High-rise Buildings Based On Artificial Neural Network Structure Selection

In the early stage of the design process, the design of tall building is a complex work. It needs various knowledge and professional experience for the structural design. A way concerned about the choice of structural styles is put forward based on artificial neural network in this paper. The qualities of the ANN, high- nonlinear ,high- permissibility of error and high- robustness, self- adaptability, online work, and so on, are adequately used in the research. From the research we know that the method based on ANN can solve the problem on choice of structural styles.First of all, the main characters of tall building are analyzed, and the main factors that can dominate choice of structural styles are picked out, and the mathematics model of the choice of structural styles based on ANN is established. Then, the question on how to choose the numbers of the hidden-layer and neuron is discussed. Two kinds of ANN, BP neural network and radial basis function neural network, are respectively used in the paper. Three kinds of BP algorithm, traditional BP algorithm, improved self-adaptive learning algorithm with momentum, and L-M algorithm, are discussed and compared in the course of analysis. It is concluded that traditional BP algorithm is not suitable for the complex data structure in the field of civil engineering, and improved L-M algorithm whose running-speed is 102~ ~ times faster than traditional BP algorithm, can deal with the problem on choice of structural styles very well. RBFNN is also introduced and used in this paper, the application of which has not been published in the field of civil engineering yet. And from the research, it is concluded that RBFN7N whose running-speed is l0~-10~ times faster than traditional BP algorithm is more efficient than BP neural network. By using RBFNN can solve the problem on choice of structural styles efficiently and correctly.