Hybrid 1D-CNN and attention-based Bi-GRU neural networks for predicting moisture content of sand gravel using NIR spectroscopy

卷积神经网络 含水量 人工神经网络 近红外光谱 人工智能 水分 模式识别(心理学) 计算机科学 校准 土壤科学 遥感 环境科学 地质学 材料科学 数学 岩土工程 复合材料 光学 物理 统计
作者
Quan Yuan,Jiajun Wang,Mingwei Zheng,Xiaoling Wang
出处
期刊:Construction and Building Materials [Elsevier BV]
卷期号:350: 128799-128799 被引量:27
标识
DOI:10.1016/j.conbuildmat.2022.128799
摘要

A non-destructive and rapid moisture content detection method of sand gravel material is required in loose material dams. The near-infrared (NIR) spectrum of sand materials is closely related to its moisture content. Recently, there is a growing need for fully using spectral information when establishing calibration models for sand gravel moisture content detection. To address these issues, a hybrid one dimensional-convolutional neural network (1D-CNN) and attention-based bidirectional gated recurrent unit (Bi-GRU) neural network was proposed to detect sand gravel moisture content with NIR spectrum. Two learners, namely, 1D-CNN and Bi-GRU, were constructed to extract local abstract information and sequence position information from the spectrum, respectively. In the 1D-CNN learner, multiple kernels CNN layers and one dimensional-separable convolution layers were conjunct to improve model accuracy and reduce network parameters. In the Bi-GRU learner, a multi-head self-attention mechanism was appended to evaluate the weights of the output features extracted by Bi-GRU layers. The proposed model achieved the best prediction results in LUCAS dataset (R2 greater than 0.75, RPD greater than 2.0) and our sand gravel spectral dataset (R2 = 0.96, RPD = 5.06) compared to other deep learning and conventional spectroscopy analysis methods. In addition, the top ten characteristic wavelength points of sand gravel were identified. These can be used to choose a discrete spectrum measuring instrument, which has a relatively low cost.
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