Ensuring safety in tunnel construction necessitates a rapid and objective evaluation of exposed tunnel faces for proactive rock mass risk assessment. The diversity of rock types and discontinuities contributes to structural complexity and local variability, introducing subjectivity and uncertainty in traditional evaluations based on the experience of field engineers. In this study, a convolutional neural network (CNN) is employed for regression to enhance the objectivity and consistency of rock mass classification via rock mass rating (RMR). The model interprets the entire tunnel face image holistically by identifying and learning from complex patterns, rather than detecting specific geological features like fractured or discontinuity zones. The network model, comprising feature extraction and regression blocks, utilizes the EfficientNet family for computational efficiency and accuracy. Data augmentation and ensemble learning address prediction variability due to image quality, optimizing the model for accurate RMR predictions and providing error ranges. The RMR predictions using excavation site images closely follow the field-evaluated evolution. A server communication-based mobile application is developed for real-time RMR evaluation, enhancing its practical field applicability. In geology, tunnel, and geotechnical engineering, where decisions rely on extensive experience, our approach demonstrates that deep learning can enhance decision-making by analyzing large accumulated datasets to predict optimal outcomes.

中文翻译：

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根据隧道掌子面图像增强岩体评级预测：一种支持决策的集成深度学习方法


为了确保隧道施工的安全，需要对暴露的隧道面进行快速、客观的评估，以进行主动的岩体风险评估。岩石类型和不连续性的多样性导致结构复杂性和局部变异性，在基于现场工程师经验的传统评估中引入了主观性和不确定性。在本研究中，采用卷积神经网络（CNN）进行回归，通过岩体评级（RMR）来增强岩体分类的客观性和一致性。该模型通过识别和学习复杂模式来整体解释整个隧道掌子面图像，而不是检测裂缝或不连续区域等特定地质特征。该网络模型由特征提取和回归模块组成，利用 EfficientNet 系列来提高计算效率和准确性。数据增强和集成学习解决了图像质量导致的预测可变性，优化了模型以实现准确的 RMR 预测并提供误差范围。使用挖掘现场图像的 RMR 预测密切遵循现场评估的演变。开发了基于服务器通信的移动应用程序，用于实时RMR评估，增强了其实际现场适用性。在地质、隧道和岩土工程中，决策依赖于丰富的经验，我们的方法表明深度学习可以通过分析大量积累的数据集来预测最佳结果来增强决策。