The neural shell: A neural network simulation tool

Ahalt, Stanley C.; Chen, Prakoon; Chou, C.-T.; Kuttuva, Suresh; Little, Thomas E.

doi:10.1016/0952-1976(92)90002-2

Cited by 5 publications

(1 citation statement)

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“…Secondly, the bridge response obtained by conventional method for dynamic coupling analysis of the vehicle-bridge system cannot be expressed explicitly, which is not convenient for engineering application. To the best of the authors' knowledge, the backpropagation (BP) neural network is usually used in the artificial intelligence field, which simulates the mode of human brain receiving and processing information, adopts a distributed processing mechanism, composes a complex network through different connection ways of basic neurons, and forms a powerful machine-learning algorithm for functions implementation such as classification, clustering, fitting, prediction, and compression [10][11][12][13][14][15][16]. BP neural network is a self-adaption nonlinear dynamic system which is composed of substantial neurons, each of which has a relatively simple structure and function, but the system consisting of a large number of neurons after combination will have a very sophisticated behavior.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Coupling Analysis of Vehicle‐Bridge System for Long‐Span Suspension Bridge Based on Backpropagation Neural Network Method

Luo

Zheng

Yuan

et al. 2020

Advances in Civil Engineering

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As the suspension bridge structures become more flexible and the forms of the vehicle load become more diverse, the dynamic coupling problem of the vehicle-bridge system has become gradually prominent in long-span suspension bridges, resulting in an increase in accuracy and efficiency requirements for dynamic coupling analysis of the vehicle-bridge system. Conventional method such as finite element method (FEM) for dynamic coupling analysis of vehicle-bridge system often requires separate iteration of vehicle system and bridge system, and the contact and coupling interactions between them are used as the link for convergence inspection, which is too computationally intensive and time-consuming. In addition, the dynamic response of the vehicle-bridge coupling system obtained by FEM cannot be expressed explicitly, which is not convenient for engineering application. To overcome these drawbacks mentioned above, the backpropagation (BP) neural network technology is proposed to the dynamic coupling analysis of the vehicle-bridge system of long-span suspension bridges. Firstly, the BP neural network was used to approximate the dynamic response of the suspension bridge in the vehicle-bridge coupling system, and the complex finite element analysis results were thus explicitly displayed in the form of a mathematical analytical expression. And then the dynamic response of the suspension bridge under vehicle load was obtained by using a dynamic explicit analysis method. It is shown through a numerical example that, compared with FEM, the proposed method is much more economical to achieve reasonable accuracy when dealing with the dynamic coupling problem of the vehicle-bridge system. Finally, an engineering case involving a detailed finite element model of a long-span suspension bridge with a main span of 1688 m is presented to demonstrate the applicability and efficiency under the premise of ensuring the approximation accuracy, which indicates that the proposed method provides a new approach for dynamic coupling analysis of the vehicle-bridge system of long-span suspension bridges.

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