|
|||||
|
|
Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model |
|
| Authors: | Yong-Seong Kim Byung-Tak Kim |
| Institution: | aDisaster Management Team, National Emergency Management Agency, 1310 Central Government Complex, 55 Sejong-ro, Jongro-gu, Seoul 110-760, Republic of Korea bCivil Design-Build Team, GS Engineering & Construction, Namdaemun-ro 5-ga, Joong-gu, Seoul 100-722, Republic of Korea |
| Abstract: | A neural network model has been developed for the prediction of relative crest settlement (RCS) of concrete-faced rockfill dams (CFRDs) using 30 databases of field data from seven countries (of which 21 were used for training and 9 for testing). The settlement values predicted using the optimum artificial neural network (ANN) model are in good agreement with these field data. A database prepared from reported crest settlement values of CFRDs after construction was used to train the ANN model to predict the RCS. It is demonstrated here that the model is capable of predicting accurately the relative crest settlement of CFRDs and is potentially applicable for general usage with knowledge of the three basic properties of a dam (void ratio, e; height, H; and vertical deformation modulus, EV). The performance of the new ANN model is compared with that of conventional methods based on the Clements theory and also with that of a proposed equation derived from the field data. The comparison indicates that the ANN model has strong potential and offers better performance than conventional methods when used as a quick interpolation and extrapolation tool. The conventional calculation model was proposed based on the fixed connection weights and bias factors of the optimum ANN structure. This method can support the dam engineer in predicting the relative crest settlement of a CFRD after impounding. |
| Keywords: | Artificial neural network Concrete-faced rockfill dam Relative crest settlement Conventional calculation methods Regression analysis |
| 本文献已被 ScienceDirect 等数据库收录！ | |