Development of Neural Network Machine Learning Models:Preliminary Design in Building Structure
| Autor: | Jing-Chi Jan, 詹君治 |
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| Rok vydání: | 2000 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 88 Computer programs are widely used to assist engineers in solving problems by shifting the burden of numerical computation to the machine. Furthermore, new methods and tools encourage civil engineers to use numerical computation in creative and imaginative ways. Despite the completely automatic structural design is presently not available; the efficiency of the conventional structural design is significantly improved by adopting some techniques of artificial intelligence (AI). Applying neural network computing, one of the artificial intelligence techniques, to structural engineering is currently an active subject in computer-aided design. Most of the previous researches concentrated on the back-propagation neural network (BPN) because BPN has a good generalization. However, the BPN sometime performs poor learning convergence when a large number of instances are used for a complicated problem, owing to its global optimization learning scheme. In addition, a long computational time in learning stage is another drawback for engineers to use BPN in structural design. The goal of this dissertation is to develop novel machine learning models to make structural design system more powerful, especially for preliminary design in building structural design. Based on the information flow in building structural design, two different kind of neural networks, integrated fuzzy neural network (IFN) and macro structure CMAC (MS_CMAC), are developed. (1) The IFN learning model combined an unsupervised fuzzy neural network (UFN) reasoning model with a supervised neural network as an assistant network. The UFN reasoning model adopts local information scheme to interpret a large number of instances for complicated problems within an acceptable computing time. Meanwhile, a self-organized learning is developed to refine the UFN reasoning. (2) The CMAC is a supervised learning model used mainly in control due to its rapid learning. The MS_CMAC is a tree-based structure of one-dimensional CMACs, where the ensemble is trained by the time inversion technique. The main feature of MS_CMAC is to decompose a multi-dimensional problem into a set of one-dimensional sub-problems so as to improve the learning convergence and prediction. For verifying the feasibility of IFN and MS_CMAC in structural design, the IFN learning model is utilized to model the initial design of building structure and steel beam design problems. The initial design is an experience-oriented problem, and the steel beam design is an iterative process under LRFD specification. Also, the MS_CMAC is employed to determine some design coefficients which are generally obtained by numerical approaches. The results indicate that the two neural networks are useful tools for engineers to solve structural design problems. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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