Yue Weiqi, Su Weilin, Gu Zhanfei, Qu Xiao
School of Civil Engineering and Environment, Zhengzhou University of Aeronautics, No. 15 Wenyuan West Road, Zhengzhou, 450046, Henan, China.
Yellow River Engineering Consulting Co., Ltd, No. 109 Jinshui Road, Zhengzhou, 450003, Henan, China.
Sci Rep. 2025 Apr 3;15(1):11450. doi: 10.1038/s41598-025-95072-0.
During the construction of TBM tunnels, a substantial quantity of rock debris is generated, leading to significant land occupation and environmental pollution. Recycling rock debris into construction materials and other resources emerges as a viable solution to these problems. To realize the continuous classified storage and disposal of tunnel rock debris, this research explores the four-level processing network, establishes an objective function for evaluating the recycling value of tunnel rock debris during TBM tunneling, and grades the recycling value by calculating the weight and similarity of their performance indicators (uniaxial compressive strength, content of acicular and flattened particles, mud content, and crushing index) through the TOPSIS method. Through correlation and weight analysis, we identify five key characteristics, i.e. cutterhead torque, tool penetration, cutterhead thrust, advancing rate, and support shoe pump pressure, to conduct real-time perception of the recycling value level of rock debris. Leveraging a comprehensive database that encompasses both tunnel rock debris performance indicators and TBM tunneling parameters, perception models are constructed using different machine learning algorithms. After Bayesian hyperparameter optimization, the perception models based on CART, SVM, KNN, and ANN demonstrate accuracies of 67.5%, 80.0%, 82.5%, and 83.8% respectively. Notably, the hyperparameter optimization significantly enhances the accuracy of the ANN perception model. When applying the optimized ANN-based rock debris recycling value grade perception model to TBM tunnel engineering, the tested perception accuracy rate stands at 83.3%, demonstrating its effectiveness and potential for practical applications. This approach provides valuable guidance for the graded storage and efficient recycling of tunnel rock debris and helps to alleviate the pollution problem.
在TBM隧道施工过程中，会产生大量的岩石碎屑，导致大量土地占用和环境污染。将岩石碎屑回收再利用为建筑材料和其他资源成为解决这些问题的可行方案。为实现隧道岩石碎屑的连续分类存储与处置，本研究探索了四级处理网络，建立了评估TBM掘进过程中隧道岩石碎屑回收价值的目标函数，并通过TOPSIS法计算其性能指标（单轴抗压强度、针片状颗粒含量、泥含量和压碎指标）的权重和相似度对回收价值进行分级。通过相关性和权重分析，识别出刀盘扭矩、刀具贯入度、刀盘推力、推进速度和支撑靴泵压力这五个关键特征，以实时感知岩石碎屑的回收价值水平。利用包含隧道岩石碎屑性能指标和TBM掘进参数的综合数据库，采用不同的机器学习算法构建感知模型。经过贝叶斯超参数优化后，基于CART、SVM、KNN和ANN的感知模型准确率分别为67.5%、80.0%、82.5%和83.8%。值得注意的是，超参数优化显著提高了ANN感知模型的准确率。将优化后的基于ANN的岩石碎屑回收价值等级感知模型应用于TBM隧道工程时，测试感知准确率为83.3%，证明了其有效性和实际应用潜力。该方法为隧道岩石碎屑的分级存储和高效回收提供了有价值的指导，并有助于缓解污染问题。
