Ladnykh Irene A, Ibadov Nabi, Anysz Hubert
Faculty of Civil Engineering, Warsaw University of Technology, 00-661 Warsaw, Poland.
Materials (Basel). 2024 Jul 2;17(13):3246. doi: 10.3390/ma17133246.
This article explores the possibility of predicting the compliance coefficients for composite shear keys of built-up timber beams using artificial neural networks. The compliance coefficients determine the stresses and deflections of built-up timber beams. The article analyzes current theoretical methods for designing wooden built-up timber beams with shear keys and possible ways of applying them in modern construction. One of the design methods, based on the use of the compliance coefficients, is also discussed in detail. The novelty of this research is that the authors of the article collected, analysed, and combined data on the experimental values of the compliance coefficient for composite shear keys of built-up timber beams obtained by different researchers and published in other studies. For the first time, the authors of this article generated a table of input and output data for predicting compliance coefficients based on the analysis of the literature and collected data by the authors. As a result of this research, the article's authors proposed an artificial neural network (ANN) architecture and determined the mean absolute percentage error for the compliance coefficients k and k, which are equal to 0.054% and 0.052%, respectively. The proposed architecture can be used for practical application in designing built-up timber beams using various composite shear keys.
本文探讨了使用人工神经网络预测组合木梁组合抗剪键柔度系数的可能性。柔度系数决定了组合木梁的应力和挠度。本文分析了当前设计带抗剪键组合木梁的理论方法以及在现代建筑中应用这些方法的可能途径。还详细讨论了基于柔度系数使用的一种设计方法。本研究的新颖之处在于，文章作者收集、分析并整合了不同研究人员在其他研究中发表的关于组合木梁组合抗剪键柔度系数实验值的数据。本文作者首次基于文献分析和自身收集的数据生成了用于预测柔度系数的输入和输出数据表。作为这项研究的结果，文章作者提出了一种人工神经网络（ANN）架构，并确定了柔度系数k和k的平均绝对百分比误差，分别为0.054%和0.052%。所提出的架构可用于实际应用中设计使用各种组合抗剪键的组合木梁。
