Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model
Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model
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DOI：
10.3390/app9122562
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发表时间：
2019-06-02
影响因子：
2.7
通讯作者：
Hariri-Ardebili, Mohammad Amin
中科院分区：
文献类型：
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作者：
Moradi, Mohammad Javad;Hariri-Ardebili, Mohammad Amin
以往的实验测试中包含了大量有用的信息，而这些信息在新的测试设置中往往被忽略。假设、材料、测试程序和测试目标的变化使得难以选择正确的模型来验证数值模型。来自不同实验的结果有时相互冲突，或者具有最小的相关性。此外，研究人员和工程师并不容易获得所有这些信息。因此，本文介绍了钢板和钢筋混凝土剪力墙的不同试验模型的综合研究结果。一个独特的库多达13个参数（力学性能和几何特征）影响的强度，刚度和位移比的剪力墙收集，包括其敏感性分析。其次，提出了一种基于人工神经网络的预测元模型。它能够预测任何期望的剪力墙的反应，具有良好的精度。该网络可以作为非线性数值模拟或昂贵的实验测试的替代方案。
There is a large amount of useful information from past experimental tests, which are usually ignored in test-setup for the new ones. Variation of assumptions, materials, test procedures, and test objectives make it difficult to choose the right model for validation of the numerical models. Results from different experiments are sometimes in conflict with each other, or have minimum correlation. Furthermore, not all these information are easily accessible for researchers and engineers. Therefore, this paper presents the results of a comprehensive study on different experimental models for steel plate and reinforced concrete shear walls. A unique library of up to 13 parameters (mechanical properties and geometric characteristics) affecting the strength, stiffness and drift ratio of the shear walls are gathered including their sensitivity analysis. Next, a predictive meta-model is developed based on artificial neural network. It is capable of forecasting the responses for any desired shear wall with good accuracy. The proposed network can be used to as an alternative to the nonlinear numerical simulations or expensive experimental test.