Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling

Numerous empirical and analytical relations exist between shield tunnel characteristics and surface and subsurface deformation. Also, 2-D and 3-D numerical analyses have been applied to such tunneling problems. Similar but substantially fewer approaches have been developed for earth pressure balance (EPB) tunneling. In the Bangkok MRTA project, data on ground deformation and shield operation were collected. The tunnel sizes are practically identical and the subsurface conditions over long distances are comparable, which allow one to establish relationships between ground characteristics and EPB – operation on the one hand, and surface deformations on the other hand. After using the information to identify which ground- and EPB-characteristic have the greatest influence on ground movements, an approach based on artificial neural networks (ANN) was used to develop predictive relations. Since the method has the ability to map input to output patterns, ANN enable one to map all influencing parameters to surface settlements. Combining the extensive computerized database and the knowledge of what influences the surface settlements, ANN can become a useful predictive method. This paper attempts to evaluate the potential as well as the limitations of ANN for predicting surface settlements caused by EPB shield tunneling and to develop optimal neural network models for this objective.     

基于改进BP网络的盾构隧道开挖地表沉降预测

盾构隧道施工中引起的地表沉降是衡量开挖方式是否合适的关键指标。文中在介绍BP神经网络及盾构施工引起变形情况的基础上,对基于BP神经网络的盾构隧道开挖引起的地表沉降预测进行了研究,考虑了训练样本中奇异数据的剔除,采用变步长的方法,并选取适当的动量项系数,综合考虑各种影响因素,建立了盾构隧道开挖引起的地表沉降预测的BP网络模型,并对广州地铁二号线进行了具体的预测分析。分析结果表明:理论计算结果与工程实际情况一致,误差小于5%,所建立的预测模型是令人满意的。     

复杂环境下地铁隧道盾构施工诱发地表土体变形的智能预测

在复杂地质环境下,地铁盾构施工参数会有较大不同,使得施工过程中的地表沉降难以控制。常规的监测手段具有滞后性,难以应对突发情况。基于此,本文提出基于BP神经网络地铁隧道盾构施工诱发地表土体变形智能预测模型,通过与杭富城际铁路11标段盾构施工时的地表沉降、右线沉降和左线沉降的实测数据对比发现,BP神经网络能够准确预测复杂环境下盾构施工引起的沉降。     

基于BP神经网络的复合地层盾构掘进参数预测与分析

复杂地质条件下盾构机掘进参数的有效预测可以对盾构施工进行针对性的指导。基于深圳地铁11号线车公庙站～红树湾站和南山站～前海湾站两个区间Φ7m盾构施工现场监测的掘进参数,首先采用BP人工神经网络方法建立了复合地层条件下盾构掘进参数的预测模型|其次,以地层参数为输入组和盾构掘进参数为输出组,通过对数据样本进行训练,得到的输出值基本与原始数据一致,说明该预测模型具有很好的非线性映射能力;最后,采用盾构区间典型地段的地层参数,利用所建立的模型预测了复合地层条件下的盾构掘进参数,预测值与实际数据变化规律相近,平均误差在15%以内。本文建立的BP神经网络模型可用于复合地层条件下同类型盾构掘进参数的预测。     

应用RBF神经网络的预应力混凝土碳化深度预测研究

在现有混凝土碳化研究成果基础上,建立了预应力混凝土碳化预测模型。随后,运用径向基函数神经网络的基本原理,通过对影响预应力混凝土碳化深度因素的分析,建立了预测碳化深度的RBF和GRNN网络模型。通过实例进行了分析计算和预测,预测结果具有较高的精度。可以说,人工神经网络预测方法是一种可同时考虑各种影响因素组合、行之有效的混凝土碳化预测分析方法。     

Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method

Accurate prediction of shield tunneling-induced settlement is a complex problem that requires consideration of many influential parameters. Recent studies reveal that machine learning (ML) algorithms can predict the settlement caused by tunneling. However, well-performing ML models are usually less interpretable. Irrelevant input features decrease the performance and interpretability of an ML model. Nonetheless, feature selection, a critical step in the ML pipeline, is usually ignored in most studies that focused on predicting tunneling-induced settlement. This study applies four techniques, i.e. Pearson correlation method, sequential forward selection (SFS), sequential backward selection (SBS) and Boruta algorithm, to investigate the effect of feature selection on the model's performance when predicting the tunneling-induced maximum surface settlement (S_max). The data set used in this study was compiled from two metro tunnel projects excavated in Hangzhou, China using earth pressure balance (EPB) shields and consists of 14 input features and a single output (i.e. S_max). The ML model that is trained on features selected from the Boruta algorithm demonstrates the best performance in both the training and testing phases. The relevant features chosen from the Boruta algorithm further indicate that tunneling-induced settlement is affected by parameters related to tunnel geometry, geological conditions and shield operation. The recently proposed Shapley additive explanations (SHAP) method explores how the input features contribute to the output of a complex ML model. It is observed that the larger settlements are induced during shield tunneling in silty clay. Moreover, the SHAP analysis reveals that the low magnitudes of face pressure at the top of the shield increase the model's output.     

Model update and real-time steering of tunnel boring machines using simulation-based meta models

A method for the simulation supported steering of the mechanized tunneling process in real time during construction is proposed. To enable real-time predictions of tunneling induced surface settlements, meta models trained a priori from a comprehensive process-oriented computational simulation model for mechanized tunneling for a certain project section of interest are introduced. For the generation of the meta models, Artificial Neural Networks (ANN) are employed in conjunction with Particle Swarm Optimization (PSO) for the model update according to monitoring data obtained during construction and for the optimization of machine parameters to keep surface settlements below a given tolerance. To provide a rich data base for the training of the meta model, the finite element simulation model for tunneling is integrated within an automatic data generator for setting up, running and postprocessing the numerical simulations for a prescribed range of parameters. Using the PSO-ANN for the inverse analysis, i.e. identification of model parameters according to monitoring results obtained during tunnel advance, allows the update of the model to the actual geological conditions in real time. The same ANN in conjunction with the PSO is also used for the determination of optimal steering parameters based on target values for settlements in the forthcoming excavation steps. The paper shows the performance of the proposed simulation-based model update and computational steering procedure by means of a prototype application to a straight tunnel advance in a non-homogeneous soil with two soil layers separated by an inclined boundary.     

Prediction of TBM jamming risk in squeezing grounds using Bayesian and artificial neural networks

This study presents an application of artificial neural network(ANN) and Bayesian network(BN) for evaluation of jamming risk of the shielded tunnel boring machines(TBMs) in adverse ground conditions such as squeezing grounds.The analysis is based on database of tunneling cases by numerical modeling to evaluate the ground convergence and possibility of machine entrapment.The results of initial numerical analysis were verified in comparison with some case studies.A dataset was established by performing additional numerical modeling of various scenarios based on variation of the most critical parameters affecting shield jamming.This includes compressive strength and deformation modulus of rock mass,tunnel radius,shield length,shield thickness,in situ stresses,depth of over-excavation,and skin friction between shield and rock.Using the dataset,an ANN was trained to predict the contact pressures from a series of ground properties and machine parameters.Furthermore,the continuous and discretized BNs were used to analyze the risk of shield jamming.The results of these two different BN methods are compared to the field observations and summarized in this paper.The developed risk models can estimate the required thrust force in both cases.The BN models can also be used in the cases with incomplete geological and geomechanical properties.     

混沌局域法与神经网络组合供水量预测

城市供水量是非线性、非平稳时间序列,组合预测模型能获得更高精度预测结果。通过深入分析混沌局域法与神经网络预测模型特点,提出了一种新的组合预测模型。首先,应用混沌局域法对城市日供水量进行初预测,然后,应用神经网络对预测结果进行修正。由于所提出的组合模型利用了混沌局域法及神经网络进行优势互补,能同时提高预测精度与计算效率。为验证所提出组合预测模型的可行性,采用某市7a实测供水量数据,对混沌局域法、BPNN、RBF及GRNN神经网络4种单一预测模型及相应的3种组合模型预测精度进行定量分析,结果表明,组合预测模型精度都高于对应单一预测模型,混沌局域法与GRNN神经网络组合模型预测精度最高,且运算时间远低于单一神经网络模型运算时间。     

基于循环神经网络的盾构隧道#br# 引发地面最大沉降预测

伴随着计算机技术的快速发展,机器学习等新兴算法正在被越来越多地运用于预测隧道掘进引发的地面最大沉降。在隧道施工过程中,由盾构机和地面监测点位采集的数据具有很强的序列化特征,而传统的机器学习算法对序列数据的处理存在一定的局限性。循环神经网络(RNN)具有极强的对时序型数据的处理能力,在视频识别、语音翻译等领域有着广泛的应用。采用两种RNN模型(LSTM、GRU)和传统的BP神经网络模型,以地质参数、几何参数和盾构机参数作为输入,对隧道施工过程中引发的地面最大沉降进行预测分析。结果显示,RNN对隧道沉降的预测结果优于传统的BP神经网络模型,并且RNN在连续未知区段的预测结果比BPNN更加稳定。     

CAST工艺的BP和RBF人工神经网络仿真模型

将误差反向传播前馈(BP)神经网络模型和径向基函数(RBF)神经网络模型应用到CAST工艺中,并采用多输入、双输出神经网络模拟处理过程中各变量之间的关系和预测出水水质.误差分析结果表明,训练阶段RBF神经网络模型的拟合精度比BP神经网络模型的高,但两者的预测精度相差不大;测试阶段BP神经网络模型和RBF神经网络模型预测出水COD的平均相对误差分别为6.35%、6.80%,预测出水TN的平均相对误差分别为7.19%、5.49%,均在8%以下,这说明两种神经网络模型均可用于模拟CAST污水处理工艺各变量之间的关系和预测出水水质,为污水厂的运行管理提供了理论依据.     

土压平衡式盾构掘进过程的相似模型试验

土压平衡式盾构掘进过程是一个多系统综合运作的复杂力学过程。为实现土压平衡盾构掘进过程的相似模拟,设计制造能够完成掘削面开挖、螺旋出土器出土、盾构机推进、管片拼装、盾尾注浆等全过程模拟的土压平衡式模型盾构机。其次对盾构-地层系统的相关参量进行甄别,选出对试验结果有重要影响的参量,同时基于相似理论,通过量纲分析的方法得到盾构-地层系统的相似准则。基于该相似准则,进行土压平衡式盾构掘进过程的室内相似模型试验,获得土压平衡式盾构掘进对地层影响的一般规律,该试验结果经过相似关系换算后可直接反映出原型中的实际地层沉降量。这种能够实现掘进全过程模拟的土压平衡式模型盾构机为深入研究盾构施工对地层及周围环境的影响提供新手段。     

基于BP神经网络的水下岩溶地层#br# 盾构掘进参数预测与分析

随着我国城市地铁网的建设,越来越多的隧道将不可避免的穿越水下岩溶区,受制于岩溶地层的复杂性、注浆加固后地层的诸多不确定性,盾构穿越该类地层施工风险极大,而选取合理的盾构掘进参数是确保盾构安全与高效掘进的关键。以长沙地铁三号线盾构穿越水下岩溶段为工程依托,首先通过统计与分析钻探数据,明确了岩溶分布特征;其次,通过输入地层特征参数和隧道特征参数,建立了可输出盾构掘进速度、推力、刀盘扭矩、开挖仓压力、气垫仓压力和同步注浆量等掘进参数的BP神经网络水下岩溶盾构掘进参数预测模型;最后,对样本数据进行了训练,并成功应用于工程实践。研究结果表明：训练的输出值与期望值吻合度较高,构建的BP神经网络模型具有较好的适应性;输出的预测结果能有效反映实际盾构掘进参数的变化趋势,预测值与实际期望值的平均误差均低于13%,在误差可接受范围内。现场应用结果表明,地表沉降在安全范围内,盾构掘进过程中未发生工程事故,盾构掘进参数选取合理,姿态控制较好。研究成果可用于指导水下岩溶盾构隧道工程施工,且该方法的提出也为其他复杂地层盾构掘进参数合理选取提供了新思路。     

土压平衡模型盾构掘进试验研究

土压平衡盾构掘进是软土地区地铁隧道施工的主要方法之一,然而,它在不同的土层中的适应性是不一样的。为研究土压平衡盾构机的盾构施工参数以及刀盘开口率对土层的适应性,在新建立的大型盾构模拟试验平台上,利用直径为1.8m的土压平衡模型盾构机在软土、砂土、砂砾土层中进行了盾构掘进模拟试验。试验平台的监测系统实时采集了盾构推进过程中的各种工作参数,通过分析试验数据,本文尝试对盾构掘进过程中土舱内外土压力的相关关系、刀盘扭矩和推力的变化及其影响因素进行了试验研究,还研究了不同刀盘开口率对盾构总推力和刀盘扭矩的影响规律,研究结果对土压平衡盾构机的设计和施工具有参考意义。     

Prediction of bed load sediments using different artificial neural network models

Modeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data. In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, radial based function (RBF), and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed. The optimum models were found through 102 data sets of flow discharge, flow velocity, water surface slopes, flow depth, and mean grain size. The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data. The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models. The accuracy performance of all models was evaluated and interpreted using different statistical error criteria, analytical graphs and confusion matrixes. Although the ANN models predicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network error was outperform than others.     

Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network

An accurate prediction of earth pressure balance (EPB) shield moving performance is important to ensure the safety tunnel excavation. A hybrid model is developed based on the particle swarm optimization (PSO) and gated recurrent unit (GRU) neural network. PSO is utilized to assign the optimal hyperparameters of GRU neural network. There are mainly four steps: data collection and processing, hybrid model establishment, model performance evaluation and correlation analysis. The developed model provides an alternative to tackle with time-series data of tunnel project. Apart from that, a novel framework about model application is performed to provide guidelines in practice. A tunnel project is utilized to evaluate the performance of proposed hybrid model. Results indicate that geological and construction variables are significant to the model performance. Correlation analysis shows that construction variables (main thrust and foam liquid volume) display the highest correlation with the cutterhead torque (CHT). This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.     

Prediction of shield tunneling-induced ground settlement using machine learning techniques

Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors. This study investigates the efficiency and feasibility of six machine learning (ML) algorithms, namely, back-propagation neural network, wavelet neural network, general regression neural network (GRNN), extreme learning machine, support vector machine and random forest (RF), to predict tunneling-induced settlement. Field data sets including geological conditions, shield operational parameters, and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models. Three indicators, mean absolute error, root mean absolute error, and coefficient of determination the (R²) are used to demonstrate the performance of each computational model. The results indicated that ML algorithms have great potential to predict tunneling-induced settlement, compared with the traditional multivariate linear regression method. GRNN and RF algorithms show the best performance among six ML algorithms, which accurately recognize the evolution of tunneling-induced settlement. The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.     

遮拦叠交效应下地铁盾构掘进引起地层沉降分析

 地铁隧道施工诱发的土体沉降以及临近地下构筑物变形是我国城市轨道交通施工安全控制和风险评估中较为关心的一类施工问题。目前,针对该领域地层沉降的简化理论研究还仅仅针对自由位移场,没有考虑临近既有构筑物的遮拦效应影响。依托上海在建地铁施工工程实践,采用简化理论方法、三维有限元数值模拟方法以及现场监测方法,分析考虑运营隧道遮拦效应影响的土压平衡盾构施工引起的周围土体沉降规律,并与自由位移场条件下盾构施工引起的地层变形进行对比分析;在此基础上,给出地铁盾构复杂叠交穿越引起的临近地铁隧道的变形规律。研究表明,本文提出的简化理论方法和三维有限元数值模拟方法可以较好地模拟遮拦叠交效应下地铁盾构掘进引起的地层沉降变形;临近既有建(构)筑物施工,盾构施工引起的周围土体沉降较大程度地受到遮拦效应影响,与自由位移场条件下的计算结果对比存在较大差别。最后,结合盾构施工监测数据,提出复杂遮拦叠交效应下的盾构叠交施工变形控制技术措施。成果可为合理制定施工场地存在复杂建(构)筑物工况条件的地铁隧道开挖对周围环境保护措施提供一定的理论依据。     

地铁隧道盾构法施工过程中地层变位的三维有限元模拟

在全面分析土压平衡式盾构施工过程中影响周围土体变形各主要因素的基础上，提出一种能够综合考虑各种因素的盾构施工三维非线性有限元模拟方法，通过对某地铁隧道盾构施工过程的模拟，分析了盾构推进过程中隧道周围及地表处土体的位移和变形以及横断面不同深度上的沉降分布规律，计算得到的隧道纵向地面沉降分布曲线与实测数据非常接近，计算结果表明所提出的盾构施工模拟方法是有效可行的。     

混凝土氯离子扩散系数的RBF神经网络预测模型

提出了一种基于RBF神经网络的氯离子扩散系数预测模型,将RBF网络模型预测的结果与另外三种不同输入的RBF模型、BP网络模型的预测结果以及实测结果进行了对比分析,结果表明,RBF神经网络模型相对其他输入指标体系模型的预测精度有所提高,且能满足工程的需要,可以作为氯离子扩散系数预测的一种新的有效的方法。