Abstract
The bamboo-wood composite container floor (BWCCF) plays an increasingly important role in the transportation area in recent years. However, the conventional mechanical testing methods are conducted in a time-consuming and resource-wasting way. Therefore, this study is aimed to provide a frugal and high-efficiency method to predict the concentrated load of BWCCF, by comparing models with two sets of parameters. First, three artificial neural network (ANN) models were developed by taking the characteristic parameters of the end face extracted by image processing as input and concentrated load as output. Then, the other three ANN models were presented by taking the vertical density profile (VDP) as input. Of the six models, the two ANN models constructed using all characteristic parameters of cross and vertical sections and all VDP parameters had the strongest generalization. The mean absolute percentage errors were determined as 3.393 and 6.196%, respectively, and the absolute percentage errors were all within 10.000%. The result indicates that the designed model has the potential to be a useful, reliable and effective tool for predicting concentrated load.
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This research was supported by National Natural Science Foundation of China (Project no. 31660174), Guangxi Innovation-Driven Development Special Fund Project of China (Project no. AA17204087-16).
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Liang, Y., Cheng, F., Jiang, Z. et al. Concentrated load simulation analysis of bamboo-wood composite container floor. Eur. J. Wood Prod. 79, 1183–1193 (2021). https://doi.org/10.1007/s00107-021-01726-x
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DOI: https://doi.org/10.1007/s00107-021-01726-x