Abstract
The bamboo–wood composite container floor (BWCCF) has been wildly utilized in transportation in recent years. However, most of the common approaches of mechanics detection are conducted in a time-consuming and resource wasting way. Therefore, this paper aims to provide a frugal and highly efficient method to predict the short-span shear stress, the modulus of rupture (MOR) and the modulus of elasticity (MOE) of the BWCCF. Artificial neural network (ANN) models were developed and support vector machine (SVM) models were constructed for comparative study by taking the characteristic parameters of image processing as input and the mechanical properties as output. The results show that the SVM models can output better values than the ANN models. In a prediction of the three mechanical properties by SVMs, the correlation coefficients (R) were determined as 0.899, 0.926, and 0.949, and the mean absolute percentage errors (MAPE) were obtained, 6.983%, 5.873%, and 4.474%, respectively. The performance measures show the strong generalization of the SVM models. The discoveries in this work provide new perspectives on the study of mechanical properties of the BWCCF combining machine learning and image processing.
Highlights
With the rapid development of international freight transport containerization, the demand for container floors, as necessary parts of containers, developed synchronously, but they were subsequently improved, from the solid wood floor made of hardwood from a single tree species to plywood floors of hardwoods
The short-span shear stress, modulus of elasticity (MOE) and modulus of rupture (MOR) of the bamboo–wood composite container floor (BWCCF) were tested and the test results served as the output of the Artificial neural network (ANN) and support vector machine (SVM) models
The characteristic parameters of the longitudinal section and the cross section of BWCCF were extracted by image processing, including a Fourier transform (S(r), S(θ)), Gabor transform, gray level co-occurrence matrix (GLCM), and wavelet transform, 36 characteristic parameters in total
Summary
With the rapid development of international freight transport containerization, the demand for container floors, as necessary parts of containers, developed synchronously, but they were subsequently improved, from the solid wood floor made of hardwood from a single tree species to plywood floors of hardwoods. The mechanical properties of bamboo were explored to assess its usage as a structural material in place of wood [1]. Comparisons of bamboo lamina with woods indicate that the average strength of bamboo laminae obtained under different loading conditions is better than softwoods and comparable with hardwoods [2]. A bamboo–wood composite is formed by gluing bamboo and wood in the same or different structural unit forms [3]. The bamboo–wood composite container floor (BWCCF) is widely used due to the advantages of abundance of resources, environmental characteristics, high mechanical properties, low cost, etc. It can be used as an ideal substitute for plywood floors of hardwood. As the main load-bearing part of container, the performance and quality of a BWCCF need to be assured, so it is necessary to effectively detect and control its mechanical properties
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