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首页> 外文期刊>Arabian Journal for Science and Engineering >Does a Single ANN Properly Predict Pushover Response Parameters of Low-, Medium- and High-Rise Infilled RC Frames?
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Does a Single ANN Properly Predict Pushover Response Parameters of Low-, Medium- and High-Rise Infilled RC Frames?

机译：单个ANN是否可以正确预测低，中和高层填充RC框架的下垂响应参数？

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摘要

Artificial neural networks, ANNs, can predict the behavior of systems that have common main features. When the problem under consideration contains groups that involve different main features, different ANNs are needed to predict the behavior of each group separately. In this paper, the efficiency of a single ANN to predict the lateral behavior of two-span structures representing a mix of low-, medium- and high-rise buildings in Egypt was investigated. All buildings were first analyzed using nonlinear pushover analysis to obtain their capacity curves, failure loads and displacements. Obtained data were used for training different ANN models. The results indicated the efficiency of a single ANN to predict the behavior of a mix of all buildings under investigation with a confidence level of 99%. The successful network was further utilized to obtain another set of data that were merged with the original data and used to develop a design neural network. The obtained network showed a very good capability to predict design variables which can be a good tool for engineering practitioners.
机译：人工神经网络（ANN）可以预测具有共同主要特征的系统的行为。当所考虑的问题包含涉及不同主要特征的组时，需要使用不同的ANN分别预测每个组的行为。在本文中，研究了单个ANN预测代表埃及低，中和高层建筑混合的两跨结构的侧向行为的效率。首先使用非线性推覆分析法对所有建筑物进行分析，以获得其容量曲线，破坏荷载和位移。获得的数据用于训练不同的ANN模型。结果表明，单个ANN能够以99％的置信度预测被调查的所有建筑物的混合行为。成功的网络被进一步用来获取与原始数据合并的另一组数据，并用于开发设计神经网络。所获得的网络显示出非常好的预测设计变量的能力，这对于工程从业人员而言可能是一个很好的工具。

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