基于纵波波速的块状岩体GSI系统

 为减少目前GSI系统对现场地质观察的依赖程度,降低其应用难度,且使其能更加准确地反映一定深度范围内的岩体特性,根据现有研究成果与GSI系统输入参数的定性、定量对应关系,建立基于纵波波速的GSI系统,据此获得大岗山坝区岩体的GSI,通过对比分析此结果与经验公式的结果及基于GSI的岩体变形模量的预测值与实测值的分布规律及其相关性,探讨了将岩体完整系数和岩石风化程度系数作为GSI系统输入参数的可行性。结果表明：基于岩体完整系数和岩石风化程度系数的GSI系统基本可行;风化程度输入参数采用风化岩石与未风化岩石的波速比平方较为合理;岩体完整系数和岩石风化程度系数丰富了GSI系统的输入参数。     

Use of an improved ANN model to predict collapse depth of thin and extremely thin layered rock strata during tunnelling

Numerous collapses have occurred during the excavation of diversion tunnels in the thin and extremely thin layered rock strata at Wudongde Hydropower Station in China. Hence, a reliable method is required to predict the risk and the depth of collapse. However, both theory and practice indicate that one single criterion methods cannot satisfactorily predict the collapse depth accurately. In this study, using an artificial neural network (ANN), an intelligent prediction method has been investigated. Through theoretical and statistical analyses, six input parameters (i.e., cover depth, minor–major principal stress ratio, geological strength index, excavation method, support strength and attitude of rock), have been selected and used in the model. Obtained from three diversion tunnels at Wudongde Hydropower Station, forty-five learning samples and six testing samples were used in the training of the model. The structural parameters and the initial weights of the ANN have been optimized by a genetic algorithm (GA). The trained model was then used to predict the collapse depth of another six excavation sites. The predictions show good agreement with the measurements at the sites. The absolute errors between the predicted and the measured collapse depths are all less than 0.35 m, and the relative errors are all less than 15%. Application of the improved ANN method to the tunnel collapse analysis at Wudongde Hydropower Station confirms its effectiveness in predicting collapse depth during tunnelling.     

A generic method for rock mass classification

Rock mass classification (RMC) is of critical importance in support design and applications to mining, tunneling and other underground excavations. Although a number of techniques are available, there exists an uncertainty in application to complex underground works. In the present work, a generic rock mass rating (GRMR) system is developed. The proposed GRMR system refers to as most commonly used techniques, and two rock load equations are suggested in terms of GRMR, which are based on the fact that whether all the rock parameters considered by the system have an influence or only few of them are influencing. The GRMR method has been validated with the data obtained from three underground coal mines in India. Then, a semi-empirical model is developed for the GRMR method using artificial neural network (ANN), and it is validated by a comparative analysis of ANN model results with that by analytical GRMR method.     

Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment

This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration (ROP) of tunnel boring machine (TBM), which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment. For this purpose, a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM. Initially, the main dataset was utilised to construct and validate four conventional soft computing (CSC) models, i.e. minimax probability machine regression, relevance vector machine, extreme learning machine, and functional network. Consequently, the estimated outputs of CSC models were united and trained using an artificial neural network (ANN) to construct a hybrid ensemble model (HENSM). The outcomes of the proposed HENSM are superior to other CSC models employed in this study. Based on the experimental results (training RMSE = 0.0283 and testing RMSE = 0.0418), the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.     

基于漂移度的隧道围岩质量分级组合评价方法

针对现有隧道围岩质量分级方法评价结果存在非一致性问题的特点,首先引入组合评价思想,选取多种已有围岩质量分级方法作为基础分级方法,建立了融合各基础分级方法优越性的隧道围岩质量分级组合评价计算模型;其次,依据不同基础分级方法评价指标的物理意义和量纲不同的特点,建立了评价指标的标准化方法,使不同基础分级方法评价结果具有可比性,以解决组合评价计算模型的组合计算方法问题;然后,针对不同基础分级方法评价结果的合理性存在差异的特点,引入漂移度概念,建立了基础分级方法合理程度的度量方法,并在此基础上,提出基础分级方法权重分配的合理确定方法,进而建立了隧道围岩质量分级的新型组合评价方法,该方法不仅可以解决不同基础分级方法评价结果的非一致性问题,更重要的是可以充分发挥各基础分级方法的优势,避免它们的不合理性,使隧道围岩质量分级更为合理;最后,通过工程实例分析,表明了该方法的合理性与可行性。     

遗传算法BP神经网络在隧洞围岩分类中的应用

针对目前神经网络训练易陷入局部极小点问题，用遗传算法优化神经网络的连接权，并在遗传进化过程中采取保留最优个体的策略，建立了基于遗传算法的BP神经网络的模型，并应用于解决水工隧洞围岩分类这一非线性和不确定性较大的实际问题，证明了这种方法是科学可行的。     

基于应力与围岩分类的冲击地压危险性评价研究

 基于山东某矿井复杂多样的地质和开采环境,提出了对冲击地压实行分类评价的技术思路。根据外部应力与巷道围岩相互作用后的围岩结构稳定性及其冲击倾向性,对围岩的冲击危险性和类型进行分类。外部静应力计算时采用倾向“载荷三带”理论模型,动应力计算时采用长壁工作面走向“载荷三带”理论模型,再叠加上构造应力等,实现了外部应力的近似计算;将外部应力作用于不同的围岩结构,结合煤岩体的冲击倾向性,得到围岩的冲击危险性和冲击类型。以此为基础形成的冲击地压分类与评价方法能较准确地反映回采工作面的冲击类型和危险程度,为制定针对性的治理措施提供了较准确的依据。研究成果已经在山东能源集团进行了应用,取得了良好的效果。     

基于能量法测量节理岩体品质因子的实验研究

品质因子是衡量岩体中应力波衰减特性的一个重要参数。利用分离式霍普金森压杆实验系统对含人工节理花岗岩试样进行单轴冲击压缩实验,研究节理吻合系数(JMC)对岩体试样品质因子的影响。首先,根据品质因子基本概念得出了利用应力波能量计算品质因子的方法,并证明在试样严格满足应力均匀条件时,其与应力–应变曲线方法是等价的。然后,用三波法得到了节理试样的应力–应变曲线,同时用应力波能量方法计算了品质因子。实验结果表明:随着节理吻合系数(JMC)降低,应力–应变曲线滞回环面积增加,节理岩体试样的动态割线模量和品质因子均减小,说明节理接触面积减小弱化了整个试样并且使试样能量耗散能力增加。     

Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning

Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed.     

基于深度学习技术的公路隧道围岩分级方法

通过深度学习技术提取公路隧道掌子面图片中的围岩分级相关信息。训练以掌子面图片和特征标签为数据集的深度卷积神经网络模型,识别围岩的节理、裂隙、破碎程度、粗糙程度、光滑程度、泥夹石和涌水等分布式特征;结合深度学习技术和岩体裂隙图像智能解译方法统计围岩节理组数和间距来描述结构面完整程度;再利用色彩模型确定岩石种类描述出岩石坚硬程度;最后将围岩分级各判别因子转换为BQ值进行分级,获得围岩分级最终结果。结果表明:深度学习模型适用于识别围岩不同形态特征,利用图像识别技术获取的围岩分级参数能够实现对公路隧道围岩等级的综合判定。该处理结果与传统BQ分级结果相吻合,验证了深度学习围岩分级的可行性和准确性。     

基于支持度的隧道围岩质量分级组合评价方法

针对基于漂移度的隧道围岩质量分级组合评价方法存在漂移度参考对象不一致,计算结果不可比问题,引入支持度概念作为各基础评价方法权重计算的依据。首先,从相似度和差异度两个方面建立新的支持度数学定义,避免了传统支持度公式仅考虑差异度的缺陷。其次,以整体数据对每种基础评价方法评价结果的综合支持度作为权重计算综合评价结果,从而建立了隧道围岩质量分级的新型组合评价方法,该方法不仅可以解决不同基础分级方法评价结果的非一致性问题,更重要的是其解决了漂移度概念下参考对象的不一致问题,使得权重结果更客观实际,理论基础更加坚实。最后,通过工程实例分析,表明了该方法的合理性与可行性。     

Multiclass stand-alone and ensemble machine learning algorithms utilised to classify soils based on their physico-chemical characteristics

This study has provided an approach to classify soil using machine learning. Multiclass elements of stand-alone machine learning algorithms (i.e. logistic regression (LR) and artificial neural network (ANN)), decision tree ensembles (i.e. decision forest (DF) and decision jungle (DJ)), and meta-ensemble models (i.e. stacking ensemble (SE) and voting ensemble (VE)) were used to classify soils based on their intrinsic physico-chemical properties. Also, the multiclass prediction was carried out across multiple cross-validation (CV) methods, i.e. train validation split (TVS), k-fold cross-validation (KFCV), and Monte Carlo cross-validation (MCCV). Results indicated that the soils' clay fraction (CF) had the most influence on the multiclass prediction of natural soils' plasticity while specific surface and carbonate content (CC) possessed the least within the nature of the dataset used in this study. Stand-alone machine learning models (LR and ANN) produced relatively less accurate predictive performance (accuracy of 0.45, average precision of 0.5, and average recall of 0.44) compared to tree-based models (accuracy of 0.68, average precision of 0.71, and recall rate of 0.68), while the meta-ensembles (SE and VE) outperformed (accuracy of 0.75, average precision of 0.74, and average recall rate of 0.72) all the models utilised for multiclass classification. Sensitivity analysis of the meta-ensembles proved their capacities to discriminate between soil classes across the methods of CV considered. Machine learning training and validation using MCCV and KFCV methods enabled better prediction while also ensuring that the dataset was not overfitted by the machine learning models. Further confirmation of this phenomenon was depicted by the continuous rise of the cumulative lift curve (LC) of the best performing models when using the MCCV technique. Overall, this study demonstrated that soil's physico-chemical properties do have a direct influence on plastic behaviour and, therefore, can be relied upon to classify soils.     

Neural network-based model for prediction of permanent deformation of unbound granular materials

Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation (PD) of unbound granular materials (UGMs), which make these methods more conservative. In addition, there are limited regression models capable of predicting the PD under multi-stress levels, and these models have regression limitations and generally fail to cover the complexity of UGM behaviour. Recent researches are focused on using new methods of computational intelligence systems to address the problems, such as artificial neural network (ANN). In this context, we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads. Extensive repeated load triaxial tests (RLTTs) were conducted on base and subbase materials locally available in Victoria, Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks. Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix. The ANN model consists of one input layer with five neurons, one hidden layer with twelve neurons, and one output layer with one neuron. The five inputs were the number of load cycles, deviatoric stress, moisture content, coefficient of uniformity, and coefficient of curvature. The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%. It shows that the ANN method is rapid and efficient to predict the PD, which could be implemented in the Austroads pavement design method.