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首页> 外文期刊>International journal of steel structures >Rapid prediction of deflections in multi-span continuous composite bridges using neural networks
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Rapid prediction of deflections in multi-span continuous composite bridges using neural networks

机译：使用神经网络快速预测多跨连续组合桥的挠度

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摘要

This paper proposes closed form expressions for the rapid prediction of deflections in steel-concrete composite bridges of large number of spans subjected to service load. The proposed expressions take into account shear lag effect, flexibility of shear connectors and cracking in concrete slabs. Three separate neural networks have been developed for right exterior span, left exterior span and interior spans. The closed form expressions have been obtained from the neural networks developed in the study. The training, validating and testing data sets for the neural networks are generated using finite element software ABAQUS. The proposed expressions have been validated for number of bridges and the errors are found to be small for practical purposes. Sensitivity studies have been carried out using the proposed expressions to evaluate the suitability of input parameters. The use of the proposed expressions requires a computational effort that is fraction of that required for the finite element analysis, therefore, can be used for rapid prediction of deflection for everyday design.
机译：本文提出了封闭形式的表达式，用于快速预测大跨度钢-混凝土组合桥梁在使用荷载作用下的挠度。所提出的表达式考虑了剪力滞后效应，剪力连接件的挠性和混凝土板的裂缝。已经针对右外部跨度，左外部跨度和内部跨度开发了三个单独的神经网络。已从研究中开发的神经网络获得了封闭形式的表达式。使用有限元软件ABAQUS生成神经网络的训练，验证和测试数据集。所提出的表达式已针对桥梁数量进行了验证，发现误差很小，实用性强。使用提议的表达式进行了敏感性研究，以评估输入参数的适用性。使用建议的表达式需要的计算量只是有限元分析所需计算量的一部分，因此可以用于日常设计的挠度快速预测。

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