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| An improved BP algorithmand its application to parametric identification in deep foundation excavation |
| Revised:March 02, 1999 |
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| DOI：10.7511/jslx20012027 |
| KeyWord:deep foundation excavation,parametric identification,maximum likelihood estimation,finite element,ANN,BP algorithm. |
| TANG Yong-li |
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| Abstract: |
| A novel method based on ANN BP algorithm to perform parametric identification in deep foundation excavation is proposed in the paper. Taking in situ measurements (displacements, pore pressures, stresses etc.) as network input and parameters to be identified as network output, the network is trained with the samples obtained from FEM computation. With the introduction of maximum likelihood approach, the errors of both the samples and the network input (in situ measurements) can be considered in the identification procedure, and the reliability of the identified parameters can also be obtained. To make the BP learning more efficient, a family of algorithms that optimize the learning rate factor and momentum factor dynamically are also studied in the paper. The numerical results provided in the paper illustrate that the computational effort for the learning process can usually be reduced by more than 10 times as compared with the conventinoal BP algorithm. |
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