Hsiao, Evan C. L. (author) / Kung, Gordon T. C. (author) / Juang, C. Hsein (author) / Schuster, Matt (author)
GeoShanghai International Conference 2006 ; 2006 ; Shanghai, China
2006-05-15
Conference paper
Electronic Resource
English
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