PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze River

The excavation face stability is crucial for safety and risk management in slurry shield tunneling, especially for the river-crossing tunnel. To avoid face collapse or blow-out, shield operators need to keep air chamber pressure balanced using their own experience, which would be difficult, discontinuous and less reliable in the process of construction. Considering the disadvantage of the manual control process, this paper presents a predictive control system for air chamber pressure in slurry shield tunneling using Elman neural network (ENN) model. It mainly contains a theoretical model, an ENN predictor and an ENN controller to set optimal control parameters automatically tracking the desired air chamber pressure. Moreover, to improve the learning capability of ENN model, a particle swarm optimization (PSO) algorithm is implemented. This system has been tested with collected data of slurry shield operation parameters in the Yangtze riverbed metro tunnel project in Wuhan, China. Analysis revealed that the predictive control system using PSO-based Elman neural network model in this paper has the potential for enhancing face stability in slurry shield tunneling.     

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。     

Application of several optimization techniques for estimating TBM advance rate in granitic rocks

This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R~2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R~2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.     

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.     

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。     

基于BIM技术的大直径公铁合建盾构隧道项目管理应用研究

大直径公铁合建盾构隧道的施工过程较为复杂,为了将产生的信息更好的共享与管理,在施工前期建立基于GIS的BIM模型,BIM模型内的各构件信息包含了该构件的所有三维几何信息和施工信息,通过BIM模型链接施工全寿命周期内产生的信息,并研发建立BIM智慧指挥平台,从而指导项目管理。依托大直径公铁合建盾构隧道项目,将BIM技术覆盖至项目管理的全生命周期,包括隧道地质勘察、前期设计、设计碰撞核查、施工技术交底、工程进度可视化管理、大数据信息集成、安全质量监管与风险管控等阶段,为工程项目管理提供了良好的指导意义。     

砂土区间地铁盾构施工土体参数反演及其验证

在地铁砂土区间盾构法施工过程中,由于盾构机的掘进和注浆填充对周围的土层有一定的扰动作用,该土层的力学参数产生了一定变化。现场原位试验很难准确测得土体的参数。根据工程重要性和施工的信息化要求,土体的部分力学参数需要利用施工过程中地表沉降监测数据进行位移反演得出。结合沈阳某砂土区间盾构施工监测,利用改进的遗传优化的神经网络算法对其土体参数进行反演,得出较为准确的土体参数,并将其代入数值模型中进行计算验证。结果表明,数值计算结果与监测数据基本吻合。     

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。     

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.     

软土地区盾构上穿越既有隧道的离心模拟研究

随着地铁网络不断完善,新建盾构隧道近距离穿越既有隧道的现象越来越多。盾构近距离穿越既有隧道的影响问题,比常规盾构施工的研究更为复杂。结合上海外滩通道盾构上穿越地铁 2 号线工程,采用离心模型试验与现场实测相结合的方法对盾构上穿越对周围地层及既有隧道的影响进行了研究。文中选用排液法在离心场中模拟盾构施工,在国内首次实现了在不停机状态下模拟隧道开挖卸载、地层损失和注浆过程,并分析了盾构上穿越施工引起的地层、新建隧道与既有隧道的纵向位移变化规律。通过现场实测数据分析了既有隧道在盾构上穿越过程中纵向变形与时程曲线的变化规律。     

基于粒子群算法和广义回归神经网络的岩爆预测

 岩爆是岩石深部开挖中一种常见的工程地质灾害。为评价岩爆发生的可能性,提出一种基于粒子群算法和广义回归神经网络模型(PSO-GRNN模型)的岩爆预测方法。该方法利用已有岩爆数据,通过神经网络技术建立回归模型,采用粒子群算法对模型参数进行优化,减少人为因素对神经网络设计的影响。据此方法,在能量理论的基础上,选取洞壁围岩最大切向应力、岩石单轴抗压强度、抗拉强度和弹性能量指数作为主要影响因素,利用国内外26组已有工程数据建立岩爆预测的PSO-GRNN模型。通过对苍岭隧道和冬瓜山铜矿岩爆预测的工程实例分析验证该方法的可行性和适用性。所提方法可为类似工程的岩爆预测提供参考。     

基于DBN的盾构隧道施工参数优化方法研究

 为严格控制地表沉降,最大限度地减小隧道盾构施工给周围环境带来的不利影响,针对现有方法的不足,基于动态贝叶斯网络(DBN)理论提出一种盾构隧道施工参数优化方法,并将其应用于武汉某盾构隧道工程。首先选定待优化施工参数作为拟建网络结构的网络节点;然后设定离散化规则划分节点状态,以离散化工程实测数据完成参数学习,得到完整的DBN优化模型;该模型经验证用于工程施工参数优化,结果表明：该模型能够正确反映地表沉降与各施工参数之间的内在联系,具备一定的科学性;基于该模型进行反向诊断推理,能够确定各施工参数的最优设定区间;在该区间内对施工参数进行实时优化,能够降低地表沉降风险;该方法实时性强,具备一定的工程应用价值。     

基于PCA法的BP网络对冰蓄冷系统的空调负荷预测

BP神经网络应用于空调负荷预测时,如果输入变量较多或变量间存在相关关系,会直接影响BP神经网络的预测准确性。针对此问题,采用主成分分析(PCA)法,在保留原始数据主要信息的前提下提取数据的主要成分。根据各主成分的贡献率对神经网络输入变量进行缩减,达到压缩变量维数的目的。然后将主成分输入到负荷预测的模型之中进行预测,使之更符合空调负荷预测的特点,提高预测的速度和精度。最后通过实际算例进行验证,实验结果表明,该方法确实可行。     

盾构井开挖及暗挖法施工对既有地下隧道安全分析

盾构隧道下穿既有隧道或构筑物时,为降低对其地基、桩基础等产生不利影响,可采用先行暗挖施工,然后盾构空推的方案。依托穗莞深城际深圳机场至前海段区间隧道下穿桂湾一路地下市政隧道安全评估项目,通过Midas GTS NX有限元分析软件模拟注浆加固、PHC桩基切除、暗挖隧道施工等工序,得出了隧道施工监控数据要求等。结果表明:该方案施工地下隧道结构安全、位移指标选取合理。同时可为隧道开挖、桩基切除、二衬施工中的结构受力模型转换等工况提供建议和指导。     

Prediction of tunneling-induced ground movement with the multi-layer perceptron

This paper presents a method to predict ground movement around tunnels with artificial neural networks. Surface settlement above a tunnel and horizontal ground movement due to a tunnel construction are predicted with the help of input variables that have direct physical significance. A MATLAB based multi-layer backpropagation neural network model is developed, trained and tested with parameters obtained from the detailed investigation of different tunnel projects published in literature. The settlement is taken as a function of tunnel diameter, depth to the tunnel axis, normalized volume loss, soil strength, groundwater characteristics and construction methods. The output variables are settlement and trough width. Parameters for the prediction of horizontal ground movement include diameter to depth ratio (D/Z), unit weight of soil and cohesion. The neural network demonstrated a promising result and predicted the desired goal fairly successfully.     

基于PSO-BP算法的隧道非线性位移分析模型

粒子群优化(PSO)算法是近年来发展迅速,并得到广泛应用的一种仿生全局最优化算法.与遗传算法相比,该算法具有操作简单、易于编程的优点.结合铜黄高速公路汤屯段大田连拱隧道施工,采用PSO算法对BP神经网络的权值进行自动优化,获得训练效果最好的BP网络模型参数以提高网络的泛化能力,建立起基于PSO-BP算法的大田隧道施工位移非线性智能分析模型,并采用此模型对后续施工隧道变形进行了预测分析.与实测位移对比表明,本文建立的PSO-BP模型平均预测相对误差仅为3.1%,可很好地作为隧道信息化施工的一种辅助方法,并为其他类似岩土工程提供借鉴.     

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。     

超大直径双线泥水盾构施工的三维数值模拟

以上海长江西路隧道工程为背景,基于适合软土的修正剑桥模型并综合考虑盾构开挖、开挖面泥水压力、盾尾注浆以及盾壳刚度和坡度等因素,建立单台超大直径(=15.43m)泥水盾构往返推进的三维有限元模型。分析不同施工步、不同泥水压力下北线盾构施工对地表沉降以及新建南线隧道的横向位移影响。通过数据分析发现,盾构返回推进时的地表位移规律明显区别于单条隧道施工的情况;盾构的反向推进会造成已建隧道管片的挤压变形以及顺指针的旋转;返回推进时,泥水压力的不同取值对已建隧道的横向位移影响显著。根据数值分析结果提出相应的施工建议:盾构返回施工时适当减小泥水压力;密切监控盾构返回推进时切口断面后1倍刀盘直径范围内已建隧道管片的变形量。     

广州地铁某联络通道在软弱地层中的施工措施

联络通道作为盾构附属工程,一般在盾构通过后、隧道贯通前与盾构隧道同时施工。当联络通道在软弱地层中施工时,其施工安全性不仅与通道本身能否顺利施工有关,还关系到整个隧道的施工安全。如处理不当,会影响盾构施工的正常进行,甚至威胁已建成的盾构隧道的安全。因此,在软弱地层中施工联络通道,是盾构地铁施工中的难点之一,本文通过介绍广州地铁某联络通道在软弱地层施工的实例,为类似工程提供参考。     

B样条在盾构隧道沉降曲线拟合中的应用

基坑施工不可避免会对邻近的盾构隧道沉降产生影响,进而影响盾构隧道结构健康。如何选择合适的方法对隧道沉降监测点数据进行拟合,找出基坑施工参数与盾构隧道沉降趋势之间的内在关系,对快速判别盾构隧道结构性能有重要意义。本文利用三次B样条曲线在连接点处曲率保持一致的独特优势,用该方法对某毗邻型基坑施工期间引起的盾构隧道沉降监测数据进行拟合,在此基础上绘制出施工前后盾构隧道累计沉降的曲率曲线,了解盾构隧道结构的整体受力状态。曲率曲线既能有效地寻找到隧道结构薄弱部位,又能通过与地铁保护区曲率控制标准进行对比,判断隧道结构受力是否超限,为隧道的健康评判提供准确依据。