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。

公路隧道运营韧性研究进展

以韧性理念为核心的系统安全分析范式,为现代公路隧道运营安全管理提供科学而务实的新视角。为深入理解韧性理念,首先结合国内外不同工程领域对韧性研究的进展,梳理总结出以下2点共识:1)韧性是一种综合性能力,着眼于系统全过程的表现;2)韧性包含一系列特征属性,可用鲁棒性、冗余性、资源充裕度、快速性囊括。鉴于经典风险分析于公路隧道运营安全管理的部分局限性,探讨引入韧性视角的必要性和意义。进而定义公路隧道运营韧性,即为对影响公路隧道正常运营通行的实际或潜在负面效应,进行预防、抵抗、适应、恢复的能力,并分析全阶段性、动态演进性和量化客观性的内涵,构建初步的概念框架。再提出基于感知、响应、学习和预测4种功能来增强隧道运营韧性的路径以及初步的韧性提升措施。特别地,针对火灾风险,介绍相关的智能技术与装备案例,包括智能火灾检测、火灾预测技术和隧道入口的柔性阻拦系统等。未来,对公路隧道运营韧性的研究将不断深化,通过构建全面的理论框架,以指导实践并推动隧道运营安全管理的科学化、系统化发展,从而建立更安全、高效、可持续的隧道运营体系。

高铁隧道片状掉块气动行为及对行车安全影响

高铁隧道衬砌病害日益恶化,衬砌掉块问题愈发严重,对列车运行安全危害极大。为探究隧道内行车时衬砌掉块脱落对行车安全性的影响,建立大气-隧道-衬砌掉块-列车三维数值模型,对列车运行条件下衬砌掉块的气动行为进行分析,研究片状衬砌掉块初始下落位置对行车安全性的影响。研究结果表明,不同剥落位置片状掉块沿隧道纵向位移均为负值;拱顶掉块沿隧道纵向位移平均为侧边墙掉块的1.45倍,上侧边墙掉块横向运动位移为拱顶掉块的2~3倍;拱顶掉块随着距隧道中线距离的增大受流场结构影响越显著,初始加速度峰值越大;拱顶剥落的衬砌掉块可能与列车直接相碰,上侧边墙剥落的衬砌掉块可能与车身侧面相碰或者被卷入到轮轨内,下侧边墙剥落的衬砌掉块远离车身运动,对行车安全无影响。研究结果对今后高速铁路隧道衬砌病害防治具有参考意义。

云模型在地下综合管廊运维安全管理绩效评价中的应用研究

为了系统地了解地下综合管廊运维安全管理工作效果,提出一种基于云模型的管廊运维安全管理绩效评价模型。在分析影响管廊运维安全相关因素的基础上,构建了管廊运维安全管理绩效评价指标体系。利用层次分析法和熵权法进行组合赋权从而确定各项评价指标权重,结合云模型进行管廊运维安全管理绩效评价。结果显示:所调研管廊的安全运维工作评价结果在“合格”和“良好”之间,总体趋向于“良好”,并识别出人员培训与提升是运维安全管理工作中的薄弱环节。该模型可以有效地评价管廊运维安全管理工作效果并识别出风险环节,丰富了管廊运维安全管理绩效评价的理论研究。

铁路隧道工程节能减排技术研究现状及展望

考虑到铁路隧道工程的施工运营过程具有高能耗、高温室气体排放的特点,本文通过统计调研近10年来的相关文献,总结了目前常用的几种铁路隧道节能减排技术。从施工、运营两个方面介绍了铁路隧道所采用的节能技术:包括增大施工废渣回收利用率、利用可再生环保建材等绿色施工技术;应用智能控制系统和变频调速等方式降低风机能耗的节能通风技术;通过智能化控制灯具实现隧道按需照明的节能照明技术。

秦岭天台山超长公路隧道群关键技术创新与实践

针对秦岭天台山超长公路隧道群在建设和运营过程中面临的工程建设难度大,运营管理复杂、安全风险高以及运营能耗高等诸多技术难题,围绕安全智慧运营、安全快速施工、单层衬砌支护、自然能源利用等方向,系统总结了秦岭山区特殊地质地形条件下32 km隧道群建设与运营过程中系列创新技术成果。主要结论:1)通过超长隧道群安全智慧运营技术创新与应用,破解山区超长连续公路隧道群运营安全保障难题;2)通过公路隧道安全快速施工技术创新与应用,突破超长大断面公路隧道机械化安全快速施工瓶颈;3)通过公路隧道单层衬砌支护技术创新与应用,构建硬质岩公路隧道单层衬砌支护体系;4)通过公路隧道利用自然能源技术推广应用,探索超长公路隧道绿色低碳运行。

运营地铁盾构隧道基底沉降的影响因素分析

隧道基底沉降不仅影响地铁列车的正常运行和乘客的舒适性,严重时会造成隧道结构病害,目前的研究主要集中在长期沉降预测和单因素影响分析,非常有必要开展隧道基底沉降的多影响因素分析。以郑州地铁1号线某盾构隧道区间为工程实例,构建三维有限元数值模型,采用人工激振力模拟地铁列车竖向荷载,提取数值模拟结果的动应力和静偏应力数据,引入循环荷载作用下累积塑性应变计算公式,采用分层总和法计算得到隧道基底沉降,研究了隧道周围土层性质、道床脱空深度和地铁列车运行速度等因素对隧道基底沉降的影响,研究结果表明:数值模拟的隧道基底沉降和发展规律与现场实测结果具有较高的吻合度,验证了有限元模拟的准确性;隧道基底沉降随着周围土层砂粒含量的提高而减小;道床脱空尺寸会加剧隧道和道床板中应力集中和振动放大现象,增大隧道下卧土层的动偏应力和基底沉降;提高车速会增大基底沉降的横向影响范围;采用正交试验法、极差分析法和方差分析法得到了隧道沉降对各因素的敏感性,从大到小依次为:土层性质、车速和道床脱空,其中,土层性质为高度显著水平、车速为显著水平。研究成果对运营地铁盾构隧道的变形监测、安全评估和加固处理具有一定的参考价值。

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。

高速公路隧道运营安全风险评估与防控探究

为探究高速公路隧道运营安全风险因素和防控策略,通过分析高速公路隧道运营事故特性,从隧道土建结构、车辆、道路状况、人、管理水平等5方面解析隧道运营安全影响因素及作用机理,提出行之有效的防控策略。对提高隧道运营的安全性、降低事故发生率和减轻事故严重性具有重要意义。

综合管廊安全运行隐患的第三方排查机制研究——以北京市为例

为提升综合管廊运营安全,建立有效的隐患排查和灾害防控机制,本文提出在管廊运维企业建立健全安全体系基础上,由相关政府部门组织,引入第三方安全服务机构,开展隐患排查抽检工作的机制;同时通过实践和专家经验,形成第三方排查工作实施路径和排查的主要工作内容。第三方排查的主要内容包括资料调研、实地调查和安全检测3部分。第三方排查机制不仅能够督促企业自律,倒逼企业自查,有效识别潜在风险,减少安全事故的发生,还能够推动综合管廊安全管理工作的持续改进和创新。