Development of Artificial Neural Network Based Model for Bridge Extended Pile-Shafts Design
|關鍵字:||橋梁延伸樁桿件;性能目標;性能目標;Extended pile-shafts;Performance-based objective;Artificial neural network(ANN).|
A common type of bridge foundation uses the so-called“extended pile-shafts”, where the circular column is continued below the ground level. Under the design level earthquake, however, extended pile-shafts for bridges may be expected to experience some level of damage below the ground level. A design procedure that incorporates soil properties into the process was developed by Song et al on 2006. The seismic design of extended pile-shafts requires a careful consideration of all the details, so the procedure was complicated and experience oriented. Artificial neural network (ANN), one of well-developed models in Artificial Intelligence (AI), is a learning-capability computing model and being widely used in different areas. The aim of this work is to develop an ANN-based model for bridge extended pile-shafts design, to simulate the human experts to complete the entire design process. Three ANN sub-models were established. One for aided design and the other two are intended to test and assess the results. First, a case base about one million cases was created according to different design parameters using Matlab, and the cases were divided into training and test cases. The training cases were then randomly divided into three to five groups to test the learning performance of these sub-ANN models. The training results revealed the feasibility of these models. Meanwhile, validation results revealed that the coefficients of determination(R2) were more than 0.9 for these models in training and testing. Finally, a complete design case was employed to test the feasibility and performance of the developed ANN-based bridge extended pile-shafts design model. The results confirmed that the system is feasible and the results are correct and engineering acceptable.
|Appears in Collections:||Thesis|
Files in This Item: