Hard-rock tunnel lithology identification using multi-scale dilated convolutional attention network based on tunnel face images

IF 2.9 3区 工程技术 Q2 ENGINEERING, CIVIL
Wenjun Zhang, Wuqi Zhang, Gaole Zhang, Jun Huang, Minggeng Li, Xiaohui Wang, Fei Ye, Xiaoming Guan
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引用次数: 0

Abstract

For real-time classification of rock-masses in hard-rock tunnels, quick determination of the rock lithology on the tunnel face during construction is essential. Motivated by current breakthroughs in artificial intelligence technology in machine vision, a new automatic detection approach for classifying tunnel lithology based on tunnel face images was developed. The method benefits from residual learning for training a deep convolutional neural network (DCNN), and a multi-scale dilated convolutional attention block is proposed. The block with different dilation rates can provide various receptive fields, and thus it can extract multi-scale features. Moreover, the attention mechanism is utilized to select the salient features adaptively and further improve the performance of the model. In this study, an initial image data set made up of photographs of tunnel faces consisting of basalt, granite, siltstone, and tuff was first collected. After classifying and enhancing the training, validation, and testing data sets, a new image data set was generated. A comparison of the experimental findings demonstrated that the suggested approach outperforms previous classifiers in terms of various indicators, including accuracy, precision, recall, F1-score, and computing time. Finally, a visualization analysis was performed to explain the process of the network in the classification of tunnel lithology through feature extraction. Overall, this study demonstrates the potential of using artificial intelligence methods for in situ rock lithology classification utilizing geological images of the tunnel face.

利用基于隧道工作面图像的多尺度扩张卷积注意力网络识别硬岩隧道岩性
为了对硬岩隧道中的岩体进行实时分类,必须在施工过程中快速确定隧道工作面的岩性。在当前机器视觉人工智能技术取得突破性进展的推动下,我们开发了一种基于隧道工作面图像的隧道岩性分类自动检测新方法。该方法利用残差学习来训练深度卷积神经网络(DCNN),并提出了一种多尺度扩张卷积注意力块。不同扩张率的卷积块可以提供不同的感受野,因此可以提取多尺度特征。此外,注意力机制还可用于自适应地选择突出特征,进一步提高模型的性能。在本研究中,首先收集了由玄武岩、花岗岩、粉砂岩和凝灰岩组成的隧道面照片组成的初始图像数据集。在对训练、验证和测试数据集进行分类和增强后,生成了一个新的图像数据集。实验结果比较表明,建议的方法在准确率、精确度、召回率、F1 分数和计算时间等各项指标上都优于之前的分类器。最后,还进行了可视化分析,解释了网络通过特征提取对隧道岩性进行分类的过程。总之,本研究展示了利用隧道面地质图像的人工智能方法进行现场岩石岩性分类的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
3.30%
发文量
734
期刊介绍: Frontiers of Structural and Civil Engineering is an international journal that publishes original research papers, review articles and case studies related to civil and structural engineering. Topics include but are not limited to the latest developments in building and bridge structures, geotechnical engineering, hydraulic engineering, coastal engineering, and transport engineering. Case studies that demonstrate the successful applications of cutting-edge research technologies are welcome. The journal also promotes and publishes interdisciplinary research and applications connecting civil engineering and other disciplines, such as bio-, info-, nano- and social sciences and technology. Manuscripts submitted for publication will be subject to a stringent peer review.
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