MS-CMAC NEURAL NETWORK LEARNING MODEL IN STRUCTURAL ENGINEERING

The present American institute of Steel Construction specifications use the alignment charts and approximate formulas conveniently to determine some coefficients in design, such as moment gradient coefficient C(b) for beams of I-shaped section and effective length factor K of columns. In these methods, the coefficients are unconservative when the boundary conditions are different from the development of specifications. The governing equations, numerical approaches, on the K and C(b) coefficients provide more accurate results. The approaches, however, are not readily available for structural engineers to use in design. Applying neural network computing toward structural engineering problems has received increasing interest, with particular emphasis placed on supervised neural networks. The cerebellar model articulation controller (CMAC), one of the supervised neural network learning models, is mostly used in the domain of control. In this work, we use a newly developed Macro Structure CMAC (MS-CMAC) neural network learning model to aid steel structure design. The topology of the novel learning model is constructed by a number of time inversion CMACs as a tree structure. The learning performance of the MS-CMAC is first compared with a stand-alone time inversion CMAC using one structural engineering example. That comparison indicates not only superior prediction but also fast learning propriety for the MS-CMAC neural network learning model. In addition, the MS-CMAC neural network learning model is applied to two steel design problems. It is shown that the MS-CMAC not only can learn structural design problems within a reasonable central processing unit time but also can estimate more accurate coefficients than that estimated through alignment charts and approximate formulas in American Institute of Steel Construction specifications.