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Abstract：
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 kw and ki, 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.
摘要：
本文探讨了使用人工神经网络预测组合木梁的复合剪切键的柔度系数的可能性。柔量系数决定了已建成的木梁的应力和挠度。本文分析了当前设计带有剪切键的木质组合木梁的理论方法，以及将其应用于现代建筑的可能方法。设计方法之一，基于顺应性系数的使用，还详细讨论了。这项研究的新颖之处在于，文章的作者收集，分析,以及由不同研究人员获得并在其他研究中发表的关于组合木梁的复合剪切键的柔度系数实验值的组合数据。第一次,本文作者根据文献分析和作者收集的数据，生成了用于预测遵从系数的输入和输出数据表。作为这项研究的结果，文章的作者提出了一种人工神经网络（ANN）架构，并确定了顺从系数kw和ki的平均绝对百分比误差，等于0.054%和0.052%，分别。所提出的体系结构可用于使用各种复合剪切键设计组合木梁的实际应用。
