Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.

Stability evaluation method of large cross-section tunnel considering modification of thickness-span ratio in mechanized operation

Purpose-This study aims to research the large cross-section tunnel stability evaluation method corrected after considering the thickness-span ratio.Design/methodology/approach-First,taking the Liuyuan Tunnel of Huanggang-Huangmei High-Speed Railway as an example and taking deflection of the third principal stress of the surrounding rock at a vault after tunnel excavation as the criterion,the critical buried depth of the large section tunnel was determined.Then,the strength reduction method was employed to calculate the tunnel safety factor under different rock classes and thickness-span ratios,and mathematical statistics was conducted to identify the relationships of the tunnel safety factor with the thickness-span ratio and the basic quality(BQ)index of the rock for different rock classes.Finally,the influences of thickness-span ratio,groundwater,initial stress of rock and structural attitude factors were considered to obtain the corrected BQ,based on which the stability of a large cross-section tunnel with a depth of more than 100 m during mechanized operation was analyzed.This evaluation method was then applied to Liuyuan Tunnel and Cimushan No.2 Tunnel of Chongqing Urban Expressway for verification.Findings-This study shows that under different rock classes,the tunnel safety factor is a strict power function of the thickness-span ratio,while a linear function of the BQ to some extent.It is more suitable to use the corrected BQ as a quantitative index to evaluate tunnel stability according to the actual conditions of the site.Originality/value-The existing industry standards do not consider the influence of buried depth and span in the evaluation of tunnel stability.The stability evaluation method of large section tunnel considering the correction of overburden span ratio proposed in this paper achieves higher accuracy for the stability evaluation of surrounding rock in a full or large-section mechanized excavation of double line high-speed railway tunnels.

Information Monitoring Technology for Support Structure of Railway Tunnel During Operation

In the process of railway construction, because of the inconvenience ofgeological condition, water bursting and mud surging happen frequently, and the laterdeformation of support structure on the happening geology section would threaten thenormal running of railway. The limit difference of deformation control value of thesupport structure section where geological accidents frequently happen, is small, andartificial half-automatic supervisory technology cannot get the health condition of tunnelin time, resulting many cars speed-down accidents due to deformation of supportstructure. Through design innovation, we introduce TGMIS in the later period ofYanzishan railway construction to quickly capture the deformation of support structure,the strain of lining concrete, the strain of steel frame, stress of surrounding soil, stress ofsurrounding water, strain of second lining steel bar and other situ data. Also we setobservation prism and measuring robot device in specific position inside tunnel, androbot laser locator laser spot is projected onto reflection target surface, by graphicprocessing algorithm, the receiver calculates the measured value and standard value ofthe 3D coordinates of the laser spot. Then the information is transmitted throughtransmitting device, transducer and USB-485 to computer to predict and evaluate thehealth condition of the support structure of the tunnel so as to provide safety warninginformation. Provide timely and reliable data for the operation company to avoid theoccurrence of vicious accidents.

Electrically and Optically Bistable Operation in an Integration of a 1310 nm DFB Laser and a Tunneling Diode

We experimentally demonstrate an In P-based hybrid integration of a single-mode DFB laser emitting at around 1310 nm and a tunneling diode. The evident negative differential resistance regions are obtained in both electrical and optical output characteristics. The electrical and optical bistabilities controlled by the voltage through the tunneling diode are also measured. When the voltage changes between 1.46 V and 1.66 V, a 200-mV-wide hysteresis loop and an optical power ON/OFF ratio of 17 dB are obtained. A side-mode suppression ratio of the integrated device in the ON state is up to 43 dB. The tunneling diode can switch on/off the laser within a very small voltage range compared with that directly controlled by a voltage source.

Causal Analysis on the Lining Exfoliation and Treatment Design of an Operational Tunnel

According to the actual situation of the secondary lining of a expressway tunnel in Chongqing,this paper analyzed the specific reasons for lining exfoliation with corresponding test reports.According to this,a quick treatment scheme for lining exfoliation is proposed,which can make the treatment timely and effective,and suggestions for treating similar diseases in tunnels are put forward,which can provide reference for similar projects.

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.

Experimental investigation on the invert stability of operating railway tunnels with different drainage systems using 3D printing technology

In recent years, the invert anomalies of operating railway tunnels in water-rich areas occur frequently,which greatly affect the transportation capacity of the railway lines. Tunnel drainage system is a crucial factor to ensure the invert stability by regulating the external water pressure(EWP). By means of a threedimensional(3D) printing model, this paper experimentally investigates the deformation behavior of the invert for the tunnels with the traditional drainage system(TDS) widely used in China and its optimized drainage system(ODS) with bottom drainage function. Six test groups with a total of 110 test conditions were designed to consider the design factors and environmental factors in engineering practice,including layout of the drainage system, blockage of the drainage system and groundwater level fluctuation. It was found that there are significant differences in the water discharge, EWP and invert stability for the tunnels with the two drainage systems. Even with a dense arrangement of the external blind tubes, TDS was still difficult to eliminate the excessive EWP below the invert, which is the main cause for the invert instability. Blockage of drainage system further increased the invert uplift and aggravated the track irregularity, especially when the blockage degree is more than 50%. However, ODS can prevent these invert anomalies by reasonably controlling the EWP at tunnel bottom. Even when the groundwater level reached 60 m and the blind tubes were fully blocked, the invert stability can still be maintained and the railway track experienced a settlement of only 1.8 mm. Meanwhile, the on-site monitoring under several rainstorms further showed that the average EWP of the invert was controlled within 84 k Pa, while the maximum settlement of the track slab was only 0.92 mm, which also was in good agreement with the results of model test.

Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine

During tunnel boring machine(TBM)excavation,lithology identification is an important issue to understand tunnelling performance and avoid time-consuming excavation.However,site investigation generally lacks ground samples and the information is subjective,heterogeneous,and imbalanced due to mixed ground conditions.In this study,an unsupervised(K-means)and synthetic minority oversampling technique(SMOTE)-guided light-gradient boosting machine(LightGBM)classifier is proposed to identify the soft ground tunnel classification and determine the imbalanced issue of tunnelling data.During the tunnel excavation,an earth pressure balance(EPB)TBM recorded 18 different operational parameters along with the three main tunnel lithologies.The proposed model is applied using Python low-code PyCaret library.Next,four decision tree-based classifiers were obtained in a short time period with automatic hyperparameter tuning to determine the best model for clustering-guided SMOTE application.In addition,the Shapley additive explanation(SHAP)was implemented to avoid the model black box problem.The proposed model was evaluated using different metrics such as accuracy,F1 score,precision,recall,and receiver operating characteristics(ROC)curve to obtain a reasonable outcome for the minority class.It shows that the proposed model can provide significant tunnel lithology identification based on the operational parameters of EPB-TBM.The proposed method can be applied to heterogeneous tunnel formations with several TBM operational parameters to describe the tunnel lithologies for efficient tunnelling.

Bilateral carpal tunnel syndrome and motor dysfunction caused by gout and type 2 diabetes:A case report

BACKGROUND Carpal tunnel syndrome(CTS)has been associated with gout and type 2 diabetes mellitus(T2DM).However,due to insufficient clinical understanding of goutrelated CTS and reliance on the diagnostic importance of elevated serum uric acid levels,such cases are prone to missed diagnosis,misdiagnosis,and delayed treatment.In addition,the effect of T2DM on gout-induced carpal tunnel syndrome has not been reported.CASE SUMMARY Herein,we present an unusual case of CTS and motor dysfunction caused by miliary tophaceous gout and T2DM.The patient presented to the hand and foot clinic with paresthesia of the fingers of both hands,especially at night.The patient was diagnosed with type 2 diabetes a month ago.Ultrasonography revealed bilateral transverse carpal ligament thickening with median nerve compression during hospitalization.The patient was successfully treated with carpal tunnel decompression and tendon release.The postoperative pathological examination revealed typical gout nodules.This case suggests that the presence of T2DM could accelerate tophi formation and worsen CTS symptoms,although no definitive proof in this regard has been described previously.CONCLUSION Tophi formation may most likely cause the co-occurrence of CTS and flexor dysfunction in gout and incipient diabetes patients.

Data-Driven Model for Risk Assessment of Cable Fire in Utility Tunnels Using Evidential Reasoning Approach

Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessment model is proposed to evaluate the cable fire risk in different UUT sections and improve O&M efficiency.Considering the uncertainties in the risk assessment,an evidential reasoning(ER)approach is used to combine quantitative sensor data and qualitative expert judgments.Meanwhile,a data transformation technique is contributed to transform continuous data into a five-grade distributed assessment.Then,a case study demonstrates how the model and the ER approach are established.The results show that in Shenzhen,China,the cable fire risk in District 8,B Road is the lowest,while more resources should be paid in District 3,C Road and District 25,C Road,which are selected as comparative roads.Based on the model,a data-driven O&M process is proposed to improve the O&M effectiveness,compared with traditional methods.This study contributes an effective ER-based cable fire evaluation model to improve the O&M efficiency of cable fire in UUTs.

煤矿智能快速掘进关键技术研究现状及展望

针对煤矿巷道掘进智能化建设情况,探讨了现阶段分别以连采机、掘锚一体机、全断面掘进系统、掘锚机为核心的四大类智能快速掘进成套装备发展现状,分析了四大类智能快掘装备与地质条件适用性,提出了实现煤矿智能化掘进需要解决的关键技术的问题,探讨了以信息传输与智能分析技术、智能感知技术、精确定位导航技术,自主定型定向截割技术、掘进机远程自主截割控制技术以及多级多工序智能协同控制技术为基础的智能化掘进实现途径;基于复杂地质条件下煤矿巷道掘进亟待解决的关键问题,提出了以掘进作业功能需求、掘进工艺要求为依据的系统化分析、模块化设计、功能优化组合的设计方法;研究了以虚控实、虚实结合的多级数字孪生构架,基于掘进巷道设备、环境、地质构造多维信息再现实现掘进机远程控制;提出了单机智能化、系统协同化、井下智控与地面远程监控相协同的控制方法。开发了新型的无重复碾压柔性超前支护技术、截割部多级伸缩截割技术、智能锚固机组多钻机并行作业等技术,研制了具有掘、支、锚、运、探多工序并行作业的新型快速掘进作业联合机组,提出了掘进机器人、超前支护机器人、锚固机器人、辅助运输机器人组柔性并行作业技术;提出了基于数据驱动的多机多工序协同控制行为规划决策方法。提出构建完整的煤矿井下跨系统全时空信息数字感知体系,构建集井下现场生产状态、掘进巷道空间信息、掘进装备状态、风险信息等多参量、多尺度、全时空特性的数据感知智能化监控平台,实现虚拟系统与实际掘进系统实时通信,通过数据驱动规划分析实现智能化、少人化掘进。

基于ELM神经网络的高速公路隧道运营风险评估模型

为克服传统高速公路隧道运营安全风险评估方法计算过程繁琐、运算效率低及泛化能力差等问题,采用极限学习机(Extreme Learning Machine,ELM)神经网络模型对高速公路隧道运营风险进行评估。首先,基于系统工程理论,分析了高速公路隧道运营风险影响因素,构建了运营风险评估指标体系。然后,以全国126个隧道典型运营事故数据为样本集,基于ELM神经网络算法,对比不同激活函数模型的分类准确率和测试时间指标,选定Sigmoid作为激活函数,训练得到高速公路隧道运营风险评估模型。最后,以该模型为核心算法开发了隧道运营风险评估系统,并依托广东省某高速公路隧道路段开展了工程应用。结果表明,所构建的风险评估模型简化了人工计算过程,可提升高速公路隧道运营风险评估的及时性和有效性。

基于TJS工法的盾构隧道运营变形控制

开展隧道洞内全方位微扰动高压喷射注浆(TJS)工法对运营隧道变形控制的研究.基于典型区间运营隧道实测数据,采用Plaxis 3D有限元数值模拟软件分析TJS工法横断面布置中桩长、打设角度、桩径对隧道加固效果的影响,通过在隧道底部打设局部垫层的方法优化TJS工法.结果表明,桩长越长、角度越接近40°、桩径越大,对隧道沉降的控制效果越明显. TJS工法加固后的隧道地表沉降减少16.38 mm,水平收敛变形为未加固时的42.87%.局部垫层能够显著提升TJS桩的加固效果,“两侧短中间长”的布置能够兼顾加固效果和降低施工成本.

考虑地层蠕变效应的桥梁桩基施工对邻近运营地铁隧道的影响

为研究桥梁桩基施工引起地层蠕变效应对邻近地铁隧道安全运营的影响,采用卸荷条件下黏土蠕变特性试验确定了隧道周围土体的蠕变模型,通过数值仿真FLAC^(3D)与现场监测相结合的方法,分析了桩基开挖期间地铁隧道的竖向位移、水平位移和应力分布状态。结果表明:广义Kelvin本构模型能够较为准确地描述黏土体开挖卸荷时的蠕变效应;桩基开挖后,邻近地铁隧道衬砌位移不断增大,随后进入稳定状态;随着桩基开挖数量的增加,地铁隧道竖向位移和水平位移总体表现为下沉和向外收敛趋势;桩-隧最小净距越小,桩基施工对隧道影响越大,采用隧道双侧布桩的施工方式,能够有效降低桩基开挖时隧道拱腰的累计水平位移,有利于地铁隧道的安全稳定运营。

山地轨道交通隧道Geo-BIM建模方法与应用研究

为解决山地轨道交通隧道在BIM建模中存在的隧道线路偏差大、隧道构件复杂繁多、属性添加效率低以及呈现效果单一等问题,提出一种基于Revit+Dynamo+Excel及EVS软件的山地轨道交通隧道Geo-BIM建模方法。结合隧道线性工程特点,对Revit内置Dynamo插件二次开发,实现隧道快速精准的参数化建模和批量定义属性参数。提出基于EVS软件建立隧道三维地质体模型的方法,制定地层建模和岩性建模的实现方式和标准流程,结合隧道BIM模型全面实现隧道建设过程中的可视化。依托施家山隧道项目,基于Cesium框架实现了BIM模型、GIS地表和地质体三维模型的集成,实现三维可视化模型与模型属性、数据信息等可交互式操作。实践表明,Geo-BIM建模方法可以快速地对隧道BIM模型及其三维地质模型进行建模,通过集成后的模型信息之间能够表现出很好的交互效果,具有良好的工程应用价值。

高速列车在艰险山区铁路长大隧道运行时的乘坐舒适性研究

为探讨艰险山区铁路长大隧道特殊线路环境对高速列车乘坐舒适性影响,考虑隧道内沿线高地温分布情况,建立列车隧道运行的计算空气动力学数值仿真模型和列车多体动力学模型,分析隧道温度和海拔高度对列车在长大隧道运行时车内压力及列车运行平稳性的影响规律。结果表明,在高地温隧道环境下,车内压力变化幅值最高下降13.2%,车体横向振动加速度仅在交会时减小,列车运行平稳性指标最高下降3.36%;随着海拔上升,车内压力变化幅值呈线性下降,列车运行平稳性指标减小,车体横向振动加速度仅在进出隧道及交会时减小。研究结果对保证高速列车在艰险山区铁路隧道运行时乘坐舒适性具有重要指导价值。

邻近运营隧道的大型基坑放坡开挖影响与实测兮析

[目的]基坑及隧道因建设过程的差异存在复杂的耦合作用,在基坑施工过程中会不可避免地对既有运营隧道产生扰动,引起隧道的位移,严重时将影响隧道运营安全。因此,有必要研究合理的基坑开挖方法和开挖步序,以减少基坑开挖过程中对既有邻近隧道的影响。[方法]依托某下沉式广场基坑,采用有限元数值模拟的方法针对邻近既有隧道的基坑的4种开挖工况进行研究,分析了不同开挖方法、开挖步序下的隧道内力分布规律、位移变化趋势及地层变形特征,比较了不同工况对施工期间隧道变形的影响,并结合基坑开挖期间的现场实际监测数据加以验证分析。[结果及结论]与拉槽放坡开挖相比,普通放坡开挖引起的隧道竖向变形较小;与整层开挖相比,接续开挖引起的隧道竖向变形较小;不同工序的隧道竖向变形最终结果较接近;与接续开挖相比,整层开挖引起的隧道水平变形比较大,这主要是由开挖步序多、基坑的时空效应明显所导致。根据现场实际监测数据,施工期间隧道竖向变形、水平变形,以及轨道竖向相对变形呈现先增大、后减小的趋势。

在不同时间点对兔行前交叉韧带重建术对骨道扩大的影响分析

目的观察在不同时间点对兔行前交叉韧带(ACL)重建术对其骨道扩大的影响。方法将80只5~6个月龄新西兰白兔随机分为4组,每组20只,均行右侧膝关节ACL切断,制备ACL损伤动物模型,实验组动物分别于造模后2周(A组)、4周(B组)、8周(C组)采用自体半腱肌腱进行ACL重建术,对照组(D组)不用ACL重建。观察术后一般情况,实验组动物分别于术后4周、8周及12周时用CT三维重建观察胫骨骨道扩大情况。12周后处死4组动物,行大体观察、组织学评价。结果4组动物术后无关节感染、死亡情况,实验组跛行及抽屉实验阴性,对照组跛行及抽屉实验阳性。实验组骨道的扩大程度在术后4周时比较差异无统计学意义(P>0.05);术后8周、12周时,C组骨道扩大的程度均大于A、B组(P<0.05),A、B组间差异均无统计学意义(P>0.05)。大体观察,A、B组骨道口组织与正常韧带止点相同,C组有类似正常韧带止点结构,D组中12只动物ACL残端完全吸收。A、B组组织学评分高于C组(P<0.05),而A、B组间比较差异无统计学意义(P>0.05)。结论本研究表明,在ACL损伤早期行重建术,更有利于降低骨道的扩大。

椿木山特长隧道施工风险评估及精细化施工方案研究

为提高特长隧道的施工效率,降低施工成本,本文依托椿木山特长隧道开展风险评估及精细化施工技术研究。分析了该隧道施工的重难点,结合各种风险源对隧道风险进行评估。基于隧道施工工艺,从人力安排和施工工时多角度分析了“七台套”大机作业的实施成效。并根据多种因素对比各种竖井布置方案的经济效益。结果表明:(1)隧道风险评估仅考虑单一风险忽视作业风险会使得风险出现不可控现象,应从地层、水文及技术水平等多角度分析;(2)与传统钻爆法施工相比,大机作业施工在降低人力成本和提高施工效率方面都具有明显优势;(3)竖井布置方案应在满足正常运营通风条件下选择埋深短、施工方便、经济效益好的方案。本文的研究成果为椿木山隧道施工提供了技术支持,也可为类似隧道提供技术参考,有助于大机作业推广。

运营交通隧道结构健康状态诊断研究进展

为形成运营隧道结构健康状态诊断的系统认识,从诊断指标体系、诊断指标分级判定标准、健康状态诊断方法和健康诊断系统4个方面,详细阐述和分析了隧道健康诊断发展现状,探讨健康诊断研究存在的不足,并指出未来可能的研究方向。