The application of an artificial neural networks (ANN) to predicting the bending strength of a laminated veneer lumber (LVL) manufactured under different conditions is considered. First, experimental studies were conducted, and then an ANN model was developed based on the experimental data obtained. LVL specimens of walnut wood, glued with urea-formaldehyde resins containing a chemically modified starch and nanocellulose, were obtained by pressing for different times. Experimental results for them showed that the direct effect of the press time, the square effect of the modified starch, and the joint effect of them had the highest statistical significance to the bending strength of LVL. The ANN model developed gave good predictions for the bending strength, well agreeing with experimental data.

中文翻译：

---

使用人工神经网络预测层压单板木材 (LVL) 的弯曲强度

考虑了人工神经网络 (ANN) 在预测不同条件下制造的层压单板木材 (LVL) 的弯曲强度中的应用。首先，进行了实验研究，然后根据获得的实验数据开发了 ANN 模型。核桃木的 LVL 标本，用含有化学改性淀粉和纳米纤维素的脲醛树脂胶合，通过不同时间的压制获得。他们的实验结果表明，压制时间的直接影响、变性淀粉的平方效应以及它们的联合效应对LVL的弯曲强度具有最高的统计显着性。开发的人工神经网络模型对弯曲强度给出了很好的预测，与实验数据非常吻合。