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I. Flood, "Using Neural Networks to Simulate Poorly Understood Engineering Processes", in B. Kumar, (Editor), "Information Processing in Civil and Structural Engineering Design", Civil-Comp Press, Edinburgh, UK, pp 219-224, 1996. doi:10.4203/ccp.37.8.3

Abstract

The paper describes a method of modeling the dynamic behavior of continuous engineering processes, using artificial neural networks. The technique is applicable to situations where the differential equations governing the behavior of a system are nonlinear and poorly understood, such as is the case for frost-heave and thaw-settlement processes in soils. A means of modeling the unknown component of governing differential equations is first described. A method of discretizing the neural network models of these equations is then illustrated, and the way in which these networks can be used to simulate the behavior of a process is discussed. The proposed approach is proven to provide highly accurate results in a series of experiments simulating the nonlinear thermal behavior of translucent solid materials. The paper concludes with an identification of several on-going areas of further development and application of the proposed tool.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 37 INFORMATION PROCESSING IN CIVIL AND STRUCTURAL ENGINEERING DESIGN Edited by: B. Kumar Paper VIII.3 Using Neural Networks to Simulate Poorly Understood Engineering Processes I. Flood Department of Civil Engineering, University of Maryland, United States of America doi:10.4203/ccp.37.8.3 purchase the full-text of this paper Full Bibliographic Reference for this paper I. Flood, "Using Neural Networks to Simulate Poorly Understood Engineering Processes", in B. Kumar, (Editor), "Information Processing in Civil and Structural Engineering Design", Civil-Comp Press, Edinburgh, UK, pp 219-224, 1996. doi:10.4203/ccp.37.8.3 Abstract The paper describes a method of modeling the dynamic behavior of continuous engineering processes, using artificial neural networks. The technique is applicable to situations where the differential equations governing the behavior of a system are nonlinear and poorly understood, such as is the case for frost-heave and thaw-settlement processes in soils. A means of modeling the unknown component of governing differential equations is first described. A method of discretizing the neural network models of these equations is then illustrated, and the way in which these networks can be used to simulate the behavior of a process is discussed. The proposed approach is proven to provide highly accurate results in a series of experiments simulating the nonlinear thermal behavior of translucent solid materials. The paper concludes with an identification of several on-going areas of further development and application of the proposed tool. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £75 +P&P)
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