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.     

Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs

Prediction of machine performance is an essential step for planning, cost estimation and selection of excavation method to assure success of tunneling operation by hard rock TBMs. Penetration rate is a principal measure of TBM performance and is used to evaluate the feasibility of using a machine in a given ground condition and to predict TBM advance rate. In this study, a database of TBM field performance from two hard rock tunneling projects in Iran including Zagros lot 1B and 2 for a total length of 14.3 km has been used to assess applicability of various analysis methods for developing reliable predictive models. The first method used for this purpose was principal component analysis (PCA) which resulted in development of a set of new empirical equations. Also, two Soft computing techniques including adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR) have been employed for this purpose. As statistical indices, root mean square error (RMSE), correlation coefficient (R²), variance account for (VAF), and mean absolute percentage error (MAPE) were used to evaluate the efficiency of the developed artificial intelligence models for TBM performance prediction. The results of the analysis show that AI based methods can effectively be implemented for prediction of TBM performance. Moreover, it was concluded that performance of the SVR model is better than the ANFIS model. A high correlation was observed between predicted and measured TBM performance for the SVR model. This study shows the feasibility of using these systems and subsequent work is underway to expand the database of TBM field performance and use the aforementioned methods to develop a more comprehensive TBM performance prediction model.     

Estimation of convergence of a high-speed railway tunnel in weak rocks using an adaptive neuro-fuzzy inference system(ANFIS) approach

Estimation of tunnel diameter convergence is a very important issue for tunneling construction,especially when the new Austrian tunneling method(NATM) is adopted.For this purpose,a systematic convergence measurement is usually implemented to adjust the design during the whole construction,and consequently deadly hazards can be prevented.In this study,a new fuzzy model capable of predicting the diameter convergences of a high-speed railway tunnel was developed on the basis of adaptive neuro-fuzzy inference system(ANFIS) approach.The proposed model used more than 1 000 datasets collected from two different tunnels,i.e.Daguan tunnel No.2 and Yaojia tunnel No.1,which are part of a tunnel located in Hunan Province,China.Six Takagi-Sugeno fuzzy inference systems were constructed by using subtractive clustering method.The data obtained from Daguan tunnel No.2 were used for model training,while the data from Yaojia tunnel No.1 were employed to evaluate the performance of the model.The input parameters include surrounding rock masses(SRM) rating index,ground engineering conditions(GEC) rating index,tunnel overburden(H),rock density(?),distance between monitoring station and working face(D),and elapsed time(T).The model’s performance was assessed by the variance account for(VAF),root mean square error(RMSE),mean absolute percentage error(MAPE) as well as the coefficient of determination(R2) between measured and predicted data as recommended by many researchers.The results showed excellent prediction accuracy and it was suggested that the proposed model can be used to estimate the tunnel convergence and convergence velocity.     

Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

In mining or construction projects, for exploitation of hard rock with high strength properties, blasting is frequently applied to breaking or moving them using high explosive energy. However, use of explosives may lead to the flyrock phenomenon. Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans, especially workers in the working sites. Thus, prediction of flyrock is of high importance. In this investigation, examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out. One hundred and fifty-two blasting events in three open-pit granite mines in Johor, Malaysia, were monitored to collect field data. The collected data include blasting parameters and rock mass properties. Site-specific weathering index (WI), geological strength index (GSI) and rock quality designation (RQD) are rock mass properties. Multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and hybrid models including Harris Hawks optimization-based MLP (known as HHO-MLP) and whale optimization algorithm-based MLP (known as WOA-MLP) were developed. The performance of various models was assessed through various performance indices, including a10-index, coefficient of determination (R²), root mean squared error (RMSE), mean absolute percentage error (MAPE), variance accounted for (VAF), and root squared error (RSE). The a10-index values for MLP, RF, SVM, HHO-MLP and WOA-MLP are 0.953, 0.933, 0.937, 0.991 and 0.972, respectively. R² of HHO-MLP is 0.998, which achieved the best performance among all five machine learning (ML) models.     

基于贝叶斯优化RF-BiLSTM的盾构机掘进速度预测的研究

盾构机掘进速度的预测是保障盾构施工的重要参考指标。为了实现盾构机掘进速度的预测,本文提出了基于贝叶斯优化RF-BiLSTM的盾构机掘进速度预测方法,即TPE-RF-BiLSTM。首先通过随机森林实现盾构机运行数据的筛选,接着利用BiLSTM实现对盾构机掘进速度的预测。此外,为了提高超参数的搜索效率,贝叶斯优化被用于掘进速度预测模型的超参数搜索,以自动化的构建掘进速度的预测模型。最后,通过郑州某地铁施工段的真实数据验证所提方法,实验结果表明,所提的方法能够有效实现掘进速度的预测。即R2=0.9650,RMSE=1.684,表现优于XGBoost,LSTM等广泛应用的成熟机器学习算法。     

Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R²) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R², MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.     

城市环境下TBM施工对围岩稳定性影响的监测分析及支护参数优化

 重庆市轨道交通六号线一期五里店站-山羊沟水库节点工程是TBM首次应用于城市轨道交通的试验段工程,为研究施工期间隧道围岩的稳定情况,进行大量的现场监控量测。基于现场监测数据,研究拱顶沉降、围岩深部位移、锚杆轴力、钢拱架内力及围岩接触应力的分布特性及变化规律,并通过数值模拟进行支护参数优化,为隧道后续施工及设计方案优化提供依据,也可为TBM在类似城市环境中的应用提供参考。     

基于模糊数学的TBM施工岩体质量分级研究

 根据隧道掘进机(TBM)施工进度将围岩分为施工条件好、施工条件较好、施工条件较差和施工条件差4个等级。利用模糊数学方法,采用岩石单轴抗压强度UCS和岩体完整性指标KV,分别建立UCS和KV关于TBM施工岩体质量4级分级的隶属度函数。采用单极性S形函数,分别构建UCS和KV的权重函数。这样,基于模糊数学的最大隶属度准则,就可以对TBM施工的岩体质量进行分级。同时,给出3个施工实例,演算表明,预测的结果与TBM施工实际相吻合,表明该分级方法简单而实用,具有很好的应用前景。     

Comparison of machine learning methods for ground settlement prediction with different tunneling datasets

This study integrates different machine learning (ML) methods and 5-fold cross-validation (CV) method to estimate the ground maximal surface settlement (MSS) induced by tunneling. We further investigate the applicability of artificial intelligent (AI) based prediction through a comparative study of two tunnelling datasets with different sizes and features. Four different ML approaches, including support vector machine (SVM), random forest (RF), back-propagation neural network (BPNN), and deep neural network (DNN), are utilized. Two techniques, i.e. particle swarm optimization (PSO) and grid search (GS) methods, are adopted for hyperparameter optimization. To assess the reliability and efficiency of the predictions, three performance evaluation indicators, including the mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient (R), are calculated. Our results indicate that proposed models can accurately and efficiently predict the settlement, while the RF model outperforms the other three methods on both datasets. The difference in model performance on two datasets (Datasets A and B) reveals the importance of data quality and quantity. Sensitivity analysis indicates that Dataset A is more significantly affected by geological conditions, while geometric characteristics play a more dominant role on Dataset B.     

Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data

Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.     

双护盾TBM开挖膨胀岩洞段施工应用技术研究

某膨胀岩洞段,围岩强度低,抗风化能力弱,水理性质不良,易发生吸水膨胀失水收缩现象。基岩洞段褶皱、褶曲发育,地层产状变化大,围岩呈层状～碎裂结构。双护盾TBM开挖后,隧洞发生不同程度的坍塌与塌方。现采取“三低一连续”（低推力、低转速、低贯人度;快速连续掘进）快速封闭围岩,严格控制施工用水;使用化学材料灌浆固结围岩等施工应用技术,成功通过了泥质软岩类隧洞长地段,为今后双护盾掘进机施工提供了宝贵经验,社会和经济效益巨大。     

Real-time rock mass condition prediction with TBM tunneling big data using a novel rock–machine mutual feedback perception method

Real-time perception of rock mass information is of great importance to efficient tunneling and hazard prevention in tunnel boring machines (TBMs). In this study, a TBM–rock mutual feedback perception method based on data mining (DM) is proposed, which takes 10 tunneling parameters related to surrounding rock conditions as input features. For implementation, first, the database of TBM tunneling parameters was established, in which 10,807 tunneling cycles from the Songhua River water conveyance tunnel were accommodated. Then, the spectral clustering (SC) algorithm based on graph theory was introduced to cluster the TBM tunneling data. According to the clustering results and rock mass boreability index, the rock mass conditions were classified into four classes, and the reasonable distribution intervals of the main tunneling parameters corresponding to each class were presented. Meanwhile, based on the deep neural network (DNN), the real-time prediction model regarding different rock conditions was established. Finally, the rationality and adaptability of the proposed method were validated via analyzing the tunneling specific energy, feature importance, and training dataset size. The proposed TBM–rock mutual feedback perception method enables the automatic identification of rock mass conditions and the dynamic adjustment of tunneling parameters during TBM driving. Furthermore, in terms of the prediction performance, the method can predict the rock mass conditions ahead of the tunnel face in real time more accurately than the traditional machine learning prediction methods.     

Multifactorial fuzzy approach to the penetrability classification of TBM in hard rock conditions

Rate of penetration of a tunnel boring machine in a hard rock environment is generally a key parameter which expresses the ease or difficulty with which the rock mass can be excavated. In this paper, the penetrability of TBM in hard rock conditions was investigated with the developed fuzzy classification system. TBM penetration rate and rock properties (such as Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), rock brittleness/toughness, Average Distance between Planes of Weakness (DPW) and orientation of discontinuities in rock mass) were evaluated by using the multifactorial fuzzy approach which is a special case of multiple objective multifactorial decision making for the penetrability classification of TBM in hard rock conditions. Using the decision function, the penetrating performance of TBM was classified into three categories; Good, Medium and Poor. Eventually, it is possible to evaluate the penetrability and determine the advance rate for new conditions by carrying out the proposed rock properties tests and using the developed fuzzy classification system.     

Reducing risk in long deep tunnels by using TBM and drill-and-blast methods in the same project-the hybrid solution

There are many examples of TBM tunnels through mountains,or in mountainous terrain,which have suffered the ultimate fate of abandonment,due to insufficient pre-investigation.Depth-of-drilling limitatio...     

Analysis and prediction of TBM performance in blocky rock conditions at the Lötschberg Base Tunnel

This paper focuses on the analysis of the TBM performance recorded during the excavation of the Lötschberg Base Tunnel. The southern part of the tunnel was excavated by two gripper TBMs, partly through blocky rock masses at great depth. The jointed nature of the blocky rock mass posed serious problems concerning the stability of the excavation face. A detailed analysis has been carried out to obtain a relationship between the rock mass conditions and the TBM performance, using the Field Penetration Index (FPI). In blocky rock conditions, the FPI is defined as the ratio between the applied thrust force and the actual penetration rate. A database of the TBM parameters and the geological/geotechnical conditions for 160 sections along the tunnel has been established. The analysis reveals a relationship between the FPI and two rock mass parameters: the volumetric joint count (J_v) and the intact rock uniaxial compressive strength (UCS). Through a multivariate regression analysis, a prediction model for FPI in blocky rock conditions (FPI_blocky) is then introduced. Finally, other TBM performance parameters such as the penetration rate, the net advance rate and the total advance rate are evaluated using FPI_blocky.     

Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic

Predicting the penetration rate of a tunnel boring machine (TBM) plays an important role in the economic and time planning of tunneling projects. In the past years, various empirical methods have been developed for the prediction of TBM penetration rates using traditional statistical analysis techniques. Soft computing techniques are now being used as an alternative statistical tool. In this study, a fuzzy logic model was developed to predict the penetration rate based on collected data from one hard rock TBM tunnel (the Queens Water Tunnel # 3, Stage 2) in New York City, USA. The model predicts the penetration rate of the TBM using rock properties such as uniaxial compressive strength, rock brittleness, distance between planes of weakness and the orientation of discontinuities in the rock mass. The results indicated that the fuzzy model can be used as a reliable predictor of TBM penetration rate for the studied tunneling project. The determination coefficient (R ²), the variance account for and the root mean square error indices of the proposed fuzzy model are 0.8930, 89.06 and 0.13, respectively.     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness (K_IC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of K_IC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R², RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R² value and lower errors.     

TBM自重对围岩变形影响的数值模拟研究

为研究双护盾全断面隧道掘进机(TBM)自重对围岩变形的影响,基于FLAC3D有限差分软件,通过改变隧道的埋深与围岩条件,建立3种工况数值模型,并对每个工况分别进行了考虑TBM自重与不考虑其自重的数值模拟,以对比各种工况下有无TBM自重作用的隧道围岩纵向位移曲线(LDP曲线);研究了TBM自重对围岩变形的影响。模拟结果表明,TBM自重可抑制隧道围岩的径向位移,并随着开挖隧道的围岩等级升高、埋深变浅,其自重对围岩变形的影响越大,且与不考虑TBM自重的围岩变形相比,边墙洞壁的径向位移减小率最大,仰拱与拱顶次之。这些认识对研究TBM与围岩的相互作用和预测卡机有重要意义。     

Pressurised TBM tunnelling in mixed face conditions resulting from tropical weathering of igneous rock

Weathering is a process that turns rock into soil. Deep weathering is prevalent in tropical and sub-tropical areas. The resulting sub-surface conditions can be very onerous for tunnelling, with tunnel drives commonly encountering a significant proportion of mixed face conditions, comprising partly rock and partly soil. Problems that have been encountered have included: inability to maintain the face pressure, ground loss, sinkholes, slow rates of tunnelling, rapid tool wear, damage to tools, mixing arms and other parts of the TBM, very frequent and long interventions, clogging and blow-outs. The nature and extent of the problems on any particular tunnel have depended on the type and design of the TBM, the nature of the rock and the proportion of the tunnel in mixed ground. In Singapore this has resulted in a change from mainly EPB to mainly slurry tunnelling in weathered igneous rock; however, predominantly EPB TBMs have been used in weathered sedimentary rock. Information from EPB and slurry TBM drives is used to illustrate the issues involved.